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Merge branch 'develop' into nightly.spacy.io

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Ines Montani 2020-08-24 13:45:31 +02:00
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3
.gitignore vendored
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@ -18,8 +18,7 @@ website/.npm
website/logs
*.log
npm-debug.log*
website/www/
website/_deploy.sh
quickstart-training-generator.js
# Cython / C extensions
cythonize.json

166
CONTRIBUTING.md
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@ -5,7 +5,7 @@
Thanks for your interest in contributing to spaCy 🎉 The project is maintained
by [@honnibal](https://github.com/honnibal) and [@ines](https://github.com/ines),
and we'll do our best to help you get started. This page will give you a quick
overview of how things are organised and most importantly, how to get involved.
overview of how things are organized and most importantly, how to get involved.
## Table of contents
@ -43,33 +43,33 @@ can also submit a [regression test](#fixing-bugs) straight away. When you're
opening an issue to report the bug, simply refer to your pull request in the
issue body. A few more tips:
- **Describing your issue:** Try to provide as many details as possible. What
exactly goes wrong? _How_ is it failing? Is there an error?
"XY doesn't work" usually isn't that helpful for tracking down problems. Always
remember to include the code you ran and if possible, extract only the relevant
parts and don't just dump your entire script. This will make it easier for us to
reproduce the error.
- **Describing your issue:** Try to provide as many details as possible. What
exactly goes wrong? _How_ is it failing? Is there an error?
"XY doesn't work" usually isn't that helpful for tracking down problems. Always
remember to include the code you ran and if possible, extract only the relevant
parts and don't just dump your entire script. This will make it easier for us to
reproduce the error.
- **Getting info about your spaCy installation and environment:** If you're
using spaCy v1.7+, you can use the command line interface to print details and
even format them as Markdown to copy-paste into GitHub issues:
`python -m spacy info --markdown`.
- **Getting info about your spaCy installation and environment:** If you're
using spaCy v1.7+, you can use the command line interface to print details and
even format them as Markdown to copy-paste into GitHub issues:
`python -m spacy info --markdown`.
- **Checking the model compatibility:** If you're having problems with a
[statistical model](https://spacy.io/models), it may be because the
model is incompatible with your spaCy installation. In spaCy v2.0+, you can check
this on the command line by running `python -m spacy validate`.
- **Checking the model compatibility:** If you're having problems with a
[statistical model](https://spacy.io/models), it may be because the
model is incompatible with your spaCy installation. In spaCy v2.0+, you can check
this on the command line by running `python -m spacy validate`.
- **Sharing a model's output, like dependencies and entities:** spaCy v2.0+
comes with [built-in visualizers](https://spacy.io/usage/visualizers) that
you can run from within your script or a Jupyter notebook. For some issues, it's
helpful to **include a screenshot** of the visualization. You can simply drag and
drop the image into GitHub's editor and it will be uploaded and included.
- **Sharing a model's output, like dependencies and entities:** spaCy v2.0+
comes with [built-in visualizers](https://spacy.io/usage/visualizers) that
you can run from within your script or a Jupyter notebook. For some issues, it's
helpful to **include a screenshot** of the visualization. You can simply drag and
drop the image into GitHub's editor and it will be uploaded and included.
- **Sharing long blocks of code or logs:** If you need to include long code,
logs or tracebacks, you can wrap them in `<details>` and `</details>`. This
[collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details)
so it only becomes visible on click, making the issue easier to read and follow.
- **Sharing long blocks of code or logs:** If you need to include long code,
logs or tracebacks, you can wrap them in `<details>` and `</details>`. This
[collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details)
so it only becomes visible on click, making the issue easier to read and follow.
### Issue labels
@ -94,39 +94,39 @@ shipped in the core library, and what could be provided in other packages. Our
philosophy is to prefer a smaller core library. We generally ask the following
questions:
- **What would this feature look like if implemented in a separate package?**
Some features would be very difficult to implement externally – for example,
changes to spaCy's built-in methods. In contrast, a library of word
alignment functions could easily live as a separate package that depended on
spaCy — there's little difference between writing `import word_aligner` and
`import spacy.word_aligner`. spaCy v2.0+ makes it easy to implement
[custom pipeline components](https://spacy.io/usage/processing-pipelines#custom-components),
and add your own attributes, properties and methods to the `Doc`, `Token` and
`Span`. If you're looking to implement a new spaCy feature, starting with a
custom component package is usually the best strategy. You won't have to worry
about spaCy's internals and you can test your module in an isolated
environment. And if it works well, we can always integrate it into the core
library later.
- **What would this feature look like if implemented in a separate package?**
Some features would be very difficult to implement externally – for example,
changes to spaCy's built-in methods. In contrast, a library of word
alignment functions could easily live as a separate package that depended on
spaCy — there's little difference between writing `import word_aligner` and
`import spacy.word_aligner`. spaCy v2.0+ makes it easy to implement
[custom pipeline components](https://spacy.io/usage/processing-pipelines#custom-components),
and add your own attributes, properties and methods to the `Doc`, `Token` and
`Span`. If you're looking to implement a new spaCy feature, starting with a
custom component package is usually the best strategy. You won't have to worry
about spaCy's internals and you can test your module in an isolated
environment. And if it works well, we can always integrate it into the core
library later.
- **Would the feature be easier to implement if it relied on "heavy" dependencies spaCy doesn't currently require?**
Python has a very rich ecosystem. Libraries like scikit-learn, SciPy, Gensim or
TensorFlow/Keras do lots of useful things — but we don't want to have them as
dependencies. If the feature requires functionality in one of these libraries,
it's probably better to break it out into a different package.
- **Would the feature be easier to implement if it relied on "heavy" dependencies spaCy doesn't currently require?**
Python has a very rich ecosystem. Libraries like scikit-learn, SciPy, Gensim or
TensorFlow/Keras do lots of useful things — but we don't want to have them as
dependencies. If the feature requires functionality in one of these libraries,
it's probably better to break it out into a different package.
- **Is the feature orthogonal to the current spaCy functionality, or overlapping?**
spaCy strongly prefers to avoid having 6 different ways of doing the same thing.
As better techniques are developed, we prefer to drop support for "the old way".
However, it's rare that one approach _entirely_ dominates another. It's very
common that there's still a use-case for the "obsolete" approach. For instance,
[WordNet](https://wordnet.princeton.edu/) is still very useful — but word
vectors are better for most use-cases, and the two approaches to lexical
semantics do a lot of the same things. spaCy therefore only supports word
vectors, and support for WordNet is currently left for other packages.
- **Is the feature orthogonal to the current spaCy functionality, or overlapping?**
spaCy strongly prefers to avoid having 6 different ways of doing the same thing.
As better techniques are developed, we prefer to drop support for "the old way".
However, it's rare that one approach _entirely_ dominates another. It's very
common that there's still a use-case for the "obsolete" approach. For instance,
[WordNet](https://wordnet.princeton.edu/) is still very useful — but word
vectors are better for most use-cases, and the two approaches to lexical
semantics do a lot of the same things. spaCy therefore only supports word
vectors, and support for WordNet is currently left for other packages.
- **Do you need the feature to get basic things done?** We do want spaCy to be
at least somewhat self-contained. If we keep needing some feature in our
recipes, that does provide some argument for bringing it "in house".
- **Do you need the feature to get basic things done?** We do want spaCy to be
at least somewhat self-contained. If we keep needing some feature in our
recipes, that does provide some argument for bringing it "in house".
### Getting started
@ -195,7 +195,7 @@ modules in `.py` files, not Cython modules in `.pyx` and `.pxd` files.**
### Code formatting
[`black`](https://github.com/ambv/black) is an opinionated Python code
formatter, optimised to produce readable code and small diffs. You can run
formatter, optimized to produce readable code and small diffs. You can run
`black` from the command-line, or via your code editor. For example, if you're
using [Visual Studio Code](https://code.visualstudio.com/), you can add the
following to your `settings.json` to use `black` for formatting and auto-format
@ -203,10 +203,10 @@ your files on save:
```json
{
"python.formatting.provider": "black",
"[python]": {
"editor.formatOnSave": true
}
"python.formatting.provider": "black",
"[python]": {
"editor.formatOnSave": true
}
}
```
@ -216,7 +216,7 @@ list of available editor integrations.
#### Disabling formatting
There are a few cases where auto-formatting doesn't improve readability – for
example, in some of the the language data files like the `tag_map.py`, or in
example, in some of the language data files like the `tag_map.py`, or in
the tests that construct `Doc` objects from lists of words and other labels.
Wrapping a block in `# fmt: off` and `# fmt: on` lets you disable formatting
for that particular code. Here's an example:
@ -286,7 +286,7 @@ Code that interacts with the file-system should accept objects that follow the
If the function is user-facing and takes a path as an argument, it should check
whether the path is provided as a string. Strings should be converted to
`pathlib.Path` objects. Serialization and deserialization functions should always
accept **file-like objects**, as it makes the library io-agnostic. Working on
accept **file-like objects**, as it makes the library IO-agnostic. Working on
buffers makes the code more general, easier to test, and compatible with Python
3's asynchronous IO.
@ -384,7 +384,7 @@ of Python and C++, with additional complexity and syntax from numpy. The
many "traps for new players". Working in Cython is very rewarding once you're
over the initial learning curve. As with C and C++, the first way you write
something in Cython will often be the performance-optimal approach. In contrast,
Python optimisation generally requires a lot of experimentation. Is it faster to
Python optimization generally requires a lot of experimentation. Is it faster to
have an `if item in my_dict` check, or to use `.get()`? What about `try`/`except`?
Does this numpy operation create a copy? There's no way to guess the answers to
these questions, and you'll usually be dissatisfied with your results — so
@ -397,10 +397,10 @@ Python. If it's not fast enough the first time, just switch to Cython.
### Resources to get you started
- [PEP 8 Style Guide for Python Code](https://www.python.org/dev/peps/pep-0008/) (python.org)
- [Official Cython documentation](http://docs.cython.org/en/latest/) (cython.org)
- [Writing C in Cython](https://explosion.ai/blog/writing-c-in-cython) (explosion.ai)
- [Multi-threading spaCy’s parser and named entity recogniser](https://explosion.ai/blog/multithreading-with-cython) (explosion.ai)
- [PEP 8 Style Guide for Python Code](https://www.python.org/dev/peps/pep-0008/) (python.org)
- [Official Cython documentation](http://docs.cython.org/en/latest/) (cython.org)
- [Writing C in Cython](https://explosion.ai/blog/writing-c-in-cython) (explosion.ai)
- [Multi-threading spaCy’s parser and named entity recognizer](https://explosion.ai/blog/multithreading-with-cython) (explosion.ai)
## Adding tests
@ -412,7 +412,7 @@ name. For example, tests for the `Tokenizer` can be found in
all test files and test functions need to be prefixed with `test_`.
When adding tests, make sure to use descriptive names, keep the code short and
concise and only test for one behaviour at a time. Try to `parametrize` test
concise and only test for one behavior at a time. Try to `parametrize` test
cases wherever possible, use our pre-defined fixtures for spaCy components and
avoid unnecessary imports.
@ -440,25 +440,25 @@ simply click on the "Suggest edits" button at the bottom of a page.
We're very excited about all the new possibilities for **community extensions**
and plugins in spaCy v2.0, and we can't wait to see what you build with it!
- An extension or plugin should add substantial functionality, be
**well-documented** and **open-source**. It should be available for users to download
and install as a Python package – for example via [PyPi](http://pypi.python.org).
- An extension or plugin should add substantial functionality, be
**well-documented** and **open-source**. It should be available for users to download
and install as a Python package – for example via [PyPi](http://pypi.python.org).
- Extensions that write to `Doc`, `Token` or `Span` attributes should be wrapped
as [pipeline components](https://spacy.io/usage/processing-pipelines#custom-components)
that users can **add to their processing pipeline** using `nlp.add_pipe()`.
- Extensions that write to `Doc`, `Token` or `Span` attributes should be wrapped
as [pipeline components](https://spacy.io/usage/processing-pipelines#custom-components)
that users can **add to their processing pipeline** using `nlp.add_pipe()`.
- When publishing your extension on GitHub, **tag it** with the topics
[`spacy`](https://github.com/topics/spacy?o=desc&s=stars) and
[`spacy-extensions`](https://github.com/topics/spacy-extension?o=desc&s=stars)
to make it easier to find. Those are also the topics we're linking to from the
spaCy website. If you're sharing your project on Twitter, feel free to tag
[@spacy_io](https://twitter.com/spacy_io) so we can check it out.
- When publishing your extension on GitHub, **tag it** with the topics
[`spacy`](https://github.com/topics/spacy?o=desc&s=stars) and
[`spacy-extensions`](https://github.com/topics/spacy-extension?o=desc&s=stars)
to make it easier to find. Those are also the topics we're linking to from the
spaCy website. If you're sharing your project on Twitter, feel free to tag
[@spacy_io](https://twitter.com/spacy_io) so we can check it out.
- Once your extension is published, you can open an issue on the
[issue tracker](https://github.com/explosion/spacy/issues) to suggest it for the
[resources directory](https://spacy.io/usage/resources#extensions) on the
website.
- Once your extension is published, you can open an issue on the
[issue tracker](https://github.com/explosion/spacy/issues) to suggest it for the
[resources directory](https://spacy.io/usage/resources#extensions) on the
website.
📖 **For more tips and best practices, see the [checklist for developing spaCy extensions](https://spacy.io/usage/processing-pipelines#extensions).**

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MANIFEST.in
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@ -5,5 +5,5 @@ include README.md
include pyproject.toml
recursive-exclude spacy/lang *.json
recursive-include spacy/lang *.json.gz
recursive-include spacy/cli *.json
recursive-include spacy/cli *.json *.yml
recursive-include licenses *

5
README.md
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@ -49,9 +49,8 @@ It's commercial open-source software, released under the MIT license.
## 💬 Where to ask questions
The spaCy project is maintained by [@honnibal](https://github.com/honnibal) and
[@ines](https://github.com/ines), along with core contributors
[@svlandeg](https://github.com/svlandeg) and
The spaCy project is maintained by [@honnibal](https://github.com/honnibal),
[@ines](https://github.com/ines), [@svlandeg](https://github.com/svlandeg) and
[@adrianeboyd](https://github.com/adrianeboyd). Please understand that we won't
be able to provide individual support via email. We also believe that help is
much more valuable if it's shared publicly, so that more people can benefit from

3
bin/ud/ud_train.py
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@ -15,7 +15,8 @@ import spacy.util
from bin.ud import conll17_ud_eval
from spacy.tokens import Token, Doc
from spacy.gold import Example
from spacy.util import compounding, minibatch, minibatch_by_words
from spacy.util import compounding, minibatch
from spacy.gold.batchers import minibatch_by_words
from spacy.pipeline._parser_internals.nonproj import projectivize
from spacy.matcher import Matcher
from spacy import displacy

10
examples/training/create_kb.py
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@ -48,8 +48,7 @@ def main(model, output_dir=None):
# You can change the dimension of vectors in your KB by using an encoder that changes the dimensionality.
# For simplicity, we'll just use the original vector dimension here instead.
vectors_dim = nlp.vocab.vectors.shape[1]
kb = KnowledgeBase(entity_vector_length=vectors_dim)
kb.initialize(nlp.vocab)
kb = KnowledgeBase(nlp.vocab, entity_vector_length=vectors_dim)
# set up the data
entity_ids = []
@ -81,7 +80,7 @@ def main(model, output_dir=None):
if not output_dir.exists():
output_dir.mkdir()
kb_path = str(output_dir / "kb")
kb.dump(kb_path)
kb.to_disk(kb_path)
print()
print("Saved KB to", kb_path)
@ -96,9 +95,8 @@ def main(model, output_dir=None):
print("Loading vocab from", vocab_path)
print("Loading KB from", kb_path)
vocab2 = Vocab().from_disk(vocab_path)
kb2 = KnowledgeBase(entity_vector_length=1)
kb.initialize(vocab2)
kb2.load_bulk(kb_path)
kb2 = KnowledgeBase(vocab2, entity_vector_length=1)
kb2.from_disk(kb_path)
print()
_print_kb(kb2)

2
examples/training/train_entity_linker.py
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@ -83,7 +83,7 @@ def main(kb_path, vocab_path, output_dir=None, n_iter=50):
if "entity_linker" not in nlp.pipe_names:
print("Loading Knowledge Base from '%s'" % kb_path)
cfg = {
"kb": {
"kb_loader": {
"@assets": "spacy.KBFromFile.v1",
"vocab_path": vocab_path,
"kb_path": kb_path,

4
netlify.toml
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@ -36,11 +36,11 @@ redirects = [
{from = "/docs/api/features", to = "/models/#architecture", force = true},
{from = "/docs/api/philosophy", to = "/usage/spacy-101", force = true},
{from = "/docs/usage/showcase", to = "/universe", force = true},
{from = "/tutorials/load-new-word-vectors", to = "/usage/vectors-similarity#custom", force = true},
{from = "/tutorials/load-new-word-vectors", to = "/usage/linguistic-features", force = true},
{from = "/tutorials", to = "/usage/examples", force = true},
# Old documentation pages (v2.x)
{from = "/usage/adding-languages", to = "/usage/linguistic-features", force = true},
{from = "/usage/vectors-similarity", to = "/usage/vectors-embeddings", force = true},
{from = "/usage/vectors-similarity", to = "/usage/linguistic-features#vectors-similarity", force = true},
{from = "/api/goldparse", to = "/api/top-level", force = true},
{from = "/api/goldcorpus", to = "/api/corpus", force = true},
{from = "/api/annotation", to = "/api/data-formats", force = true},

5
pyproject.toml
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@ -6,9 +6,10 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc>=8.0.0a27,<8.0.0a30",
"thinc>=8.0.0a29,<8.0.0a40",
"blis>=0.4.0,<0.5.0",
"pytokenizations",
"smart_open>=2.0.0,<3.0.0"
"smart_open>=2.0.0,<3.0.0",
"pathy"
]
build-backend = "setuptools.build_meta"

3
requirements.txt
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@ -1,7 +1,7 @@
# Our libraries
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.0.0a27,<8.0.0a30
thinc>=8.0.0a29,<8.0.0a40
blis>=0.4.0,<0.5.0
ml_datasets>=0.1.1
murmurhash>=0.28.0,<1.1.0
@ -9,6 +9,7 @@ wasabi>=0.7.1,<1.1.0
srsly>=2.1.0,<3.0.0
catalogue>=0.0.7,<1.1.0
typer>=0.3.0,<0.4.0
pathy
# Third party dependencies
numpy>=1.15.0
requests>=2.13.0,<3.0.0

5
setup.cfg
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@ -34,18 +34,19 @@ setup_requires =
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc>=8.0.0a27,<8.0.0a30
thinc>=8.0.0a29,<8.0.0a40
install_requires =
# Our libraries
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.0.0a27,<8.0.0a30
thinc>=8.0.0a29,<8.0.0a40
blis>=0.4.0,<0.5.0
wasabi>=0.7.1,<1.1.0
srsly>=2.1.0,<3.0.0
catalogue>=0.0.7,<1.1.0
typer>=0.3.0,<0.4.0
pathy
# Third-party dependencies
tqdm>=4.38.0,<5.0.0
numpy>=1.15.0

2
spacy/about.py
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@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy-nightly"
__version__ = "3.0.0a7"
__version__ = "3.0.0a10"
__release__ = True
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

2
spacy/cli/__init__.py
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@ -21,6 +21,8 @@ from .project.clone import project_clone # noqa: F401
from .project.assets import project_assets # noqa: F401
from .project.run import project_run # noqa: F401
from .project.dvc import project_update_dvc # noqa: F401
from .project.push import project_push # noqa: F401
from .project.pull import project_pull # noqa: F401
@app.command("link", no_args_is_help=True, deprecated=True, hidden=True)

69
spacy/cli/_util.py
View File

@ -1,4 +1,5 @@
from typing import Dict, Any, Union, List, Optional
from typing import Dict, Any, Union, List, Optional, TYPE_CHECKING
import sys
from pathlib import Path
from wasabi import msg
import srsly
@ -8,11 +9,13 @@ from typer.main import get_command
from contextlib import contextmanager
from thinc.config import Config, ConfigValidationError
from configparser import InterpolationError
import sys
from ..schemas import ProjectConfigSchema, validate
from ..util import import_file
if TYPE_CHECKING:
from pathy import Pathy # noqa: F401
PROJECT_FILE = "project.yml"
PROJECT_LOCK = "project.lock"
@ -68,11 +71,12 @@ def parse_config_overrides(args: List[str]) -> Dict[str, Any]:
opt = args.pop(0)
err = f"Invalid CLI argument '{opt}'"
if opt.startswith("--"): # new argument
opt = opt.replace("--", "").replace("-", "_")
opt = opt.replace("--", "")
if "." not in opt:
msg.fail(f"{err}: can't override top-level section", exits=1)
if "=" in opt: # we have --opt=value
opt, value = opt.split("=", 1)
opt = opt.replace("-", "_")
else:
if not args or args[0].startswith("--"): # flag with no value
value = "true"
@ -92,11 +96,12 @@ def parse_config_overrides(args: List[str]) -> Dict[str, Any]:
return result
def load_project_config(path: Path) -> Dict[str, Any]:
def load_project_config(path: Path, interpolate: bool = True) -> Dict[str, Any]:
"""Load the project.yml file from a directory and validate it. Also make
sure that all directories defined in the config exist.
path (Path): The path to the project directory.
interpolate (bool): Whether to substitute project variables.
RETURNS (Dict[str, Any]): The loaded project.yml.
"""
config_path = path / PROJECT_FILE
@ -109,16 +114,34 @@ def load_project_config(path: Path) -> Dict[str, Any]:
msg.fail(invalid_err, e, exits=1)
errors = validate(ProjectConfigSchema, config)
if errors:
msg.fail(invalid_err, "\n".join(errors), exits=1)
msg.fail(invalid_err)
print("\n".join(errors))
sys.exit(1)
validate_project_commands(config)
# Make sure directories defined in config exist
for subdir in config.get("directories", []):
dir_path = path / subdir
if not dir_path.exists():
dir_path.mkdir(parents=True)
if interpolate:
err = "project.yml validation error"
with show_validation_error(title=err, hint_fill=False):
config = substitute_project_variables(config)
return config
def substitute_project_variables(config: Dict[str, Any], overrides: Dict = {}):
key = "vars"
config.setdefault(key, {})
config[key].update(overrides)
# Need to put variables in the top scope again so we can have a top-level
# section "project" (otherwise, a list of commands in the top scope wouldn't)
# be allowed by Thinc's config system
cfg = Config({"project": config, key: config[key]})
interpolated = cfg.interpolate()
return dict(interpolated["project"])
def validate_project_commands(config: Dict[str, Any]) -> None:
"""Check that project commands and workflows are valid, don't contain
duplicates, don't clash and only refer to commands that exist.
@ -229,3 +252,39 @@ def get_sourced_components(config: Union[Dict[str, Any], Config]) -> List[str]:
for name, cfg in config.get("components", {}).items()
if "factory" not in cfg and "source" in cfg
]
def upload_file(src: Path, dest: Union[str, "Pathy"]) -> None:
"""Upload a file.
src (Path): The source path.
url (str): The destination URL to upload to.
"""
dest = ensure_pathy(dest)
with dest.open(mode="wb") as output_file:
with src.open(mode="rb") as input_file:
output_file.write(input_file.read())
def download_file(src: Union[str, "Pathy"], dest: Path, *, force: bool = False) -> None:
"""Download a file using smart_open.
url (str): The URL of the file.
dest (Path): The destination path.
force (bool): Whether to force download even if file exists.
If False, the download will be skipped.
"""
if dest.exists() and not force:
return None
src = ensure_pathy(src)
with src.open(mode="rb") as input_file:
with dest.open(mode="wb") as output_file:
output_file.write(input_file.read())
def ensure_pathy(path):
"""Temporary helper to prevent importing Pathy globally (which can cause
slow and annoying Google Cloud warning)."""
from pathy import Pathy # noqa: F811
return Pathy(path)

18
spacy/cli/debug_data.py
View File

@ -3,7 +3,7 @@ from pathlib import Path
from collections import Counter
import sys
import srsly
from wasabi import Printer, MESSAGES, msg, diff_strings
from wasabi import Printer, MESSAGES, msg
import typer
from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
@ -32,8 +32,6 @@ def debug_config_cli(
ctx: typer.Context, # This is only used to read additional arguments
config_path: Path = Arg(..., help="Path to config file", exists=True),
code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
auto_fill: bool = Opt(False, "--auto-fill", "-F", help="Whether or not to auto-fill the config with built-in defaults if possible"),
diff: bool = Opt(False, "--diff", "-D", help="Show a visual diff if config was auto-filled")
# fmt: on
):
"""Debug a config.cfg file and show validation errors. The command will
@ -49,18 +47,8 @@ def debug_config_cli(
import_code(code_path)
with show_validation_error(config_path):
config = util.load_config(config_path, overrides=overrides)
nlp, _ = util.load_model_from_config(config, auto_fill=auto_fill)
if auto_fill:
orig_config = config.to_str()
filled_config = nlp.config.to_str()
if orig_config == filled_config:
msg.good("Original config is valid, no values were auto-filled")
else:
msg.good("Auto-filled config is valid")
if diff:
print(diff_strings(config.to_str(), nlp.config.to_str()))
else:
msg.good("Original config is valid")
nlp, _ = util.load_model_from_config(config)
msg.good("Original config is valid")
@debug_cli.command(

2
spacy/cli/evaluate.py
View File

@ -70,7 +70,7 @@ def evaluate(
corpus = Corpus(data_path, gold_preproc=gold_preproc)
nlp = util.load_model(model)
dev_dataset = list(corpus(nlp))
scores = nlp.evaluate(dev_dataset, verbose=False)
scores = nlp.evaluate(dev_dataset)
metrics = {
"TOK": "token_acc",
"TAG": "tag_acc",

55
spacy/cli/init_config.py
View File

@ -3,17 +3,17 @@ from enum import Enum
from pathlib import Path
from wasabi import Printer, diff_strings
from thinc.api import Config
from pydantic import BaseModel
import srsly
import re
from .. import util
from ..schemas import RecommendationSchema
from ._util import init_cli, Arg, Opt, show_validation_error, COMMAND
TEMPLATE_ROOT = Path(__file__).parent / "templates"
TEMPLATE_PATH = TEMPLATE_ROOT / "quickstart_training.jinja"
RECOMMENDATIONS_PATH = TEMPLATE_ROOT / "quickstart_training_recommendations.json"
ROOT = Path(__file__).parent / "templates"
TEMPLATE_PATH = ROOT / "quickstart_training.jinja"
RECOMMENDATIONS = srsly.read_yaml(ROOT / "quickstart_training_recommendations.yml")
class Optimizations(str, Enum):
@ -21,25 +21,10 @@ class Optimizations(str, Enum):
accuracy = "accuracy"
class RecommendationsTrfItem(BaseModel):
name: str
size_factor: int
class RecommendationsTrf(BaseModel):
efficiency: RecommendationsTrfItem
accuracy: RecommendationsTrfItem
class RecommendationSchema(BaseModel):
word_vectors: Optional[str] = None
transformer: Optional[RecommendationsTrf] = None
@init_cli.command("config")
def init_config_cli(
# fmt: off
output_file: Path = Arg("-", help="File to save config.cfg to (or - for stdout)", allow_dash=True),
output_file: Path = Arg(..., help="File to save config.cfg to or - for stdout (will only output config and no additional logging info)", allow_dash=True),
lang: Optional[str] = Opt("en", "--lang", "-l", help="Two-letter code of the language to use"),
pipeline: Optional[str] = Opt("tagger,parser,ner", "--pipeline", "-p", help="Comma-separated names of trainable pipeline components to include in the model (without 'tok2vec' or 'transformer')"),
optimize: Optimizations = Opt(Optimizations.efficiency.value, "--optimize", "-o", help="Whether to optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). This will impact the choice of architecture, pretrained weights and related hyperparameters."),
@ -111,14 +96,11 @@ def init_config(
from jinja2 import Template
except ImportError:
msg.fail("This command requires jinja2", "pip install jinja2", exits=1)
recommendations = srsly.read_json(RECOMMENDATIONS_PATH)
lang_defaults = util.get_lang_class(lang).Defaults
has_letters = lang_defaults.writing_system.get("has_letters", True)
# Filter out duplicates since tok2vec and transformer are added by template
pipeline = [pipe for pipe in pipeline if pipe not in ("tok2vec", "transformer")]
reco = RecommendationSchema(**recommendations.get(lang, {})).dict()
with TEMPLATE_PATH.open("r") as f:
template = Template(f.read())
# Filter out duplicates since tok2vec and transformer are added by template
pipeline = [pipe for pipe in pipeline if pipe not in ("tok2vec", "transformer")]
reco = RecommendationSchema(**RECOMMENDATIONS.get(lang, {})).dict()
variables = {
"lang": lang,
"components": pipeline,
@ -126,8 +108,15 @@ def init_config(
"hardware": "cpu" if cpu else "gpu",
"transformer_data": reco["transformer"],
"word_vectors": reco["word_vectors"],
"has_letters": has_letters,
"has_letters": reco["has_letters"],
}
if variables["transformer_data"] and not has_spacy_transformers():
msg.warn(
"To generate a more effective transformer-based config (GPU-only), "
"install the spacy-transformers package and re-run this command. "
"The config generated now does not use transformers."
)
variables["transformer_data"] = None
base_template = template.render(variables).strip()
# Giving up on getting the newlines right in jinja for now
base_template = re.sub(r"\n\n\n+", "\n\n", base_template)
@ -144,8 +133,6 @@ def init_config(
for label, value in use_case.items():
msg.text(f"- {label}: {value}")
use_transformer = bool(template_vars.use_transformer)
if use_transformer:
require_spacy_transformers(msg)
with show_validation_error(hint_fill=False):
config = util.load_config_from_str(base_template)
nlp, _ = util.load_model_from_config(config, auto_fill=True)
@ -167,12 +154,10 @@ def save_config(config: Config, output_file: Path, is_stdout: bool = False) -> N
print(f"{COMMAND} train {output_file.parts[-1]} {' '.join(variables)}")
def require_spacy_transformers(msg: Printer) -> None:
def has_spacy_transformers() -> bool:
try:
import spacy_transformers # noqa: F401
return True
except ImportError:
msg.fail(
"Using a transformer-based pipeline requires spacy-transformers "
"to be installed.",
exits=1,
)
return False

1
spacy/cli/package.py
View File

@ -229,6 +229,7 @@ if __name__ == '__main__':
TEMPLATE_MANIFEST = """
include meta.json
include config.cfg
""".strip()

18
spacy/cli/project/assets.py
View File

@ -4,10 +4,10 @@ from wasabi import msg
import re
import shutil
import requests
import smart_open
from ...util import ensure_path, working_dir
from .._util import project_cli, Arg, PROJECT_FILE, load_project_config, get_checksum
from .._util import download_file
# TODO: find a solution for caches
@ -44,16 +44,14 @@ def project_assets(project_dir: Path) -> None:
if not assets:
msg.warn(f"No assets specified in {PROJECT_FILE}", exits=0)
msg.info(f"Fetching {len(assets)} asset(s)")
variables = config.get("variables", {})
for asset in assets:
dest = asset["dest"].format(**variables)
dest = asset["dest"]
url = asset.get("url")
checksum = asset.get("checksum")
if not url:
# project.yml defines asset without URL that the user has to place
check_private_asset(dest, checksum)
continue
url = url.format(**variables)
fetch_asset(project_path, url, dest, checksum)
@ -132,15 +130,3 @@ def convert_asset_url(url: str) -> str:
)
return converted
return url
def download_file(url: str, dest: Path, chunk_size: int = 1024) -> None:
"""Download a file using smart_open.
url (str): The URL of the file.
dest (Path): The destination path.
chunk_size (int): The size of chunks to read/write.
"""
with smart_open.open(url, mode="rb") as input_file:
with dest.open(mode="wb") as output_file:
output_file.write(input_file.read())

12
spacy/cli/project/dvc.py
View File

@ -99,7 +99,6 @@ def update_dvc_config(
if ref_hash == config_hash and not force:
return False # Nothing has changed in project.yml, don't need to update
dvc_config_path.unlink()
variables = config.get("variables", {})
dvc_commands = []
config_commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
for name in workflows[workflow]:
@ -122,7 +121,7 @@ def update_dvc_config(
dvc_commands.append(join_command(full_cmd))
with working_dir(path):
dvc_flags = {"--verbose": verbose, "--quiet": silent}
run_dvc_commands(dvc_commands, variables, flags=dvc_flags)
run_dvc_commands(dvc_commands, flags=dvc_flags)
with dvc_config_path.open("r+", encoding="utf8") as f:
content = f.read()
f.seek(0, 0)
@ -131,23 +130,16 @@ def update_dvc_config(
def run_dvc_commands(
commands: List[str] = tuple(),
variables: Dict[str, str] = {},
flags: Dict[str, bool] = {},
commands: List[str] = tuple(), flags: Dict[str, bool] = {},
) -> None:
"""Run a sequence of DVC commands in a subprocess, in order.
commands (List[str]): The string commands without the leading "dvc".
variables (Dict[str, str]): Dictionary of variable names, mapped to their
values. Will be used to substitute format string variables in the
commands.
flags (Dict[str, bool]): Conditional flags to be added to command. Makes it
easier to pass flags like --quiet that depend on a variable or
command-line setting while avoiding lots of nested conditionals.
"""
for command in commands:
# Substitute variables, e.g. "./{NAME}.json"
command = command.format(**variables)
command = split_command(command)
dvc_command = ["dvc", *command]
# Add the flags if they are set to True

38
spacy/cli/project/pull.py Normal file
View File

@ -0,0 +1,38 @@
from pathlib import Path
from wasabi import msg
from .remote_storage import RemoteStorage
from .remote_storage import get_command_hash
from .._util import project_cli, Arg
from .._util import load_project_config
@project_cli.command("pull")
def project_pull_cli(
# fmt: off
remote: str = Arg("default", help="Name or path of remote storage"),
project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
# fmt: on
):
"""Retrieve any precomputed outputs from a remote storage that are available.
You can alias remotes in your project.yml by mapping them to storage paths.
A storage can be anything that the smart-open library can upload to, e.g.
gcs, aws, ssh, local directories etc
"""
for url, output_path in project_pull(project_dir, remote):
if url is not None:
msg.good(f"Pulled {output_path} from {url}")
def project_pull(project_dir: Path, remote: str, *, verbose: bool = False):
config = load_project_config(project_dir)
if remote in config.get("remotes", {}):
remote = config["remotes"][remote]
storage = RemoteStorage(project_dir, remote)
for cmd in config.get("commands", []):
deps = [project_dir / dep for dep in cmd.get("deps", [])]
if any(not dep.exists() for dep in deps):
continue
cmd_hash = get_command_hash("", "", deps, cmd["script"])
for output_path in cmd.get("outputs", []):
url = storage.pull(output_path, command_hash=cmd_hash)
yield url, output_path

51
spacy/cli/project/push.py Normal file
View File

@ -0,0 +1,51 @@
from pathlib import Path
from wasabi import msg
from .remote_storage import RemoteStorage
from .remote_storage import get_content_hash, get_command_hash
from .._util import load_project_config
from .._util import project_cli, Arg
@project_cli.command("push")
def project_push_cli(
# fmt: off
remote: str = Arg("default", help="Name or path of remote storage"),
project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
# fmt: on
):
"""Persist outputs to a remote storage. You can alias remotes in your project.yml
by mapping them to storage paths. A storage can be anything that the smart-open
library can upload to, e.g. gcs, aws, ssh, local directories etc
"""
for output_path, url in project_push(project_dir, remote):
if url is None:
msg.info(f"Skipping {output_path}")
else:
msg.good(f"Pushed {output_path} to {url}")
def project_push(project_dir: Path, remote: str):
"""Persist outputs to a remote storage. You can alias remotes in your project.yml
by mapping them to storage paths. A storage can be anything that the smart-open
library can upload to, e.g. gcs, aws, ssh, local directories etc
"""
config = load_project_config(project_dir)
if remote in config.get("remotes", {}):
remote = config["remotes"][remote]
storage = RemoteStorage(project_dir, remote)
for cmd in config.get("commands", []):
deps = [project_dir / dep for dep in cmd.get("deps", [])]
if any(not dep.exists() for dep in deps):
continue
cmd_hash = get_command_hash(
"", "", [project_dir / dep for dep in cmd.get("deps", [])], cmd["script"]
)
for output_path in cmd.get("outputs", []):
output_loc = project_dir / output_path
if output_loc.exists():
url = storage.push(
output_path,
command_hash=cmd_hash,
content_hash=get_content_hash(output_loc),
)
yield output_path, url

169
spacy/cli/project/remote_storage.py Normal file
View File

@ -0,0 +1,169 @@
from typing import Optional, List, Dict, TYPE_CHECKING
import os
import site
import hashlib
import urllib.parse
import tarfile
from pathlib import Path
from .._util import get_hash, get_checksum, download_file, ensure_pathy
from ...util import make_tempdir
if TYPE_CHECKING:
from pathy import Pathy # noqa: F401
class RemoteStorage:
"""Push and pull outputs to and from a remote file storage.
Remotes can be anything that `smart-open` can support: AWS, GCS, file system,
ssh, etc.
"""
def __init__(self, project_root: Path, url: str, *, compression="gz"):
self.root = project_root
self.url = ensure_pathy(url)
self.compression = compression
def push(self, path: Path, command_hash: str, content_hash: str) -> "Pathy":
"""Compress a file or directory within a project and upload it to a remote
storage. If an object exists at the full URL, nothing is done.
Within the remote storage, files are addressed by their project path
(url encoded) and two user-supplied hashes, representing their creation
context and their file contents. If the URL already exists, the data is
not uploaded. Paths are archived and compressed prior to upload.
"""
loc = self.root / path
if not loc.exists():
raise IOError(f"Cannot push {loc}: does not exist.")
url = self.make_url(path, command_hash, content_hash)
if url.exists():
return None
tmp: Path
with make_tempdir() as tmp:
tar_loc = tmp / self.encode_name(str(path))
mode_string = f"w:{self.compression}" if self.compression else "w"
with tarfile.open(tar_loc, mode=mode_string) as tar_file:
tar_file.add(str(loc), arcname=str(path))
with tar_loc.open(mode="rb") as input_file:
with url.open(mode="wb") as output_file:
output_file.write(input_file.read())
return url
def pull(
self,
path: Path,
*,
command_hash: Optional[str] = None,
content_hash: Optional[str] = None,
) -> Optional["Pathy"]:
"""Retrieve a file from the remote cache. If the file already exists,
nothing is done.
If the command_hash and/or content_hash are specified, only matching
results are returned. If no results are available, an error is raised.
"""
dest = self.root / path
if dest.exists():
return None
url = self.find(path, command_hash=command_hash, content_hash=content_hash)
if url is None:
return url
else:
# Make sure the destination exists
if not dest.parent.exists():
dest.parent.mkdir(parents=True)
tmp: Path
with make_tempdir() as tmp:
tar_loc = tmp / url.parts[-1]
download_file(url, tar_loc)
mode_string = f"r:{self.compression}" if self.compression else "r"
with tarfile.open(tar_loc, mode=mode_string) as tar_file:
# This requires that the path is added correctly, relative
# to root. This is how we set things up in push()
tar_file.extractall(self.root)
return url
def find(
self,
path: Path,
*,
command_hash: Optional[str] = None,
content_hash: Optional[str] = None,
) -> Optional["Pathy"]:
"""Find the best matching version of a file within the storage,
or `None` if no match can be found. If both the creation and content hash
are specified, only exact matches will be returned. Otherwise, the most
recent matching file is preferred.
"""
name = self.encode_name(str(path))
if command_hash is not None and content_hash is not None:
url = self.make_url(path, command_hash, content_hash)
urls = [url] if url.exists() else []
elif command_hash is not None:
urls = list((self.url / name / command_hash).iterdir())
else:
urls = list((self.url / name).iterdir())
if content_hash is not None:
urls = [url for url in urls if url.parts[-1] == content_hash]
return urls[-1] if urls else None
def make_url(self, path: Path, command_hash: str, content_hash: str) -> "Pathy":
"""Construct a URL from a subpath, a creation hash and a content hash."""
return self.url / self.encode_name(str(path)) / command_hash / content_hash
def encode_name(self, name: str) -> str:
"""Encode a subpath into a URL-safe name."""
return urllib.parse.quote_plus(name)
def get_content_hash(loc: Path) -> str:
return get_checksum(loc)
def get_command_hash(
site_hash: str, env_hash: str, deps: List[Path], cmd: List[str]
) -> str:
"""Create a hash representing the execution of a command. This includes the
currently installed packages, whatever environment variables have been marked
as relevant, and the command.
"""
hashes = [site_hash, env_hash] + [get_checksum(dep) for dep in sorted(deps)]
hashes.extend(cmd)
creation_bytes = "".join(hashes).encode("utf8")
return hashlib.md5(creation_bytes).hexdigest()
def get_site_hash():
"""Hash the current Python environment's site-packages contents, including
the name and version of the libraries. The list we're hashing is what
`pip freeze` would output.
"""
site_dirs = site.getsitepackages()
if site.ENABLE_USER_SITE:
site_dirs.extend(site.getusersitepackages())
packages = set()
for site_dir in site_dirs:
site_dir = Path(site_dir)
for subpath in site_dir.iterdir():
if subpath.parts[-1].endswith("dist-info"):
packages.add(subpath.parts[-1].replace(".dist-info", ""))
package_bytes = "".join(sorted(packages)).encode("utf8")
return hashlib.md5sum(package_bytes).hexdigest()
def get_env_hash(env: Dict[str, str]) -> str:
"""Construct a hash of the environment variables that will be passed into
the commands.
Values in the env dict may be references to the current os.environ, using
the syntax $ENV_VAR to mean os.environ[ENV_VAR]
"""
env_vars = {}
for key, value in env.items():
if value.startswith("$"):
env_vars[key] = os.environ.get(value[1:], "")
else:
env_vars[key] = value
return get_hash(env_vars)

50
spacy/cli/project/run.py
View File

@ -44,7 +44,6 @@ def project_run(
dry (bool): Perform a dry run and don't execute commands.
"""
config = load_project_config(project_dir)
variables = config.get("variables", {})
commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
workflows = config.get("workflows", {})
validate_subcommand(commands.keys(), workflows.keys(), subcommand)
@ -54,22 +53,20 @@ def project_run(
project_run(project_dir, cmd, force=force, dry=dry)
else:
cmd = commands[subcommand]
variables = config.get("variables", {})
for dep in cmd.get("deps", []):
dep = dep.format(**variables)
if not (project_dir / dep).exists():
err = f"Missing dependency specified by command '{subcommand}': {dep}"
err_kwargs = {"exits": 1} if not dry else {}
msg.fail(err, **err_kwargs)
with working_dir(project_dir) as current_dir:
rerun = check_rerun(current_dir, cmd, variables)
rerun = check_rerun(current_dir, cmd)
if not rerun and not force:
msg.info(f"Skipping '{cmd['name']}': nothing changed")
else:
msg.divider(subcommand)
run_commands(cmd["script"], variables, dry=dry)
run_commands(cmd["script"], dry=dry)
if not dry:
update_lockfile(current_dir, cmd, variables)
update_lockfile(current_dir, cmd)
def print_run_help(project_dir: Path, subcommand: Optional[str] = None) -> None:
@ -115,23 +112,15 @@ def print_run_help(project_dir: Path, subcommand: Optional[str] = None) -> None:
def run_commands(
commands: List[str] = tuple(),
variables: Dict[str, Any] = {},
silent: bool = False,
dry: bool = False,
commands: List[str] = tuple(), silent: bool = False, dry: bool = False,
) -> None:
"""Run a sequence of commands in a subprocess, in order.
commands (List[str]): The string commands.
variables (Dict[str, Any]): Dictionary of variable names, mapped to their
values. Will be used to substitute format string variables in the
commands.
silent (bool): Don't print the commands.
dry (bool): Perform a dry run and don't execut anything.
"""
for command in commands:
# Substitute variables, e.g. "./{NAME}.json"
command = command.format(**variables)
command = split_command(command)
# Not sure if this is needed or a good idea. Motivation: users may often
# use commands in their config that reference "python" and we want to
@ -173,15 +162,12 @@ def validate_subcommand(
)
def check_rerun(
project_dir: Path, command: Dict[str, Any], variables: Dict[str, Any]
) -> bool:
def check_rerun(project_dir: Path, command: Dict[str, Any]) -> bool:
"""Check if a command should be rerun because its settings or inputs/outputs
changed.
project_dir (Path): The current project directory.
command (Dict[str, Any]): The command, as defined in the project.yml.
variables (Dict[str, Any]): The variables defined in the project.yml.
RETURNS (bool): Whether to re-run the command.
"""
lock_path = project_dir / PROJECT_LOCK
@ -197,19 +183,16 @@ def check_rerun(
# If the entry in the lockfile matches the lockfile entry that would be
# generated from the current command, we don't rerun because it means that
# all inputs/outputs, hashes and scripts are the same and nothing changed
return get_hash(get_lock_entry(project_dir, command, variables)) != get_hash(entry)
return get_hash(get_lock_entry(project_dir, command)) != get_hash(entry)
def update_lockfile(
project_dir: Path, command: Dict[str, Any], variables: Dict[str, Any]
) -> None:
def update_lockfile(project_dir: Path, command: Dict[str, Any]) -> None:
"""Update the lockfile after running a command. Will create a lockfile if
it doesn't yet exist and will add an entry for the current command, its
script and dependencies/outputs.
project_dir (Path): The current project directory.
command (Dict[str, Any]): The command, as defined in the project.yml.
variables (Dict[str, Any]): The variables defined in the project.yml.
"""
lock_path = project_dir / PROJECT_LOCK
if not lock_path.exists():
@ -217,13 +200,11 @@ def update_lockfile(
data = {}
else:
data = srsly.read_yaml(lock_path)
data[command["name"]] = get_lock_entry(project_dir, command, variables)
data[command["name"]] = get_lock_entry(project_dir, command)
srsly.write_yaml(lock_path, data)
def get_lock_entry(
project_dir: Path, command: Dict[str, Any], variables: Dict[str, Any]
) -> Dict[str, Any]:
def get_lock_entry(project_dir: Path, command: Dict[str, Any]) -> Dict[str, Any]:
"""Get a lockfile entry for a given command. An entry includes the command,
the script (command steps) and a list of dependencies and outputs with
their paths and file hashes, if available. The format is based on the
@ -231,12 +212,11 @@ def get_lock_entry(
project_dir (Path): The current project directory.
command (Dict[str, Any]): The command, as defined in the project.yml.
variables (Dict[str, Any]): The variables defined in the project.yml.
RETURNS (Dict[str, Any]): The lockfile entry.
"""
deps = get_fileinfo(project_dir, command.get("deps", []), variables)
outs = get_fileinfo(project_dir, command.get("outputs", []), variables)
outs_nc = get_fileinfo(project_dir, command.get("outputs_no_cache", []), variables)
deps = get_fileinfo(project_dir, command.get("deps", []))
outs = get_fileinfo(project_dir, command.get("outputs", []))
outs_nc = get_fileinfo(project_dir, command.get("outputs_no_cache", []))
return {
"cmd": f"{COMMAND} run {command['name']}",
"script": command["script"],
@ -245,20 +225,16 @@ def get_lock_entry(
}
def get_fileinfo(
project_dir: Path, paths: List[str], variables: Dict[str, Any]
) -> List[Dict[str, str]]:
def get_fileinfo(project_dir: Path, paths: List[str]) -> List[Dict[str, str]]:
"""Generate the file information for a list of paths (dependencies, outputs).
Includes the file path and the file's checksum.
project_dir (Path): The current project directory.
paths (List[str]): The file paths.
variables (Dict[str, Any]): The variables defined in the project.yml.
RETURNS (List[Dict[str, str]]): The lockfile entry for a file.
"""
data = []
for path in paths:
path = path.format(**variables)
file_path = project_dir / path
md5 = get_checksum(file_path) if file_path.exists() else None
data.append({"path": path, "md5": md5})

18
spacy/cli/templates/quickstart_training.jinja
View File

@ -105,10 +105,10 @@ factory = "tok2vec"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = ${components.tok2vec.model.encode:width}
width = ${components.tok2vec.model.encode.width}
rows = {{ 2000 if optimize == "efficiency" else 7000 }}
also_embed_subwords = {{ true if has_letters else false }}
also_use_static_vectors = {{ true if optimize == "accuracy" else false }}
also_embed_subwords = {{ "true" if has_letters else "false" }}
also_use_static_vectors = {{ "true" if optimize == "accuracy" else "false" }}
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
@ -127,7 +127,7 @@ nO = null
[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}
width = ${components.tok2vec.model.encode.width}
{%- endif %}
{% if "parser" in components -%}
@ -144,7 +144,7 @@ nO = null
[components.parser.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}
width = ${components.tok2vec.model.encode.width}
{%- endif %}
{% if "ner" in components %}
@ -161,7 +161,7 @@ nO = null
[components.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode:width}
width = ${components.tok2vec.model.encode.width}
{% endif %}
{% endif %}
@ -194,12 +194,12 @@ initial_rate = 5e-5
[training.train_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:train}
max_length = {{ 500 if hardware == "gpu" else 0 }}
path = ${paths.train}
max_length = {{ 500 if hardware == "gpu" else 2000 }}
[training.dev_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:dev}
path = ${paths.dev}
max_length = 0
{% if use_transformer %}

13
spacy/cli/templates/quickstart_training_recommendations.json
View File

@ -1,13 +0,0 @@
{
"en": {
"word_vectors": "en_vectors_web_lg",
"transformer": {
"efficiency": { "name": "roberta-base", "size_factor": 3 },
"accuracy": { "name": "roberta-base", "size_factor": 3 }
}
},
"de": {
"word_vectors": null,
"transformer": null
}
}

103
spacy/cli/templates/quickstart_training_recommendations.yml Normal file
View File

@ -0,0 +1,103 @@
# Recommended settings and available resources for each language, if available.
# Not all languages have recommended word vectors or transformers and for some,
# the recommended transformer for efficiency and accuracy may be the same.
en:
word_vectors: en_vectors_web_lg
transformer:
efficiency:
name: roberta-base
size_factor: 3
accuracy:
name: roberta-base
size_factor: 3
de:
word_vectors: null
transformer:
efficiency:
name: bert-base-german-cased
size_factor: 3
accuracy:
name: bert-base-german-cased
size_factor: 3
fr:
word_vectors: null
transformer:
efficiency:
name: camembert-base
size_factor: 3
accuracy:
name: camembert-base
size_factor: 3
es:
word_vectors: null
transformer:
efficiency:
name: mrm8488/RuPERTa-base
size_factor: 3
accuracy:
name: mrm8488/RuPERTa-base
size_factor: 3
sv:
word_vectors: null
transformer:
efficiency:
name: KB/bert-base-swedish-cased
size_factor: 3
accuracy:
name: KB/bert-base-swedish-cased
size_factor: 3
fi:
word_vectors: null
transformer:
efficiency:
name: TurkuNLP/bert-base-finnish-cased-v1
size_factor: 3
accuracy:
name: TurkuNLP/bert-base-finnish-cased-v1
size_factor: 3
el:
word_vectors: null
transformer:
efficiency:
name: nlpaueb/bert-base-greek-uncased-v1
size_factor: 3
accuracy:
name: nlpaueb/bert-base-greek-uncased-v1
size_factor: 3
tr:
word_vectors: null
transformer:
efficiency:
name: dbmdz/bert-base-turkish-cased
size_factor: 3
accuracy:
name: dbmdz/bert-base-turkish-cased
size_factor: 3
zh:
word_vectors: null
transformer:
efficiency:
name: bert-base-chinese
size_factor: 3
accuracy:
name: bert-base-chinese
size_factor: 3
has_letters: false
ar:
word_vectors: null
transformer:
efficiency:
name: asafaya/bert-base-arabic
size_factor: 3
accuracy:
name: asafaya/bert-base-arabic
size_factor: 3
pl:
word_vectors: null
transformer:
efficiency:
name: dkleczek/bert-base-polish-cased-v1
size_factor: 3
accuracy:
name: dkleczek/bert-base-polish-cased-v1
size_factor: 3

24
spacy/cli/train.py
View File

@ -75,7 +75,9 @@ def train(
msg.info("Using CPU")
msg.info(f"Loading config and nlp from: {config_path}")
with show_validation_error(config_path):
config = util.load_config(config_path, overrides=config_overrides)
config = util.load_config(
config_path, overrides=config_overrides, interpolate=True
)
if config.get("training", {}).get("seed") is not None:
fix_random_seed(config["training"]["seed"])
# Use original config here before it's resolved to functions
@ -162,13 +164,14 @@ def train(
progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
except Exception as e:
if output_path is not None:
# We don't want to swallow the traceback if we don't have a
# specific error.
msg.warn(
f"Aborting and saving the final best model. "
f"Encountered exception: {str(e)}",
exits=1,
f"Encountered exception: {str(e)}"
)
else:
raise e
nlp.to_disk(output_path / "model-final")
raise e
finally:
if output_path is not None:
final_model_path = output_path / "model-final"
@ -207,7 +210,9 @@ def create_evaluation_callback(
scores = nlp.evaluate(dev_examples)
# Calculate a weighted sum based on score_weights for the main score
try:
weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights)
weighted_score = sum(
scores.get(s, 0.0) * weights.get(s, 0.0) for s in weights
)
except KeyError as e:
keys = list(scores.keys())
err = Errors.E983.format(dict="score_weights", key=str(e), keys=keys)
@ -235,7 +240,7 @@ def train_while_improving(
with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`,
where info is a dict, and is_best_checkpoint is in [True, False, None] --
None indicating that the iteration was not evaluated as a checkpoint.
The evaluation is conducted by calling the evaluate callback, which should
The evaluation is conducted by calling the evaluate callback.
Positional arguments:
nlp: The spaCy pipeline to evaluate.
@ -377,7 +382,8 @@ def setup_printer(
try:
scores = [
"{0:.2f}".format(float(info["other_scores"][col])) for col in score_cols
"{0:.2f}".format(float(info["other_scores"].get(col, 0.0)))
for col in score_cols
]
except KeyError as e:
raise KeyError(
@ -403,7 +409,7 @@ def update_meta(
) -> None:
nlp.meta["performance"] = {}
for metric in training["score_weights"]:
nlp.meta["performance"][metric] = info["other_scores"][metric]
nlp.meta["performance"][metric] = info["other_scores"].get(metric, 0.0)
for pipe_name in nlp.pipe_names:
nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name]

14
spacy/default_config.cfg
View File

@ -23,12 +23,12 @@ after_pipeline_creation = null
# Training hyper-parameters and additional features.
[training]
seed = ${system:seed}
seed = ${system.seed}
dropout = 0.1
accumulate_gradient = 1
# Extra resources for transfer-learning or pseudo-rehearsal
init_tok2vec = ${paths:init_tok2vec}
raw_text = ${paths:raw}
init_tok2vec = ${paths.init_tok2vec}
raw_text = ${paths.raw}
vectors = null
# Controls early-stopping. 0 or -1 mean unlimited.
patience = 1600
@ -42,7 +42,7 @@ frozen_components = []
[training.train_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:train}
path = ${paths.train}
# Whether to train on sequences with 'gold standard' sentence boundaries
# and tokens. If you set this to true, take care to ensure your run-time
# data is passed in sentence-by-sentence via some prior preprocessing.
@ -54,7 +54,7 @@ limit = 0
[training.dev_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:dev}
path = ${paths.dev}
# Whether to train on sequences with 'gold standard' sentence boundaries
# and tokens. If you set this to true, take care to ensure your run-time
# data is passed in sentence-by-sentence via some prior preprocessing.
@ -98,8 +98,8 @@ max_length = 500
dropout = 0.2
n_save_every = null
batch_size = 3000
seed = ${system:seed}
use_pytorch_for_gpu_memory = ${system:use_pytorch_for_gpu_memory}
seed = ${system.seed}
use_pytorch_for_gpu_memory = ${system.use_pytorch_for_gpu_memory}
tok2vec_model = "components.tok2vec.model"
[pretraining.objective]

2
spacy/displacy/__init__.py
View File

@ -18,7 +18,7 @@ RENDER_WRAPPER = None
def render(
docs: Union[Iterable[Doc], Doc],
docs: Union[Iterable[Union[Doc, Span]], Doc, Span],
style: str = "dep",
page: bool = False,
minify: bool = False,

4
spacy/displacy/render.py
View File

@ -252,8 +252,10 @@ class EntityRenderer:
colors.update(user_color)
colors.update(options.get("colors", {}))
self.default_color = DEFAULT_ENTITY_COLOR
self.colors = colors
self.colors = {label.upper(): color for label, color in colors.items()}
self.ents = options.get("ents", None)
if self.ents is not None:
self.ents = [ent.upper() for ent in self.ents]
self.direction = DEFAULT_DIR
self.lang = DEFAULT_LANG
template = options.get("template")

4
spacy/displacy/templates.py
View File

@ -51,14 +51,14 @@ TPL_ENTS = """
TPL_ENT = """
<mark class="entity" style="background: {bg}; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
{text}
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">{label}</span>
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">{label}</span>
</mark>
"""
TPL_ENT_RTL = """
<mark class="entity" style="background: {bg}; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em">
{text}
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-right: 0.5rem">{label}</span>
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-right: 0.5rem">{label}</span>
</mark>
"""

14
spacy/errors.py
View File

@ -78,10 +78,11 @@ class Warnings:
"are currently: {langs}")
# TODO: fix numbering after merging develop into master
W090 = ("Could not locate any binary .spacy files in path '{path}'.")
W091 = ("Could not clean/remove the temp directory at {dir}: {msg}.")
W092 = ("Ignoring annotations for sentence starts, as dependency heads are set.")
W093 = ("Could not find any data to train the {name} on. Is your "
"input data correctly formatted ?")
"input data correctly formatted?")
W094 = ("Model '{model}' ({model_version}) specifies an under-constrained "
"spaCy version requirement: {version}. This can lead to compatibility "
"problems with older versions, or as new spaCy versions are "
@ -476,6 +477,10 @@ class Errors:
E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
# TODO: fix numbering after merging develop into master
E928 = ("A 'KnowledgeBase' should be written to / read from a file, but the "
"provided argument {loc} is an existing directory.")
E929 = ("A 'KnowledgeBase' could not be read from {loc} - the path does "
"not seem to exist.")
E930 = ("Received invalid get_examples callback in {name}.begin_training. "
"Expected function that returns an iterable of Example objects but "
"got: {obj}")
@ -503,8 +508,6 @@ class Errors:
"not found in pipeline. Available components: {opts}")
E945 = ("Can't copy pipeline component '{name}' from source. Expected loaded "
"nlp object, but got: {source}")
E946 = ("The Vocab for the knowledge base is not initialized. Did you forget to "
"call kb.initialize()?")
E947 = ("Matcher.add received invalid 'greedy' argument: expected "
"a string value from {expected} but got: '{arg}'")
E948 = ("Matcher.add received invalid 'patterns' argument: expected "
@ -600,7 +603,8 @@ class Errors:
"\"en_core_web_sm\" will copy the component from that model.\n\n{config}")
E985 = ("Can't load model from config file: no 'nlp' section found.\n\n{config}")
E986 = ("Could not create any training batches: check your input. "
"Perhaps discard_oversize should be set to False ?")
"Are the train and dev paths defined? "
"Is 'discard_oversize' set appropriately? ")
E987 = ("The text of an example training instance is either a Doc or "
"a string, but found {type} instead.")
E988 = ("Could not parse any training examples. Ensure the data is "
@ -610,8 +614,6 @@ class Errors:
"of the training data in spaCy 3.0 onwards. The 'update' "
"function should now be called with a batch of 'Example' "
"objects, instead of (text, annotation) tuples. ")
E990 = ("An entity linking component needs to be initialized with a "
"KnowledgeBase object, but found {type} instead.")
E991 = ("The function 'select_pipes' should be called with either a "
"'disable' argument to list the names of the pipe components "
"that should be disabled, or with an 'enable' argument that "

13
spacy/gold/corpus.py
View File

@ -1,8 +1,10 @@
import warnings
from typing import Union, List, Iterable, Iterator, TYPE_CHECKING, Callable
from pathlib import Path
from .. import util
from .example import Example
from ..errors import Warnings
from ..tokens import DocBin, Doc
from ..vocab import Vocab
@ -10,6 +12,8 @@ if TYPE_CHECKING:
# This lets us add type hints for mypy etc. without causing circular imports
from ..language import Language # noqa: F401
FILE_TYPE = ".spacy"
@util.registry.readers("spacy.Corpus.v1")
def create_docbin_reader(
@ -53,8 +57,9 @@ class Corpus:
@staticmethod
def walk_corpus(path: Union[str, Path]) -> List[Path]:
path = util.ensure_path(path)
if not path.is_dir():
if not path.is_dir() and path.parts[-1].endswith(FILE_TYPE):
return [path]
orig_path = path
paths = [path]
locs = []
seen = set()
@ -66,8 +71,10 @@ class Corpus:
continue
elif path.is_dir():
paths.extend(path.iterdir())
elif path.parts[-1].endswith(".spacy"):
elif path.parts[-1].endswith(FILE_TYPE):
locs.append(path)
if len(locs) == 0:
warnings.warn(Warnings.W090.format(path=orig_path))
return locs
def __call__(self, nlp: "Language") -> Iterator[Example]:
@ -135,7 +142,7 @@ class Corpus:
i = 0
for loc in locs:
loc = util.ensure_path(loc)
if loc.parts[-1].endswith(".spacy"):
if loc.parts[-1].endswith(FILE_TYPE):
doc_bin = DocBin().from_disk(loc)
docs = doc_bin.get_docs(vocab)
for doc in docs:

2
spacy/kb.pxd
View File

@ -140,7 +140,7 @@ cdef class KnowledgeBase:
self._entries.push_back(entry)
self._aliases_table.push_back(alias)
cpdef load_bulk(self, loc)
cpdef from_disk(self, loc)
cpdef set_entities(self, entity_list, freq_list, vector_list)

54
spacy/kb.pyx
View File

@ -1,4 +1,5 @@
# cython: infer_types=True, profile=True
from typing import Iterator
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from cpython.exc cimport PyErr_SetFromErrno
@ -64,6 +65,16 @@ cdef class Candidate:
return self.prior_prob
def get_candidates(KnowledgeBase kb, span) -> Iterator[Candidate]:
"""
Return candidate entities for a given span by using the text of the span as the alias
and fetching appropriate entries from the index.
This particular function is optimized to work with the built-in KB functionality,
but any other custom candidate generation method can be used in combination with the KB as well.
"""
return kb.get_alias_candidates(span.text)
cdef class KnowledgeBase:
"""A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
@ -71,25 +82,16 @@ cdef class KnowledgeBase:
DOCS: https://spacy.io/api/kb
"""
def __init__(self, entity_vector_length):
"""Create a KnowledgeBase. Make sure to call kb.initialize() before using it."""
def __init__(self, Vocab vocab, entity_vector_length):
"""Create a KnowledgeBase."""
self.mem = Pool()
self.entity_vector_length = entity_vector_length
self._entry_index = PreshMap()
self._alias_index = PreshMap()
self.vocab = None
def initialize(self, Vocab vocab):
self.vocab = vocab
self.vocab.strings.add("")
self._create_empty_vectors(dummy_hash=self.vocab.strings[""])
def require_vocab(self):
if self.vocab is None:
raise ValueError(Errors.E946)
@property
def entity_vector_length(self):
"""RETURNS (uint64): length of the entity vectors"""
@ -102,14 +104,12 @@ cdef class KnowledgeBase:
return len(self._entry_index)
def get_entity_strings(self):
self.require_vocab()
return [self.vocab.strings[x] for x in self._entry_index]
def get_size_aliases(self):
return len(self._alias_index)
def get_alias_strings(self):
self.require_vocab()
return [self.vocab.strings[x] for x in self._alias_index]
def add_entity(self, unicode entity, float freq, vector[float] entity_vector):
@ -117,7 +117,6 @@ cdef class KnowledgeBase:
Add an entity to the KB, optionally specifying its log probability based on corpus frequency
Return the hash of the entity ID/name at the end.
"""
self.require_vocab()
cdef hash_t entity_hash = self.vocab.strings.add(entity)
# Return if this entity was added before
@ -140,7 +139,6 @@ cdef class KnowledgeBase:
return entity_hash
cpdef set_entities(self, entity_list, freq_list, vector_list):
self.require_vocab()
if len(entity_list) != len(freq_list) or len(entity_list) != len(vector_list):
raise ValueError(Errors.E140)
@ -176,12 +174,10 @@ cdef class KnowledgeBase:
i += 1
def contains_entity(self, unicode entity):
self.require_vocab()
cdef hash_t entity_hash = self.vocab.strings.add(entity)
return entity_hash in self._entry_index
def contains_alias(self, unicode alias):
self.require_vocab()
cdef hash_t alias_hash = self.vocab.strings.add(alias)
return alias_hash in self._alias_index
@ -190,7 +186,6 @@ cdef class KnowledgeBase:
For a given alias, add its potential entities and prior probabilies to the KB.
Return the alias_hash at the end
"""
self.require_vocab()
# Throw an error if the length of entities and probabilities are not the same
if not len(entities) == len(probabilities):
raise ValueError(Errors.E132.format(alias=alias,
@ -234,7 +229,6 @@ cdef class KnowledgeBase:
Throw an error if this entity+prior prob would exceed the sum of 1.
For efficiency, it's best to use the method `add_alias` as much as possible instead of this one.
"""
self.require_vocab()
# Check if the alias exists in the KB
cdef hash_t alias_hash = self.vocab.strings[alias]
if not alias_hash in self._alias_index:
@ -274,14 +268,12 @@ cdef class KnowledgeBase:
alias_entry.probs = probs
self._aliases_table[alias_index] = alias_entry
def get_candidates(self, unicode alias):
def get_alias_candidates(self, unicode alias) -> Iterator[Candidate]:
"""
Return candidate entities for an alias. Each candidate defines the entity, the original alias,
and the prior probability of that alias resolving to that entity.
If the alias is not known in the KB, and empty list is returned.
"""
self.require_vocab()
cdef hash_t alias_hash = self.vocab.strings[alias]
if not alias_hash in self._alias_index:
return []
@ -298,7 +290,6 @@ cdef class KnowledgeBase:
if entry_index != 0]
def get_vector(self, unicode entity):
self.require_vocab()
cdef hash_t entity_hash = self.vocab.strings[entity]
# Return an empty list if this entity is unknown in this KB
@ -311,7 +302,6 @@ cdef class KnowledgeBase:
def get_prior_prob(self, unicode entity, unicode alias):
""" Return the prior probability of a given alias being linked to a given entity,
or return 0.0 when this combination is not known in the knowledge base"""
self.require_vocab()
cdef hash_t alias_hash = self.vocab.strings[alias]
cdef hash_t entity_hash = self.vocab.strings[entity]
@ -329,8 +319,7 @@ cdef class KnowledgeBase:
return 0.0
def dump(self, loc):
self.require_vocab()
def to_disk(self, loc):
cdef Writer writer = Writer(loc)
writer.write_header(self.get_size_entities(), self.entity_vector_length)
@ -370,7 +359,7 @@ cdef class KnowledgeBase:
writer.close()
cpdef load_bulk(self, loc):
cpdef from_disk(self, loc):
cdef hash_t entity_hash
cdef hash_t alias_hash
cdef int64_t entry_index
@ -462,12 +451,11 @@ cdef class KnowledgeBase:
cdef class Writer:
def __init__(self, object loc):
if path.exists(loc):
assert not path.isdir(loc), f"{loc} is directory"
if isinstance(loc, Path):
loc = bytes(loc)
if path.exists(loc):
assert not path.isdir(loc), "%s is directory." % loc
if path.isdir(loc):
raise ValueError(Errors.E928.format(loc=loc))
cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
self._fp = fopen(<char*>bytes_loc, 'wb')
if not self._fp:
@ -511,8 +499,10 @@ cdef class Reader:
def __init__(self, object loc):
if isinstance(loc, Path):
loc = bytes(loc)
assert path.exists(loc)
assert not path.isdir(loc)
if not path.exists(loc):
raise ValueError(Errors.E929.format(loc=loc))
if path.isdir(loc):
raise ValueError(Errors.E928.format(loc=loc))
cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
self._fp = fopen(<char*>bytes_loc, 'rb')
if not self._fp:

26
spacy/language.py
View File

@ -439,8 +439,6 @@ class Language:
assigns: Iterable[str] = tuple(),
requires: Iterable[str] = tuple(),
retokenizes: bool = False,
scores: Iterable[str] = tuple(),
default_score_weights: Dict[str, float] = SimpleFrozenDict(),
func: Optional[Callable[[Doc], Doc]] = None,
) -> Callable:
"""Register a new pipeline component. Can be used for stateless function
@ -456,12 +454,6 @@ class Language:
e.g. "token.ent_id". Used for pipeline analyis.
retokenizes (bool): Whether the component changes the tokenization.
Used for pipeline analysis.
scores (Iterable[str]): All scores set by the component if it's trainable,
e.g. ["ents_f", "ents_r", "ents_p"].
default_score_weights (Dict[str, float]): The scores to report during
training, and their default weight towards the final score used to
select the best model. Weights should sum to 1.0 per component and
will be combined and normalized for the whole pipeline.
func (Optional[Callable]): Factory function if not used as a decorator.
DOCS: https://spacy.io/api/language#component
@ -482,8 +474,6 @@ class Language:
assigns=assigns,
requires=requires,
retokenizes=retokenizes,
scores=scores,
default_score_weights=default_score_weights,
func=factory_func,
)
return component_func
@ -782,9 +772,15 @@ class Language:
self.remove_pipe(name)
if not len(self.pipeline) or pipe_index == len(self.pipeline):
# we have no components to insert before/after, or we're replacing the last component
self.add_pipe(factory_name, name=name)
self.add_pipe(factory_name, name=name, config=config, validate=validate)
else:
self.add_pipe(factory_name, name=name, before=pipe_index)
self.add_pipe(
factory_name,
name=name,
before=pipe_index,
config=config,
validate=validate,
)
def rename_pipe(self, old_name: str, new_name: str) -> None:
"""Rename a pipeline component.
@ -1112,7 +1108,6 @@ class Language:
self,
examples: Iterable[Example],
*,
verbose: bool = False,
batch_size: int = 256,
scorer: Optional[Scorer] = None,
component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
@ -1121,7 +1116,6 @@ class Language:
"""Evaluate a model's pipeline components.
examples (Iterable[Example]): `Example` objects.
verbose (bool): Print debugging information.
batch_size (int): Batch size to use.
scorer (Optional[Scorer]): Scorer to use. If not passed in, a new one
will be created.
@ -1140,7 +1134,6 @@ class Language:
scorer_cfg = {}
if scorer is None:
kwargs = dict(scorer_cfg)
kwargs.setdefault("verbose", verbose)
kwargs.setdefault("nlp", self)
scorer = Scorer(**kwargs)
texts = [eg.reference.text for eg in examples]
@ -1163,8 +1156,7 @@ class Language:
docs = list(docs)
end_time = timer()
for i, (doc, eg) in enumerate(zip(docs, examples)):
if verbose:
print(doc)
util.logger.debug(doc)
eg.predicted = doc
results = scorer.score(examples)
n_words = sum(len(eg.predicted) for eg in examples)

30
spacy/ml/models/entity_linker.py
View File

@ -1,9 +1,9 @@
from typing import Optional
from typing import Optional, Callable, Iterable
from thinc.api import chain, clone, list2ragged, reduce_mean, residual
from thinc.api import Model, Maxout, Linear
from ...util import registry
from ...kb import KnowledgeBase
from ...kb import KnowledgeBase, Candidate, get_candidates
from ...vocab import Vocab
@ -25,15 +25,23 @@ def build_nel_encoder(tok2vec: Model, nO: Optional[int] = None) -> Model:
@registry.assets.register("spacy.KBFromFile.v1")
def load_kb(vocab_path: str, kb_path: str) -> KnowledgeBase:
vocab = Vocab().from_disk(vocab_path)
kb = KnowledgeBase(entity_vector_length=1)
kb.initialize(vocab)
kb.load_bulk(kb_path)
return kb
def load_kb(kb_path: str) -> Callable[[Vocab], KnowledgeBase]:
def kb_from_file(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
kb.from_disk(kb_path)
return kb
return kb_from_file
@registry.assets.register("spacy.EmptyKB.v1")
def empty_kb(entity_vector_length: int) -> KnowledgeBase:
kb = KnowledgeBase(entity_vector_length=entity_vector_length)
return kb
def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
def empty_kb_factory(vocab):
return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
return empty_kb_factory
@registry.assets.register("spacy.CandidateGenerator.v1")
def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
return get_candidates

53
spacy/pipeline/entity_linker.py
View File

@ -6,7 +6,7 @@ from thinc.api import CosineDistance, get_array_module, Model, Optimizer, Config
from thinc.api import set_dropout_rate
import warnings
from ..kb import KnowledgeBase
from ..kb import KnowledgeBase, Candidate
from ..tokens import Doc
from .pipe import Pipe, deserialize_config
from ..language import Language
@ -32,35 +32,30 @@ subword_features = true
"""
DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
default_kb_config = """
[kb]
@assets = "spacy.EmptyKB.v1"
entity_vector_length = 64
"""
DEFAULT_NEL_KB = Config().from_str(default_kb_config)["kb"]
@Language.factory(
"entity_linker",
requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
assigns=["token.ent_kb_id"],
default_config={
"kb": DEFAULT_NEL_KB,
"kb_loader": {"@assets": "spacy.EmptyKB.v1", "entity_vector_length": 64},
"model": DEFAULT_NEL_MODEL,
"labels_discard": [],
"incl_prior": True,
"incl_context": True,
"get_candidates": {"@assets": "spacy.CandidateGenerator.v1"},
},
)
def make_entity_linker(
nlp: Language,
name: str,
model: Model,
kb: KnowledgeBase,
kb_loader: Callable[[Vocab], KnowledgeBase],
*,
labels_discard: Iterable[str],
incl_prior: bool,
incl_context: bool,
get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
):
"""Construct an EntityLinker component.
@ -76,10 +71,11 @@ def make_entity_linker(
nlp.vocab,
model,
name,
kb=kb,
kb_loader=kb_loader,
labels_discard=labels_discard,
incl_prior=incl_prior,
incl_context=incl_context,
get_candidates=get_candidates,
)
@ -97,10 +93,11 @@ class EntityLinker(Pipe):
model: Model,
name: str = "entity_linker",
*,
kb: KnowledgeBase,
kb_loader: Callable[[Vocab], KnowledgeBase],
labels_discard: Iterable[str],
incl_prior: bool,
incl_context: bool,
get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
) -> None:
"""Initialize an entity linker.
@ -108,7 +105,7 @@ class EntityLinker(Pipe):
model (thinc.api.Model): The Thinc Model powering the pipeline component.
name (str): The component instance name, used to add entries to the
losses during training.
kb (KnowledgeBase): The KnowledgeBase holding all entities and their aliases.
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
incl_context (bool): Whether or not to include the local context in the model.
@ -119,17 +116,12 @@ class EntityLinker(Pipe):
self.model = model
self.name = name
cfg = {
"kb": kb,
"labels_discard": list(labels_discard),
"incl_prior": incl_prior,
"incl_context": incl_context,
}
if not isinstance(kb, KnowledgeBase):
raise ValueError(Errors.E990.format(type=type(self.kb)))
kb.initialize(vocab)
self.kb = kb
if "kb" in cfg:
del cfg["kb"] # we don't want to duplicate its serialization
self.kb = kb_loader(self.vocab)
self.get_candidates = get_candidates
self.cfg = dict(cfg)
self.distance = CosineDistance(normalize=False)
# how many neightbour sentences to take into account
@ -326,10 +318,11 @@ class EntityLinker(Pipe):
end_token = sentences[end_sentence].end
sent_doc = doc[start_token:end_token].as_doc()
# currently, the context is the same for each entity in a sentence (should be refined)
sentence_encoding = self.model.predict([sent_doc])[0]
xp = get_array_module(sentence_encoding)
sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)
xp = self.model.ops.xp
if self.cfg.get("incl_context"):
sentence_encoding = self.model.predict([sent_doc])[0]
sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)
for ent in sent.ents:
entity_count += 1
to_discard = self.cfg.get("labels_discard", [])
@ -337,7 +330,7 @@ class EntityLinker(Pipe):
# ignoring this entity - setting to NIL
final_kb_ids.append(self.NIL)
else:
candidates = self.kb.get_candidates(ent.text)
candidates = self.get_candidates(self.kb, ent)
if not candidates:
# no prediction possible for this entity - setting to NIL
final_kb_ids.append(self.NIL)
@ -421,10 +414,9 @@ class EntityLinker(Pipe):
DOCS: https://spacy.io/api/entitylinker#to_disk
"""
serialize = {}
self.cfg["entity_width"] = self.kb.entity_vector_length
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
serialize["vocab"] = lambda p: self.vocab.to_disk(p)
serialize["kb"] = lambda p: self.kb.dump(p)
serialize["kb"] = lambda p: self.kb.to_disk(p)
serialize["model"] = lambda p: self.model.to_disk(p)
util.to_disk(path, serialize, exclude)
@ -446,15 +438,10 @@ class EntityLinker(Pipe):
except AttributeError:
raise ValueError(Errors.E149) from None
def load_kb(p):
self.kb = KnowledgeBase(entity_vector_length=self.cfg["entity_width"])
self.kb.initialize(self.vocab)
self.kb.load_bulk(p)
deserialize = {}
deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
deserialize["cfg"] = lambda p: self.cfg.update(deserialize_config(p))
deserialize["kb"] = load_kb
deserialize["kb"] = lambda p: self.kb.from_disk(p)
deserialize["model"] = load_model
util.from_disk(path, deserialize, exclude)
return self

0
spacy/pipeline/nn_parser.pyx
View File

1
spacy/pipeline/tagger.pyx
View File

@ -68,7 +68,6 @@ class Tagger(Pipe):
name (str): The component instance name, used to add entries to the
losses during training.
labels (List): The set of labels. Defaults to None.
set_morphology (bool): Whether to set morphological features.
DOCS: https://spacy.io/api/tagger#init
"""

47
spacy/schemas.py
View File

@ -167,18 +167,20 @@ class ModelMetaSchema(BaseModel):
lang: StrictStr = Field(..., title="Two-letter language code, e.g. 'en'")
name: StrictStr = Field(..., title="Model name")
version: StrictStr = Field(..., title="Model version")
spacy_version: Optional[StrictStr] = Field(None, title="Compatible spaCy version identifier")
parent_package: Optional[StrictStr] = Field("spacy", title="Name of parent spaCy package, e.g. spacy or spacy-nightly")
pipeline: Optional[List[StrictStr]] = Field([], title="Names of pipeline components")
description: Optional[StrictStr] = Field(None, title="Model description")
license: Optional[StrictStr] = Field(None, title="Model license")
author: Optional[StrictStr] = Field(None, title="Model author name")
email: Optional[StrictStr] = Field(None, title="Model author email")
url: Optional[StrictStr] = Field(None, title="Model author URL")
sources: Optional[Union[List[StrictStr], Dict[str, str]]] = Field(None, title="Training data sources")
vectors: Optional[Dict[str, Any]] = Field(None, title="Included word vectors")
accuracy: Optional[Dict[str, Union[float, int]]] = Field(None, title="Accuracy numbers")
speed: Optional[Dict[str, Union[float, int]]] = Field(None, title="Speed evaluation numbers")
spacy_version: StrictStr = Field("", title="Compatible spaCy version identifier")
parent_package: StrictStr = Field("spacy", title="Name of parent spaCy package, e.g. spacy or spacy-nightly")
pipeline: List[StrictStr] = Field([], title="Names of pipeline components")
description: StrictStr = Field("", title="Model description")
license: StrictStr = Field("", title="Model license")
author: StrictStr = Field("", title="Model author name")
email: StrictStr = Field("", title="Model author email")
url: StrictStr = Field("", title="Model author URL")
sources: Optional[Union[List[StrictStr], List[Dict[str, str]]]] = Field(None, title="Training data sources")
vectors: Dict[str, Any] = Field({}, title="Included word vectors")
labels: Dict[str, Dict[str, List[str]]] = Field({}, title="Component labels, keyed by component name")
accuracy: Dict[str, Union[float, Dict[str, float]]] = Field({}, title="Accuracy numbers")
speed: Dict[str, Union[float, int]] = Field({}, title="Speed evaluation numbers")
spacy_git_version: StrictStr = Field("", title="Commit of spaCy version used")
# fmt: on
@ -301,7 +303,7 @@ class ProjectConfigCommand(BaseModel):
class ProjectConfigSchema(BaseModel):
# fmt: off
variables: Dict[StrictStr, Union[str, int, float, bool]] = Field({}, title="Optional variables to substitute in commands")
vars: Dict[StrictStr, Any] = Field({}, title="Optional variables to substitute in commands")
assets: List[ProjectConfigAsset] = Field([], title="Data assets")
workflows: Dict[StrictStr, List[StrictStr]] = Field({}, title="Named workflows, mapped to list of project commands to run in order")
commands: List[ProjectConfigCommand] = Field([], title="Project command shortucts")
@ -309,3 +311,22 @@ class ProjectConfigSchema(BaseModel):
class Config:
title = "Schema for project configuration file"
# Recommendations for init config workflows
class RecommendationTrfItem(BaseModel):
name: str
size_factor: int
class RecommendationTrf(BaseModel):
efficiency: RecommendationTrfItem
accuracy: RecommendationTrfItem
class RecommendationSchema(BaseModel):
word_vectors: Optional[str] = None
transformer: Optional[RecommendationTrf] = None
has_letters: bool = True

13
spacy/scorer.py
View File

@ -2,7 +2,7 @@ from typing import Optional, Iterable, Dict, Any, Callable, Tuple, TYPE_CHECKING
import numpy as np
from .gold import Example
from .tokens import Token, Doc
from .tokens import Token, Doc, Span
from .errors import Errors
from .util import get_lang_class
from .morphology import Morphology
@ -250,15 +250,16 @@ class Scorer:
examples: Iterable[Example],
attr: str,
*,
getter: Callable[[Doc, str], Any] = getattr,
getter: Callable[[Doc, str], Iterable[Span]] = getattr,
**cfg,
) -> Dict[str, Any]:
"""Returns PRF scores for labeled spans.
examples (Iterable[Example]): Examples to score
attr (str): The attribute to score.
getter (Callable[[Doc, str], Any]): Defaults to getattr. If provided,
getter(doc, attr) should return the spans for the individual doc.
getter (Callable[[Doc, str], Iterable[Span]]): Defaults to getattr. If
provided, getter(doc, attr) should return the spans for the
individual doc.
RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under
the keys attr_p/r/f and the per-type PRF scores under attr_per_type.
@ -444,7 +445,7 @@ class Scorer:
*,
getter: Callable[[Token, str], Any] = getattr,
head_attr: str = "head",
head_getter: Callable[[Token, str], Any] = getattr,
head_getter: Callable[[Token, str], Token] = getattr,
ignore_labels: Tuple[str] = tuple(),
**cfg,
) -> Dict[str, Any]:
@ -458,7 +459,7 @@ class Scorer:
individual token.
head_attr (str): The attribute containing the head token. Defaults to
'head'.
head_getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
head_getter (Callable[[Token, str], Token]): Defaults to getattr. If provided,
head_getter(token, attr) should return the value of the head for an
individual token.
ignore_labels (Tuple): Labels to ignore while scoring (e.g., punct).

173
spacy/tests/pipeline/test_entity_linker.py
View File

@ -1,6 +1,7 @@
from typing import Callable, Iterable
import pytest
from spacy.kb import KnowledgeBase
from spacy.kb import KnowledgeBase, get_candidates, Candidate
from spacy import util, registry
from spacy.gold import Example
@ -21,8 +22,7 @@ def assert_almost_equal(a, b):
def test_kb_valid_entities(nlp):
"""Test the valid construction of a KB with 3 entities and two aliases"""
mykb = KnowledgeBase(entity_vector_length=3)
mykb.initialize(nlp.vocab)
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[8, 4, 3])
@ -51,8 +51,7 @@ def test_kb_valid_entities(nlp):
def test_kb_invalid_entities(nlp):
"""Test the invalid construction of a KB with an alias linked to a non-existing entity"""
mykb = KnowledgeBase(entity_vector_length=1)
mykb.initialize(nlp.vocab)
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
@ -68,8 +67,7 @@ def test_kb_invalid_entities(nlp):
def test_kb_invalid_probabilities(nlp):
"""Test the invalid construction of a KB with wrong prior probabilities"""
mykb = KnowledgeBase(entity_vector_length=1)
mykb.initialize(nlp.vocab)
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
@ -83,8 +81,7 @@ def test_kb_invalid_probabilities(nlp):
def test_kb_invalid_combination(nlp):
"""Test the invalid construction of a KB with non-matching entity and probability lists"""
mykb = KnowledgeBase(entity_vector_length=1)
mykb.initialize(nlp.vocab)
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
@ -100,8 +97,7 @@ def test_kb_invalid_combination(nlp):
def test_kb_invalid_entity_vector(nlp):
"""Test the invalid construction of a KB with non-matching entity vector lengths"""
mykb = KnowledgeBase(entity_vector_length=3)
mykb.initialize(nlp.vocab)
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1, 2, 3])
@ -117,14 +113,14 @@ def test_kb_default(nlp):
assert len(entity_linker.kb) == 0
assert entity_linker.kb.get_size_entities() == 0
assert entity_linker.kb.get_size_aliases() == 0
# default value from pipeline.entity_linker
# 64 is the default value from pipeline.entity_linker
assert entity_linker.kb.entity_vector_length == 64
def test_kb_custom_length(nlp):
"""Test that the default (empty) KB can be configured with a custom entity length"""
entity_linker = nlp.add_pipe(
"entity_linker", config={"kb": {"entity_vector_length": 35}}
"entity_linker", config={"kb_loader": {"entity_vector_length": 35}}
)
assert len(entity_linker.kb) == 0
assert entity_linker.kb.get_size_entities() == 0
@ -141,7 +137,7 @@ def test_kb_undefined(nlp):
def test_kb_empty(nlp):
"""Test that the EL can't train with an empty KB"""
config = {"kb": {"@assets": "spacy.EmptyKB.v1", "entity_vector_length": 342}}
config = {"kb_loader": {"@assets": "spacy.EmptyKB.v1", "entity_vector_length": 342}}
entity_linker = nlp.add_pipe("entity_linker", config=config)
assert len(entity_linker.kb) == 0
with pytest.raises(ValueError):
@ -150,8 +146,13 @@ def test_kb_empty(nlp):
def test_candidate_generation(nlp):
"""Test correct candidate generation"""
mykb = KnowledgeBase(entity_vector_length=1)
mykb.initialize(nlp.vocab)
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
doc = nlp("douglas adam Adam shrubbery")
douglas_ent = doc[0:1]
adam_ent = doc[1:2]
Adam_ent = doc[2:3]
shrubbery_ent = doc[3:4]
# adding entities
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
@ -163,21 +164,76 @@ def test_candidate_generation(nlp):
mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
# test the size of the relevant candidates
assert len(mykb.get_candidates("douglas")) == 2
assert len(mykb.get_candidates("adam")) == 1
assert len(mykb.get_candidates("shrubbery")) == 0
assert len(get_candidates(mykb, douglas_ent)) == 2
assert len(get_candidates(mykb, adam_ent)) == 1
assert len(get_candidates(mykb, Adam_ent)) == 0 # default case sensitive
assert len(get_candidates(mykb, shrubbery_ent)) == 0
# test the content of the candidates
assert mykb.get_candidates("adam")[0].entity_ == "Q2"
assert mykb.get_candidates("adam")[0].alias_ == "adam"
assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 12)
assert_almost_equal(mykb.get_candidates("adam")[0].prior_prob, 0.9)
assert get_candidates(mykb, adam_ent)[0].entity_ == "Q2"
assert get_candidates(mykb, adam_ent)[0].alias_ == "adam"
assert_almost_equal(get_candidates(mykb, adam_ent)[0].entity_freq, 12)
assert_almost_equal(get_candidates(mykb, adam_ent)[0].prior_prob, 0.9)
def test_el_pipe_configuration(nlp):
"""Test correct candidate generation as part of the EL pipe"""
nlp.add_pipe("sentencizer")
pattern = {"label": "PERSON", "pattern": [{"LOWER": "douglas"}]}
ruler = nlp.add_pipe("entity_ruler")
ruler.add_patterns([pattern])
@registry.assets.register("myAdamKB.v1")
def mykb() -> Callable[["Vocab"], KnowledgeBase]:
def create_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
kb.add_entity(entity="Q2", freq=12, entity_vector=[2])
kb.add_entity(entity="Q3", freq=5, entity_vector=[3])
kb.add_alias(
alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1]
)
return kb
return create_kb
# run an EL pipe without a trained context encoder, to check the candidate generation step only
nlp.add_pipe(
"entity_linker",
config={"kb_loader": {"@assets": "myAdamKB.v1"}, "incl_context": False},
)
# With the default get_candidates function, matching is case-sensitive
text = "Douglas and douglas are not the same."
doc = nlp(text)
assert doc[0].ent_kb_id_ == "NIL"
assert doc[1].ent_kb_id_ == ""
assert doc[2].ent_kb_id_ == "Q2"
def get_lowercased_candidates(kb, span):
return kb.get_alias_candidates(span.text.lower())
@registry.assets.register("spacy.LowercaseCandidateGenerator.v1")
def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
return get_lowercased_candidates
# replace the pipe with a new one with with a different candidate generator
nlp.replace_pipe(
"entity_linker",
"entity_linker",
config={
"kb_loader": {"@assets": "myAdamKB.v1"},
"incl_context": False,
"get_candidates": {"@assets": "spacy.LowercaseCandidateGenerator.v1"},
},
)
doc = nlp(text)
assert doc[0].ent_kb_id_ == "Q2"
assert doc[1].ent_kb_id_ == ""
assert doc[2].ent_kb_id_ == "Q2"
def test_append_alias(nlp):
"""Test that we can append additional alias-entity pairs"""
mykb = KnowledgeBase(entity_vector_length=1)
mykb.initialize(nlp.vocab)
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
@ -189,26 +245,25 @@ def test_append_alias(nlp):
mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
# test the size of the relevant candidates
assert len(mykb.get_candidates("douglas")) == 2
assert len(mykb.get_alias_candidates("douglas")) == 2
# append an alias
mykb.append_alias(alias="douglas", entity="Q1", prior_prob=0.2)
# test the size of the relevant candidates has been incremented
assert len(mykb.get_candidates("douglas")) == 3
assert len(mykb.get_alias_candidates("douglas")) == 3
# append the same alias-entity pair again should not work (will throw a warning)
with pytest.warns(UserWarning):
mykb.append_alias(alias="douglas", entity="Q1", prior_prob=0.3)
# test the size of the relevant candidates remained unchanged
assert len(mykb.get_candidates("douglas")) == 3
assert len(mykb.get_alias_candidates("douglas")) == 3
def test_append_invalid_alias(nlp):
"""Test that append an alias will throw an error if prior probs are exceeding 1"""
mykb = KnowledgeBase(entity_vector_length=1)
mykb.initialize(nlp.vocab)
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
@ -228,16 +283,18 @@ def test_preserving_links_asdoc(nlp):
"""Test that Span.as_doc preserves the existing entity links"""
@registry.assets.register("myLocationsKB.v1")
def dummy_kb() -> KnowledgeBase:
mykb = KnowledgeBase(entity_vector_length=1)
mykb.initialize(nlp.vocab)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=8, entity_vector=[1])
# adding aliases
mykb.add_alias(alias="Boston", entities=["Q1"], probabilities=[0.7])
mykb.add_alias(alias="Denver", entities=["Q2"], probabilities=[0.6])
return mykb
def dummy_kb() -> Callable[["Vocab"], KnowledgeBase]:
def create_kb(vocab):
mykb = KnowledgeBase(vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=8, entity_vector=[1])
# adding aliases
mykb.add_alias(alias="Boston", entities=["Q1"], probabilities=[0.7])
mykb.add_alias(alias="Denver", entities=["Q2"], probabilities=[0.6])
return mykb
return create_kb
# set up pipeline with NER (Entity Ruler) and NEL (prior probability only, model not trained)
nlp.add_pipe("sentencizer")
@ -247,7 +304,7 @@ def test_preserving_links_asdoc(nlp):
]
ruler = nlp.add_pipe("entity_ruler")
ruler.add_patterns(patterns)
el_config = {"kb": {"@assets": "myLocationsKB.v1"}, "incl_prior": False}
el_config = {"kb_loader": {"@assets": "myLocationsKB.v1"}, "incl_prior": False}
el_pipe = nlp.add_pipe("entity_linker", config=el_config, last=True)
el_pipe.begin_training(lambda: [])
el_pipe.incl_context = False
@ -331,24 +388,28 @@ def test_overfitting_IO():
train_examples.append(Example.from_dict(doc, annotation))
@registry.assets.register("myOverfittingKB.v1")
def dummy_kb() -> KnowledgeBase:
# create artificial KB - assign same prior weight to the two russ cochran's
# Q2146908 (Russ Cochran): American golfer
# Q7381115 (Russ Cochran): publisher
mykb = KnowledgeBase(entity_vector_length=3)
mykb.initialize(nlp.vocab)
mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
mykb.add_alias(
alias="Russ Cochran",
entities=["Q2146908", "Q7381115"],
probabilities=[0.5, 0.5],
)
return mykb
def dummy_kb() -> Callable[["Vocab"], KnowledgeBase]:
def create_kb(vocab):
# create artificial KB - assign same prior weight to the two russ cochran's
# Q2146908 (Russ Cochran): American golfer
# Q7381115 (Russ Cochran): publisher
mykb = KnowledgeBase(vocab, entity_vector_length=3)
mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
mykb.add_alias(
alias="Russ Cochran",
entities=["Q2146908", "Q7381115"],
probabilities=[0.5, 0.5],
)
return mykb
return create_kb
# Create the Entity Linker component and add it to the pipeline
nlp.add_pipe(
"entity_linker", config={"kb": {"@assets": "myOverfittingKB.v1"}}, last=True
"entity_linker",
config={"kb_loader": {"@assets": "myOverfittingKB.v1"}},
last=True,
)
# train the NEL pipe

6
spacy/tests/pipeline/test_pipe_factories.py
View File

@ -356,13 +356,13 @@ def test_language_factories_combine_score_weights(weights, expected):
def test_language_factories_scores():
name = "test_language_factories_scores"
func = lambda doc: doc
func = lambda nlp, name: lambda doc: doc
weights1 = {"a1": 0.5, "a2": 0.5}
weights2 = {"b1": 0.2, "b2": 0.7, "b3": 0.1}
Language.component(
Language.factory(
f"{name}1", scores=list(weights1), default_score_weights=weights1, func=func,
)
Language.component(
Language.factory(
f"{name}2", scores=list(weights2), default_score_weights=weights2, func=func,
)
meta1 = Language.get_factory_meta(f"{name}1")

8
spacy/tests/pipeline/test_pipe_methods.py
View File

@ -78,6 +78,14 @@ def test_replace_last_pipe(nlp):
assert nlp.pipe_names == ["sentencizer", "ner"]
def test_replace_pipe_config(nlp):
nlp.add_pipe("entity_linker")
nlp.add_pipe("sentencizer")
assert nlp.get_pipe("entity_linker").cfg["incl_prior"] == True
nlp.replace_pipe("entity_linker", "entity_linker", config={"incl_prior": False})
assert nlp.get_pipe("entity_linker").cfg["incl_prior"] == False
@pytest.mark.parametrize("old_name,new_name", [("old_pipe", "new_pipe")])
def test_rename_pipe(nlp, old_name, new_name):
with pytest.raises(ValueError):

14
spacy/tests/regression/test_issue4501-5000.py
View File

@ -65,7 +65,7 @@ def test_issue4590(en_vocab):
def test_issue4651_with_phrase_matcher_attr():
"""Test that the EntityRuler PhraseMatcher is deserialize correctly using
"""Test that the EntityRuler PhraseMatcher is deserialized correctly using
the method from_disk when the EntityRuler argument phrase_matcher_attr is
specified.
"""
@ -87,7 +87,7 @@ def test_issue4651_with_phrase_matcher_attr():
def test_issue4651_without_phrase_matcher_attr():
"""Test that the EntityRuler PhraseMatcher is deserialize correctly using
"""Test that the EntityRuler PhraseMatcher is deserialized correctly using
the method from_disk when the EntityRuler argument phrase_matcher_attr is
not specified.
"""
@ -139,8 +139,7 @@ def test_issue4665():
def test_issue4674():
"""Test that setting entities with overlapping identifiers does not mess up IO"""
nlp = English()
kb = KnowledgeBase(entity_vector_length=3)
kb.initialize(nlp.vocab)
kb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
vector1 = [0.9, 1.1, 1.01]
vector2 = [1.8, 2.25, 2.01]
with pytest.warns(UserWarning):
@ -156,10 +155,9 @@ def test_issue4674():
if not dir_path.exists():
dir_path.mkdir()
file_path = dir_path / "kb"
kb.dump(str(file_path))
kb2 = KnowledgeBase(entity_vector_length=3)
kb2.initialize(nlp.vocab)
kb2.load_bulk(str(file_path))
kb.to_disk(str(file_path))
kb2 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
kb2.from_disk(str(file_path))
assert kb2.get_size_entities() == 1

25
spacy/tests/regression/test_issue5230.py
View File

@ -1,3 +1,4 @@
from typing import Callable
import warnings
from unittest import TestCase
import pytest
@ -70,13 +71,15 @@ def entity_linker():
nlp = Language()
@registry.assets.register("TestIssue5230KB.v1")
def dummy_kb() -> KnowledgeBase:
kb = KnowledgeBase(entity_vector_length=1)
kb.initialize(nlp.vocab)
kb.add_entity("test", 0.0, zeros((1, 1), dtype="f"))
return kb
def dummy_kb() -> Callable[["Vocab"], KnowledgeBase]:
def create_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
kb.add_entity("test", 0.0, zeros((1, 1), dtype="f"))
return kb
config = {"kb": {"@assets": "TestIssue5230KB.v1"}}
return create_kb
config = {"kb_loader": {"@assets": "TestIssue5230KB.v1"}}
entity_linker = nlp.add_pipe("entity_linker", config=config)
# need to add model for two reasons:
# 1. no model leads to error in serialization,
@ -121,19 +124,17 @@ def test_writer_with_path_py35():
def test_save_and_load_knowledge_base():
nlp = Language()
kb = KnowledgeBase(entity_vector_length=1)
kb.initialize(nlp.vocab)
kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
with make_tempdir() as d:
path = d / "kb"
try:
kb.dump(path)
kb.to_disk(path)
except Exception as e:
pytest.fail(str(e))
try:
kb_loaded = KnowledgeBase(entity_vector_length=1)
kb_loaded.initialize(nlp.vocab)
kb_loaded.load_bulk(path)
kb_loaded = KnowledgeBase(nlp.vocab, entity_vector_length=1)
kb_loaded.from_disk(path)
except Exception as e:
pytest.fail(str(e))

12
spacy/tests/serialize/test_serialize_config.py
View File

@ -20,11 +20,11 @@ dev = ""
[training.train_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:train}
path = ${paths.train}
[training.dev_corpus]
@readers = "spacy.Corpus.v1"
path = ${paths:dev}
path = ${paths.dev}
[training.batcher]
@batchers = "batch_by_words.v1"
@ -57,7 +57,7 @@ factory = "tagger"
[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model:width}
width = ${components.tok2vec.model.width}
"""
@ -284,13 +284,13 @@ def test_config_overrides():
def test_config_interpolation():
config = Config().from_str(nlp_config_string, interpolate=False)
assert config["training"]["train_corpus"]["path"] == "${paths:train}"
assert config["training"]["train_corpus"]["path"] == "${paths.train}"
interpolated = config.interpolate()
assert interpolated["training"]["train_corpus"]["path"] == ""
nlp = English.from_config(config)
assert nlp.config["training"]["train_corpus"]["path"] == "${paths:train}"
assert nlp.config["training"]["train_corpus"]["path"] == "${paths.train}"
# Ensure that variables are preserved in nlp config
width = "${components.tok2vec.model:width}"
width = "${components.tok2vec.model.width}"
assert config["components"]["tagger"]["model"]["tok2vec"]["width"] == width
assert nlp.config["components"]["tagger"]["model"]["tok2vec"]["width"] == width
interpolated2 = nlp.config.interpolate()

64
spacy/tests/serialize/test_serialize_kb.py
View File

@ -1,4 +1,8 @@
from spacy.util import ensure_path
from typing import Callable
from spacy import util
from spacy.lang.en import English
from spacy.util import ensure_path, registry
from spacy.kb import KnowledgeBase
from ..util import make_tempdir
@ -15,20 +19,16 @@ def test_serialize_kb_disk(en_vocab):
if not dir_path.exists():
dir_path.mkdir()
file_path = dir_path / "kb"
kb1.dump(str(file_path))
kb2 = KnowledgeBase(entity_vector_length=3)
kb2.initialize(en_vocab)
kb2.load_bulk(str(file_path))
kb1.to_disk(str(file_path))
kb2 = KnowledgeBase(vocab=en_vocab, entity_vector_length=3)
kb2.from_disk(str(file_path))
# final assertions
_check_kb(kb2)
def _get_dummy_kb(vocab):
kb = KnowledgeBase(entity_vector_length=3)
kb.initialize(vocab)
kb = KnowledgeBase(vocab, entity_vector_length=3)
kb.add_entity(entity="Q53", freq=33, entity_vector=[0, 5, 3])
kb.add_entity(entity="Q17", freq=2, entity_vector=[7, 1, 0])
kb.add_entity(entity="Q007", freq=7, entity_vector=[0, 0, 7])
@ -61,7 +61,7 @@ def _check_kb(kb):
assert alias_string not in kb.get_alias_strings()
# check candidates & probabilities
candidates = sorted(kb.get_candidates("double07"), key=lambda x: x.entity_)
candidates = sorted(kb.get_alias_candidates("double07"), key=lambda x: x.entity_)
assert len(candidates) == 2
assert candidates[0].entity_ == "Q007"
@ -75,3 +75,47 @@ def _check_kb(kb):
assert candidates[1].entity_vector == [7, 1, 0]
assert candidates[1].alias_ == "double07"
assert 0.099 < candidates[1].prior_prob < 0.101
def test_serialize_subclassed_kb():
"""Check that IO of a custom KB works fine as part of an EL pipe."""
class SubKnowledgeBase(KnowledgeBase):
def __init__(self, vocab, entity_vector_length, custom_field):
super().__init__(vocab, entity_vector_length)
self.custom_field = custom_field
@registry.assets.register("spacy.CustomKB.v1")
def custom_kb(
entity_vector_length: int, custom_field: int
) -> Callable[["Vocab"], KnowledgeBase]:
def custom_kb_factory(vocab):
return SubKnowledgeBase(
vocab=vocab,
entity_vector_length=entity_vector_length,
custom_field=custom_field,
)
return custom_kb_factory
nlp = English()
config = {
"kb_loader": {
"@assets": "spacy.CustomKB.v1",
"entity_vector_length": 342,
"custom_field": 666,
}
}
entity_linker = nlp.add_pipe("entity_linker", config=config)
assert type(entity_linker.kb) == SubKnowledgeBase
assert entity_linker.kb.entity_vector_length == 342
assert entity_linker.kb.custom_field == 666
# Make sure the custom KB is serialized correctly
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
entity_linker2 = nlp2.get_pipe("entity_linker")
assert type(entity_linker2.kb) == SubKnowledgeBase
assert entity_linker2.kb.entity_vector_length == 342
assert entity_linker2.kb.custom_field == 666

32
spacy/tests/test_cli.py
View File

@ -2,14 +2,16 @@ import pytest
from spacy.gold import docs_to_json, biluo_tags_from_offsets
from spacy.gold.converters import iob2docs, conll_ner2docs, conllu2docs
from spacy.lang.en import English
from spacy.schemas import ProjectConfigSchema, validate
from spacy.schemas import ProjectConfigSchema, RecommendationSchema, validate
from spacy.cli.pretrain import make_docs
from spacy.cli.init_config import init_config, RECOMMENDATIONS_PATH
from spacy.cli.init_config import RecommendationSchema
from spacy.cli.init_config import init_config, RECOMMENDATIONS
from spacy.cli._util import validate_project_commands, parse_config_overrides
from spacy.util import get_lang_class
from spacy.cli._util import load_project_config, substitute_project_variables
from thinc.config import ConfigValidationError
import srsly
from .util import make_tempdir
def test_cli_converters_conllu2json():
# from NorNE: https://github.com/ltgoslo/norne/blob/3d23274965f513f23aa48455b28b1878dad23c05/ud/nob/no_bokmaal-ud-dev.conllu
@ -296,6 +298,24 @@ def test_project_config_validation2(config, n_errors):
assert len(errors) == n_errors
def test_project_config_interpolation():
variables = {"a": 10, "b": {"c": "foo", "d": True}}
commands = [
{"name": "x", "script": ["hello ${vars.a} ${vars.b.c}"]},
{"name": "y", "script": ["${vars.b.c} ${vars.b.d}"]},
]
project = {"commands": commands, "vars": variables}
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d)
assert cfg["commands"][0]["script"][0] == "hello 10 foo"
assert cfg["commands"][1]["script"][0] == "foo true"
commands = [{"name": "x", "script": ["hello ${vars.a} ${vars.b.e}"]}]
project = {"commands": commands, "vars": variables}
with pytest.raises(ConfigValidationError):
substitute_project_variables(project)
@pytest.mark.parametrize(
"args,expected",
[
@ -335,7 +355,5 @@ def test_init_config(lang, pipeline, optimize):
def test_model_recommendations():
recommendations = srsly.read_json(RECOMMENDATIONS_PATH)
for lang, data in recommendations.items():
assert get_lang_class(lang)
for lang, data in RECOMMENDATIONS.items():
assert RecommendationSchema(**data)

16
spacy/tests/test_displacy.py
View File

@ -1,6 +1,6 @@
import pytest
from spacy import displacy
from spacy.displacy.render import DependencyRenderer
from spacy.displacy.render import DependencyRenderer, EntityRenderer
from spacy.tokens import Span
from spacy.lang.fa import Persian
@ -97,3 +97,17 @@ def test_displacy_render_wrapper(en_vocab):
assert html.endswith("/div>TEST")
# Restore
displacy.set_render_wrapper(lambda html: html)
def test_displacy_options_case():
ents = ["foo", "BAR"]
colors = {"FOO": "red", "bar": "green"}
renderer = EntityRenderer({"ents": ents, "colors": colors})
text = "abcd"
labels = ["foo", "bar", "FOO", "BAR"]
spans = [{"start": i, "end": i + 1, "label": labels[i]} for i in range(len(text))]
result = renderer.render_ents("abcde", spans, None).split("\n\n")
assert "red" in result[0] and "foo" in result[0]
assert "green" in result[1] and "bar" in result[1]
assert "red" in result[2] and "FOO" in result[2]
assert "green" in result[3] and "BAR" in result[3]

4
spacy/tokenizer.pyx
View File

@ -47,9 +47,9 @@ cdef class Tokenizer:
`infix_finditer` (callable): A function matching the signature of
`re.compile(string).finditer` to find infixes.
token_match (callable): A boolean function matching strings to be
recognised as tokens.
recognized as tokens.
url_match (callable): A boolean function matching strings to be
recognised as tokens after considering prefixes and suffixes.
recognized as tokens after considering prefixes and suffixes.
EXAMPLE:
>>> tokenizer = Tokenizer(nlp.vocab)

6
spacy/tokens/doc.pyx
View File

@ -102,8 +102,7 @@ cdef class Doc:
Construction 2
>>> from spacy.tokens import Doc
>>> doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'],
>>> spaces=[True, False, False])
>>> doc = Doc(nlp.vocab, words=["hello", "world", "!"], spaces=[True, False, False])
DOCS: https://spacy.io/api/doc
"""
@ -1194,8 +1193,7 @@ cdef class Doc:
retokenizer.merge(span, attributes[i])
def to_json(self, underscore=None):
"""Convert a Doc to JSON. The format it produces will be the new format
for the `spacy train` command (not implemented yet).
"""Convert a Doc to JSON.
underscore (list): Optional list of string names of custom doc._.
attributes. Attribute values need to be JSON-serializable. Values will

99
spacy/util.py
View File

@ -1,5 +1,5 @@
from typing import List, Union, Dict, Any, Optional, Iterable, Callable, Tuple
from typing import Iterator, Type, Pattern, TYPE_CHECKING
from typing import Iterator, Type, Pattern, Generator, TYPE_CHECKING
from types import ModuleType
import os
import importlib
@ -249,7 +249,16 @@ def load_model_from_package(
disable: Iterable[str] = tuple(),
config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
) -> "Language":
"""Load a model from an installed package."""
"""Load a model from an installed package.
name (str): The package name.
vocab (Vocab / True): Optional vocab to pass in on initialization. If True,
a new Vocab object will be created.
disable (Iterable[str]): Names of pipeline components to disable.
config (Dict[str, Any] / Config): Config overrides as nested dict or dict
keyed by section values in dot notation.
RETURNS (Language): The loaded nlp object.
"""
cls = importlib.import_module(name)
return cls.load(vocab=vocab, disable=disable, config=config)
@ -263,7 +272,17 @@ def load_model_from_path(
config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
) -> "Language":
"""Load a model from a data directory path. Creates Language class with
pipeline from config.cfg and then calls from_disk() with path."""
pipeline from config.cfg and then calls from_disk() with path.
name (str): Package name or model path.
meta (Dict[str, Any]): Optional model meta.
vocab (Vocab / True): Optional vocab to pass in on initialization. If True,
a new Vocab object will be created.
disable (Iterable[str]): Names of pipeline components to disable.
config (Dict[str, Any] / Config): Config overrides as nested dict or dict
keyed by section values in dot notation.
RETURNS (Language): The loaded nlp object.
"""
if not model_path.exists():
raise IOError(Errors.E052.format(path=model_path))
if not meta:
@ -284,6 +303,15 @@ def load_model_from_config(
) -> Tuple["Language", Config]:
"""Create an nlp object from a config. Expects the full config file including
a section "nlp" containing the settings for the nlp object.
name (str): Package name or model path.
meta (Dict[str, Any]): Optional model meta.
vocab (Vocab / True): Optional vocab to pass in on initialization. If True,
a new Vocab object will be created.
disable (Iterable[str]): Names of pipeline components to disable.
auto_fill (bool): Whether to auto-fill config with missing defaults.
validate (bool): Whether to show config validation errors.
RETURNS (Language): The loaded nlp object.
"""
if "nlp" not in config:
raise ValueError(Errors.E985.format(config=config))
@ -308,6 +336,13 @@ def load_model_from_init_py(
) -> "Language":
"""Helper function to use in the `load()` method of a model package's
__init__.py.
vocab (Vocab / True): Optional vocab to pass in on initialization. If True,
a new Vocab object will be created.
disable (Iterable[str]): Names of pipeline components to disable.
config (Dict[str, Any] / Config): Config overrides as nested dict or dict
keyed by section values in dot notation.
RETURNS (Language): The loaded nlp object.
"""
model_path = Path(init_file).parent
meta = get_model_meta(model_path)
@ -325,7 +360,14 @@ def load_config(
overrides: Dict[str, Any] = SimpleFrozenDict(),
interpolate: bool = False,
) -> Config:
"""Load a config file. Takes care of path validation and section order."""
"""Load a config file. Takes care of path validation and section order.
path (Union[str, Path]): Path to the config file.
overrides: (Dict[str, Any]): Config overrides as nested dict or
dict keyed by section values in dot notation.
interpolate (bool): Whether to interpolate and resolve variables.
RETURNS (Config): The loaded config.
"""
config_path = ensure_path(path)
if not config_path.exists() or not config_path.is_file():
raise IOError(Errors.E053.format(path=config_path, name="config.cfg"))
@ -337,7 +379,12 @@ def load_config(
def load_config_from_str(
text: str, overrides: Dict[str, Any] = SimpleFrozenDict(), interpolate: bool = False
):
"""Load a full config from a string."""
"""Load a full config from a string. Wrapper around Thinc's Config.from_str.
text (str): The string config to load.
interpolate (bool): Whether to interpolate and resolve variables.
RETURNS (Config): The loaded config.
"""
return Config(section_order=CONFIG_SECTION_ORDER).from_str(
text, overrides=overrides, interpolate=interpolate,
)
@ -435,19 +482,18 @@ def get_base_version(version: str) -> str:
return Version(version).base_version
def get_model_meta(path: Union[str, Path]) -> Dict[str, Any]:
"""Get model meta.json from a directory path and validate its contents.
def load_meta(path: Union[str, Path]) -> Dict[str, Any]:
"""Load a model meta.json from a path and validate its contents.
path (str / Path): Path to model directory.
RETURNS (Dict[str, Any]): The model's meta data.
path (Union[str, Path]): Path to meta.json.
RETURNS (Dict[str, Any]): The loaded meta.
"""
model_path = ensure_path(path)
if not model_path.exists():
raise IOError(Errors.E052.format(path=model_path))
meta_path = model_path / "meta.json"
if not meta_path.is_file():
raise IOError(Errors.E053.format(path=meta_path, name="meta.json"))
meta = srsly.read_json(meta_path)
path = ensure_path(path)
if not path.parent.exists():
raise IOError(Errors.E052.format(path=path.parent))
if not path.exists() or not path.is_file():
raise IOError(Errors.E053.format(path=path, name="meta.json"))
meta = srsly.read_json(path)
for setting in ["lang", "name", "version"]:
if setting not in meta or not meta[setting]:
raise ValueError(Errors.E054.format(setting=setting))
@ -471,6 +517,16 @@ def get_model_meta(path: Union[str, Path]) -> Dict[str, Any]:
return meta
def get_model_meta(path: Union[str, Path]) -> Dict[str, Any]:
"""Get model meta.json from a directory path and validate its contents.
path (str / Path): Path to model directory.
RETURNS (Dict[str, Any]): The model's meta data.
"""
model_path = ensure_path(path)
return load_meta(model_path / "meta.json")
def is_package(name: str) -> bool:
"""Check if string maps to a package installed via pip.
@ -554,7 +610,7 @@ def working_dir(path: Union[str, Path]) -> None:
@contextmanager
def make_tempdir() -> None:
def make_tempdir() -> Generator[Path, None, None]:
"""Execute a block in a temporary directory and remove the directory and
its contents at the end of the with block.
@ -886,6 +942,15 @@ def escape_html(text: str) -> str:
def get_words_and_spaces(
words: Iterable[str], text: str
) -> Tuple[List[str], List[bool]]:
"""Given a list of words and a text, reconstruct the original tokens and
return a list of words and spaces that can be used to create a Doc. This
can help recover destructive tokenization that didn't preserve any
whitespace information.
words (Iterable[str]): The words.
text (str): The original text.
RETURNS (Tuple[List[str], List[bool]]): The words and spaces.
"""
if "".join("".join(words).split()) != "".join(text.split()):
raise ValueError(Errors.E194.format(text=text, words=words))
text_words = []

91
website/README.md
View File

@ -75,7 +75,8 @@ import { H1, H2, H3, H4, H5, Label, InlineList, Comment } from
Headlines are set in
[HK Grotesk](http://cargocollective.com/hanken/HK-Grotesk-Open-Source-Font) by
Hanken Design. All other body text and code uses the best-matching default
system font to provide a "native" reading experience.
system font to provide a "native" reading experience. All code uses the
[JetBrains Mono](https://www.jetbrains.com/lp/mono/) typeface by JetBrains.
<Infobox title="Important note" variant="warning">
@ -106,7 +107,7 @@ Tags are also available as standalone `<Tag />` components.
| Argument | Example | Result |
| -------- | -------------------------- | ----------------------------------------- |
| `tag` | `{tag="method"}` | <Tag>method</Tag> |
| `new` | `{new="2"}` | <Tag variant="new">2</Tag> |
| `new` | `{new="3"}` | <Tag variant="new">3</Tag> |
| `model` | `{model="tagger, parser"}` | <Tag variant="model">tagger, parser</Tag> |
| `hidden` | `{hidden="true"}` | |
@ -130,6 +131,8 @@ Special link styles are used depending on the link URL.
- [I am a regular external link](https://explosion.ai)
- [I am a link to the documentation](/api/doc)
- [I am a link to an architecture](/api/architectures#HashEmbedCNN)
- [I am a link to a model](/models/en#en_core_web_sm)
- [I am a link to GitHub](https://github.com/explosion/spaCy)
### Abbreviations {#abbr}
@ -188,18 +191,20 @@ the buttons are implemented as styled links instead of native button elements.
<InlineList><Button to="#" variant="primary">Primary small</Button>
<Button to="#" variant="secondary">Secondary small</Button></InlineList>
<br />
<InlineList><Button to="#" variant="primary" large>Primary large</Button>
<Button to="#" variant="secondary" large>Secondary large</Button></InlineList>
## Components
### Table
### Table {#table}
> #### Markdown
>
> ```markdown_
> | Header 1 | Header 2 |
> | --- | --- |
> | -------- | -------- |
> | Column 1 | Column 2 |
> ```
>
@ -213,7 +218,7 @@ the buttons are implemented as styled links instead of native button elements.
> ```
Tables are used to present data and API documentation. Certain keywords can be
used to mark a footer row with a distinct style, for example to visualise the
used to mark a footer row with a distinct style, for example to visualize the
return values of a documented function.
| Header 1 | Header 2 | Header 3 | Header 4 |
@ -224,7 +229,73 @@ return values of a documented function.
| Column 1 | Column 2 | Column 3 | Column 4 |
| **RETURNS** | Column 2 | Column 3 | Column 4 |
### List
Tables also support optional "divider" rows that are typically used to denote
keyword-only arguments in API documentation. To turn a row into a dividing
headline, it should only include content in its first cell, and its value should
be italicized:
> #### Markdown
>
> ```markdown_
> | Header 1 | Header 2 | Header 3 |
> | -------- | -------- | -------- |
> | Column 1 | Column 2 | Column 3 |
> | _Hello_ | | |
> | Column 1 | Column 2 | Column 3 |
> ```
| Header 1 | Header 2 | Header 3 |
| -------- | -------- | -------- |
| Column 1 | Column 2 | Column 3 |
| _Hello_ | | |
| Column 1 | Column 2 | Column 3 |
### Type Annotations {#type-annotations}
> #### Markdown
>
> ```markdown_
> ~~Model[List[Doc], Floats2d]~~
> ```
>
> #### JSX
>
> ```markup
> <TypeAnnotation>Model[List[Doc], Floats2d]</Typeannotation>
> ```
Type annotations are special inline code blocks are used to describe Python
types in the [type hints](https://docs.python.org/3/library/typing.html) format.
The special component will split the type, apply syntax highlighting and link
all types that specify links in `meta/type-annotations.json`. Types can link to
internal or external documentation pages. To make it easy to represent the type
annotations in Markdown, the rendering "hijacks" the `~~` tags that would
typically be converted to a `<del>` element – but in this case, text surrounded
by `~~` becomes a type annotation.
- ~~Dict[str, List[Union[Doc, Span]]]~~
- ~~Model[List[Doc], List[numpy.ndarray]]~~
Type annotations support a special visual style in tables and will render as a
separate row, under the cell text. This allows the API docs to display complex
types without taking up too much space in the cell. The type annotation should
always be the **last element** in the row.
> #### Markdown
>
> ```markdown_
> | Header 1 | Header 2 |
> | -------- | ----------------------- |
> | Column 1 | Column 2 ~~List[Doc]~~ |
> ```
| Name | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. ~~Model[List[Doc], FullTransformerBatch]~~ |
| `annotation_setter` | Function that takes a batch of `Doc` objects and transformer outputs can set additional annotations on the `Doc`. ~~Callable[[List[Doc], FullTransformerBatch], None]~~ |
### List {#list}
> #### Markdown
>
@ -255,7 +326,7 @@ automatically.
3. Lorem ipsum dolor
4. consectetur adipiscing elit
### Aside
### Aside {#aside}
> #### Markdown
>
@ -280,7 +351,7 @@ To make them easier to use in Markdown, paragraphs formatted as blockquotes will
turn into asides by default. Level 4 headlines (with a leading `####`) will
become aside titles.
### Code Block
### Code Block {#code-block}
> #### Markdown
>
@ -387,7 +458,7 @@ original file is shown at the top of the widget.
https://github.com/explosion/spaCy/tree/master/spacy/language.py
```
### Infobox
### Infobox {#infobox}
import Infobox from 'components/infobox'
@ -425,7 +496,7 @@ blocks.
</Infobox>
### Accordion
### Accordion {#accordion}
import Accordion from 'components/accordion'

330
website/docs/api/architectures.md
View File

@ -11,9 +11,17 @@ menu:
- ['Entity Linking', 'entitylinker']
---
TODO: intro and how architectures work, link to
[`registry`](/api/top-level#registry),
[custom models](/usage/training#custom-models) usage etc.
A **model architecture** is a function that wires up a
[`Model`](https://thinc.ai/docs/api-model) instance, which you can then use in a
pipeline component or as a layer of a larger network. This page documents
spaCy's built-in architectures that are used for different NLP tasks. All
trainable [built-in components](/api#architecture-pipeline) expect a `model`
argument defined in the config and document their the default architecture.
Custom architectures can be registered using the
[`@spacy.registry.architectures`](/api/top-level#regsitry) decorator and used as
part of the [training config](/usage/training#custom-functions). Also see the
usage documentation on
[layers and model architectures](/usage/layers-architectures).
## Tok2Vec architectures {#tok2vec-arch source="spacy/ml/models/tok2vec.py"}
@ -33,18 +41,19 @@ TODO: intro and how architectures work, link to
> subword_features = true
> ```
Build spaCy's 'standard' tok2vec layer, which uses hash embedding with subword
Build spaCy's "standard" embedding layer, which uses hash embedding with subword
features and a CNN with layer-normalized maxout.
| Name | Type | Description |
| -------------------- | ---- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | int | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. |
| `depth` | int | The number of convolutional layers to use. Recommended values are between `2` and `8`. |
| `embed_size` | int | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. |
| `window_size` | int | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * (window_size * 2 + 1)`, so a 4-layer network with a window size of `2` will be sensitive to 17 words at a time. Recommended value is `1`. |
| `maxout_pieces` | int | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. |
| `subword_features` | bool | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. |
| `pretrained_vectors` | bool | Whether to also use static vectors. |
| Name | Description |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. ~~int~~ |
| `depth` | The number of convolutional layers to use. Recommended values are between `2` and `8`. ~~int~~ |
| `embed_size` | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. ~~int~~ |
| `window_size` | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * (window_size * 2 + 1)`, so a 4-layer network with a window size of `2` will be sensitive to 17 words at a time. Recommended value is `1`. ~~int~~ |
| `maxout_pieces` | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. ~~int~~ |
| `subword_features` | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. ~~bool~~ |
| `pretrained_vectors` | Whether to also use static vectors. ~~bool~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy.Tok2Vec.v1 {#Tok2Vec}
@ -67,10 +76,11 @@ Construct a tok2vec model out of embedding and encoding subnetworks. See the
["Embed, Encode, Attend, Predict"](https://explosion.ai/blog/deep-learning-formula-nlp)
blog post for background.
| Name | Type | Description |
| -------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `embed` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** `List[Doc]`. **Output:** `List[Floats2d]`. Embed tokens into context-independent word vector representations. For example, [CharacterEmbed](/api/architectures#CharacterEmbed) or [MultiHashEmbed](/api/architectures#MultiHashEmbed) |
| `encode` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** `List[Floats2d]`. **Output:** `List[Floats2d]`. Encode context into the embeddings, using an architecture such as a CNN, BiLSTM or transformer. For example, [MaxoutWindowEncoder](/api/architectures#MaxoutWindowEncoder). |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `embed` | Embed tokens into context-independent word vector representations. For example, [CharacterEmbed](/api/architectures#CharacterEmbed) or [MultiHashEmbed](/api/architectures#MultiHashEmbed). ~~Model[List[Doc], List[Floats2d]]~~ |
| `encode` | Encode context into the embeddings, using an architecture such as a CNN, BiLSTM or transformer. For example, [MaxoutWindowEncoder](/api/architectures#MaxoutWindowEncoder). ~~Model[List[Floats2d], List[Floats2d]]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy.Tok2VecListener.v1 {#Tok2VecListener}
@ -92,7 +102,7 @@ blog post for background.
>
> [components.tagger.model.tok2vec]
> @architectures = "spacy.Tok2VecListener.v1"
> width = ${components.tok2vec.model:width}
> width = ${components.tok2vec.model.width}
> ```
A listener is used as a sublayer within a component such as a
@ -108,10 +118,11 @@ Instead of defining its own `Tok2Vec` instance, a model architecture like
[Tagger](/api/architectures#tagger) can define a listener as its `tok2vec`
argument that connects to the shared `tok2vec` component in the pipeline.
| Name | Type | Description |
| ---------- | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `width` | int | The width of the vectors produced by the "upstream" [`Tok2Vec`](/api/tok2vec) component. |
| `upstream` | str | A string to identify the "upstream" `Tok2Vec` component to communicate with. The upstream name should either be the wildcard string `"*"`, or the name of the `Tok2Vec` component. You'll almost never have multiple upstream `Tok2Vec` components, so the wildcard string will almost always be fine. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The width of the vectors produced by the "upstream" [`Tok2Vec`](/api/tok2vec) component. ~~int~~ |
| `upstream` | A string to identify the "upstream" `Tok2Vec` component to communicate with. The upstream name should either be the wildcard string `"*"`, or the name of the `Tok2Vec` component. You'll almost never have multiple upstream `Tok2Vec` components, so the wildcard string will almost always be fine. ~~str~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy.MultiHashEmbed.v1 {#MultiHashEmbed}
@ -134,12 +145,13 @@ definitions depending on the `Vocab` of the `Doc` object passed in. Vectors from
pretrained static vectors can also be incorporated into the concatenated
representation.
| Name | Type | Description |
| ------------------------- | ---- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | int | The output width. Also used as the width of the embedding tables. Recommended values are between `64` and `300`. |
| `rows` | int | The number of rows for the embedding tables. Can be low, due to the hashing trick. Embeddings for prefix, suffix and word shape use half as many rows. Recommended values are between `2000` and `10000`. |
| `also_embed_subwords` | bool | Whether to use the `PREFIX`, `SUFFIX` and `SHAPE` features in the embeddings. If not using these, you may need more rows in your hash embeddings, as there will be increased chance of collisions. |
| `also_use_static_vectors` | bool | Whether to also use static word vectors. Requires a vectors table to be loaded in the [Doc](/api/doc) objects' vocab. |
| Name | Description |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The output width. Also used as the width of the embedding tables. Recommended values are between `64` and `300`. ~~int~~ |
| `rows` | The number of rows for the embedding tables. Can be low, due to the hashing trick. Embeddings for prefix, suffix and word shape use half as many rows. Recommended values are between `2000` and `10000`. ~~int~~ |
| `also_embed_subwords` | Whether to use the `PREFIX`, `SUFFIX` and `SHAPE` features in the embeddings. If not using these, you may need more rows in your hash embeddings, as there will be increased chance of collisions. ~~bool~~ |
| `also_use_static_vectors` | Whether to also use static word vectors. Requires a vectors table to be loaded in the [Doc](/api/doc) objects' vocab. ~~bool~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy.CharacterEmbed.v1 {#CharacterEmbed}
@ -170,12 +182,13 @@ concatenated. A hash-embedded vector of the `NORM` of the word is also
concatenated on, and the result is then passed through a feed-forward network to
construct a single vector to represent the information.
| Name | Type | Description |
| ------- | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | int | The width of the output vector and the `NORM` hash embedding. |
| `rows` | int | The number of rows in the `NORM` hash embedding table. |
| `nM` | int | The dimensionality of the character embeddings. Recommended values are between `16` and `64`. |
| `nC` | int | The number of UTF-8 bytes to embed per word. Recommended values are between `3` and `8`, although it may depend on the length of words in the language. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The width of the output vector and the `NORM` hash embedding. ~~int~~ |
| `rows` | The number of rows in the `NORM` hash embedding table. ~~int~~ |
| `nM` | The dimensionality of the character embeddings. Recommended values are between `16` and `64`. ~~int~~ |
| `nC` | The number of UTF-8 bytes to embed per word. Recommended values are between `3` and `8`, although it may depend on the length of words in the language. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy.MaxoutWindowEncoder.v1 {#MaxoutWindowEncoder}
@ -193,12 +206,13 @@ construct a single vector to represent the information.
Encode context using convolutions with maxout activation, layer normalization
and residual connections.
| Name | Type | Description |
| --------------- | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `width` | int | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. |
| `window_size` | int | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. |
| `maxout_pieces` | int | The number of maxout pieces to use. Recommended values are `2` or `3`. |
| `depth` | int | The number of convolutional layers. Recommended value is `4`. |
| Name | Description |
| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. ~~int~~ |
| `window_size` | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. ~~int~~ |
| `maxout_pieces` | The number of maxout pieces to use. Recommended values are `2` or `3`. ~~int~~ |
| `depth` | The number of convolutional layers. Recommended value is `4`. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Floats2d], List[Floats2d]]~~ |
### spacy.MishWindowEncoder.v1 {#MishWindowEncoder}
@ -216,11 +230,12 @@ Encode context using convolutions with
[`Mish`](https://thinc.ai/docs/api-layers#mish) activation, layer normalization
and residual connections.
| Name | Type | Description |
| ------------- | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `width` | int | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. |
| `window_size` | int | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. |
| `depth` | int | The number of convolutional layers. Recommended value is `4`. |
| Name | Description |
| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. ~~int~~ |
| `window_size` | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. ~~int~~ |
| `depth` | The number of convolutional layers. Recommended value is `4`. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Floats2d], List[Floats2d]]~~ |
### spacy.TorchBiLSTMEncoder.v1 {#TorchBiLSTMEncoder}
@ -237,18 +252,58 @@ and residual connections.
Encode context using bidirectional LSTM layers. Requires
[PyTorch](https://pytorch.org).
| Name | Type | Description |
| ------------- | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `width` | int | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. |
| `window_size` | int | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. |
| `depth` | int | The number of convolutional layers. Recommended value is `4`. |
| Name | Description |
| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. ~~int~~ |
| `window_size` | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. ~~int~~ |
| `depth` | The number of convolutional layers. Recommended value is `4`. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Floats2d], List[Floats2d]]~~ |
### spacy.StaticVectors.v1 {#StaticVectors}
> #### Example config
>
> ```ini
> [model]
> @architectures = "spacy.StaticVectors.v1"
> nO = null
> nM = null
> dropout = 0.2
> key_attr = "ORTH"
>
> [model.init_W]
> @initializers = "glorot_uniform_init.v1"
> ```
Embed [`Doc`](/api/doc) objects with their vocab's vectors table, applying a
learned linear projection to control the dimensionality. See the documentation
on [static vectors](/usage/embeddings-transformers#static-vectors) for details.
| Name |  Description |
| ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nO` | The output width of the layer, after the linear projection. ~~Optional[int]~~ |
| `nM` | The width of the static vectors. ~~Optional[int]~~ |
| `dropout` | Optional dropout rate. If set, it's applied per dimension over the whole batch. Defaults to `None`. ~~Optional[float]~~ |
| `init_W` | The [initialization function](https://thinc.ai/docs/api-initializers). Defaults to [`glorot_uniform_init`](https://thinc.ai/docs/api-initializers#glorot_uniform_init). ~~Callable[[Ops, Tuple[int, ...]]], FloatsXd]~~ |
| `key_attr` | Defaults to `"ORTH"`. ~~str~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Ragged]~~ |
## Transformer architectures {#transformers source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/architectures.py"}
The following architectures are provided by the package
[`spacy-transformers`](https://github.com/explosion/spacy-transformers). See the
[usage documentation](/usage/transformers) for how to integrate the
architectures into your training config.
[usage documentation](/usage/embeddings-transformers#transformers) for how to
integrate the architectures into your training config.
<Infobox variant="warning">
Note that in order to use these architectures in your config, you need to
install the
[`spacy-transformers`](https://github.com/explosion/spacy-transformers). See the
[installation docs](/usage/embeddings-transformers#transformers-installation)
for details and system requirements.
</Infobox>
### spacy-transformers.TransformerModel.v1 {#TransformerModel}
@ -266,13 +321,30 @@ architectures into your training config.
> stride = 96
> ```
<!-- TODO: description -->
Load and wrap a transformer model from the
[HuggingFace `transformers`](https://huggingface.co/transformers) library. You
can any transformer that has pretrained weights and a PyTorch implementation.
The `name` variable is passed through to the underlying library, so it can be
either a string or a path. If it's a string, the pretrained weights will be
downloaded via the transformers library if they are not already available
locally.
| Name | Type | Description |
| ------------------ | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | Any model name that can be loaded by [`transformers.AutoModel`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoModel). |
| `get_spans` | `Callable` | Function that takes a batch of [`Doc`](/api/doc) object and returns lists of [`Span`](/api) objects to process by the transformer. [See here](/api/transformer#span_getters) for built-in options and examples. |
| `tokenizer_config` | `Dict[str, Any]` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). |
In order to support longer documents, the
[TransformerModel](/api/architectures#TransformerModel) layer allows you to pass
in a `get_spans` function that will divide up the [`Doc`](/api/doc) objects
before passing them through the transformer. Your spans are allowed to overlap
or exclude tokens. This layer is usually used directly by the
[`Transformer`](/api/transformer) component, which allows you to share the
transformer weights across your pipeline. For a layer that's configured for use
in other components, see
[Tok2VecTransformer](/api/architectures#Tok2VecTransformer).
| Name | Description |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Any model name that can be loaded by [`transformers.AutoModel`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoModel). ~~str~~ |
| `get_spans` | Function that takes a batch of [`Doc`](/api/doc) object and returns lists of [`Span`](/api) objects to process by the transformer. [See here](/api/transformer#span_getters) for built-in options and examples. ~~Callable[[List[Doc]], List[Span]]~~ |
| `tokenizer_config` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). ~~Dict[str, Any]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], FullTransformerBatch]~~ |
### spacy-transformers.Tok2VecListener.v1 {#transformers-Tok2VecListener}
@ -297,10 +369,11 @@ operate over wordpieces, which usually don't align one-to-one against spaCy
tokens. The layer therefore requires a reduction operation in order to calculate
a single token vector given zero or more wordpiece vectors.
| Name | Type | Description |
| ------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `pooling` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** [`Ragged`](https://thinc.ai/docs/api-types#ragged). **Output:** [`Floats2d`](https://thinc.ai/docs/api-types#types) | A reduction layer used to calculate the token vectors based on zero or more wordpiece vectors. If in doubt, mean pooling (see [`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean)) is usually a good choice. |
| `grad_factor` | float | Reweight gradients from the component before passing them upstream. You can set this to `0` to "freeze" the transformer weights with respect to the component, or use it to make some components more significant than others. Leaving it at `1.0` is usually fine. |
| Name | Description |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `pooling` | A reduction layer used to calculate the token vectors based on zero or more wordpiece vectors. If in doubt, mean pooling (see [`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean)) is usually a good choice. ~~Model[Ragged, Floats2d]~~ |
| `grad_factor` | Reweight gradients from the component before passing them upstream. You can set this to `0` to "freeze" the transformer weights with respect to the component, or use it to make some components more significant than others. Leaving it at `1.0` is usually fine. ~~float~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy-transformers.Tok2VecTransformer.v1 {#Tok2VecTransformer}
@ -320,12 +393,13 @@ Use a transformer as a [`Tok2Vec`](/api/tok2vec) layer directly. This does
object, but it's a **simpler solution** if you only need the transformer within
one component.
| Name | Type | Description |
| ------------------ | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_spans` | callable | Function that takes a batch of [`Doc`](/api/doc) object and returns lists of [`Span`](/api) objects to process by the transformer. [See here](/api/transformer#span_getters) for built-in options and examples. |
| `tokenizer_config` | `Dict[str, Any]` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). |
| `pooling` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** [`Ragged`](https://thinc.ai/docs/api-types#ragged). **Output:** [`Floats2d`](https://thinc.ai/docs/api-types#types) | A reduction layer used to calculate the token vectors based on zero or more wordpiece vectors. If in doubt, mean pooling (see [`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean)) is usually a good choice. |
| `grad_factor` | float | Reweight gradients from the component before passing them upstream. You can set this to `0` to "freeze" the transformer weights with respect to the component, or use it to make some components more significant than others. Leaving it at `1.0` is usually fine. |
| Name | Description |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_spans` | Function that takes a batch of [`Doc`](/api/doc) object and returns lists of [`Span`](/api) objects to process by the transformer. [See here](/api/transformer#span_getters) for built-in options and examples. ~~Callable[[List[Doc]], List[Span]]~~ |
| `tokenizer_config` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). ~~Dict[str, Any]~~ |
| `pooling` | A reduction layer used to calculate the token vectors based on zero or more wordpiece vectors. If in doubt, mean pooling (see [`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean)) is usually a good choice. ~~Model[Ragged, Floats2d]~~ |
| `grad_factor` | Reweight gradients from the component before passing them upstream. You can set this to `0` to "freeze" the transformer weights with respect to the component, or use it to make some components more significant than others. Leaving it at `1.0` is usually fine. ~~float~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
## Parser & NER architectures {#parser}
@ -351,7 +425,7 @@ one component.
> subword_features = true
> ```
Build a transition-based parser model. Can apply to NER or dependency-parsing.
Build a transition-based parser model. Can apply to NER or dependency parsing.
Transition-based parsing is an approach to structured prediction where the task
of predicting the structure is mapped to a series of state transitions. You
might find [this tutorial](https://explosion.ai/blog/parsing-english-in-python)
@ -368,14 +442,15 @@ consists of either two or three subnetworks:
state representation. If not present, the output from the lower model is used
as action scores directly.
| Name | Type | Description |
| ------------------- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** `List[Doc]`. **Output:** `List[Floats2d]`. Subnetwork to map tokens into vector representations. |
| `nr_feature_tokens` | int | The number of tokens in the context to use to construct the state vector. Valid choices are `1`, `2`, `3`, `6`, `8` and `13`. The `2`, `8` and `13` feature sets are designed for the parser, while the `3` and `6` feature sets are designed for the entity recognizer. The recommended feature sets are `3` for NER, and `8` for the dependency parser. |
| `hidden_width` | int | The width of the hidden layer. |
| `maxout_pieces` | int | How many pieces to use in the state prediction layer. Recommended values are `1`, `2` or `3`. If `1`, the maxout non-linearity is replaced with a [`Relu`](https://thinc.ai/docs/api-layers#relu) non-linearity if `use_upper` is `True`, and no non-linearity if `False`. |
| `use_upper` | bool | Whether to use an additional hidden layer after the state vector in order to predict the action scores. It is recommended to set this to `False` for large pretrained models such as transformers, and `True` for smaller networks. The upper layer is computed on CPU, which becomes a bottleneck on larger GPU-based models, where it's also less necessary. |
| `nO` | int | The number of actions the model will predict between. Usually inferred from data at the beginning of training, or loaded from disk. |
| Name | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tok2vec` | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
| `nr_feature_tokens` | The number of tokens in the context to use to construct the state vector. Valid choices are `1`, `2`, `3`, `6`, `8` and `13`. The `2`, `8` and `13` feature sets are designed for the parser, while the `3` and `6` feature sets are designed for the entity recognizer. The recommended feature sets are `3` for NER, and `8` for the dependency parser. ~~int~~ |
| `hidden_width` | The width of the hidden layer. ~~int~~ |
| `maxout_pieces` | How many pieces to use in the state prediction layer. Recommended values are `1`, `2` or `3`. If `1`, the maxout non-linearity is replaced with a [`Relu`](https://thinc.ai/docs/api-layers#relu) non-linearity if `use_upper` is `True`, and no non-linearity if `False`. ~~int~~ |
| `use_upper` | Whether to use an additional hidden layer after the state vector in order to predict the action scores. It is recommended to set this to `False` for large pretrained models such as transformers, and `True` for smaller networks. The upper layer is computed on CPU, which becomes a bottleneck on larger GPU-based models, where it's also less necessary. ~~bool~~ |
| `nO` | The number of actions the model will predict between. Usually inferred from data at the beginning of training, or loaded from disk. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Docs], List[List[Floats2d]]]~~ |
### spacy.BILUOTagger.v1 {#BILUOTagger source="spacy/ml/models/simple_ner.py"}
@ -402,9 +477,10 @@ generally results in better linear separation between classes, especially for
non-CRF models, because there are more distinct classes for the different
situations ([Ratinov et al., 2009](https://www.aclweb.org/anthology/W09-1119/)).
| Name | Type | Description |
| --------- | ------------------------------------------ | ----------------------------------------------------------------------------------------------------------- |
| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** `List[Doc]`. **Output:** `List[Floats2d]`. Subnetwork to map tokens into vector representations. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------ |
| `tok2vec` | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy.IOBTagger.v1 {#IOBTagger source="spacy/ml/models/simple_ner.py"}
@ -427,9 +503,10 @@ spans into tags assigned to each token. The first token of a span is given the
tag B-LABEL, and subsequent tokens are given the tag I-LABEL. All other tokens
are assigned the tag O.
| Name | Type | Description |
| --------- | ------------------------------------------ | ----------------------------------------------------------------------------------------------------------- |
| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** `List[Doc]`. **Output:** `List[Floats2d]`. Subnetwork to map tokens into vector representations. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------ |
| `tok2vec` | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
## Tagging architectures {#tagger source="spacy/ml/models/tagger.py"}
@ -450,10 +527,11 @@ Build a tagger model, using a provided token-to-vector component. The tagger
model simply adds a linear layer with softmax activation to predict scores given
the token vectors.
| Name | Type | Description |
| --------- | ------------------------------------------ | ----------------------------------------------------------------------------------------------------------- |
| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** `List[Doc]`. **Output:** `List[Floats2d]`. Subnetwork to map tokens into vector representations. |
| `nO` | int | The number of tags to output. Inferred from the data if `None`. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------ |
| `tok2vec` | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
| `nO` | The number of tags to output. Inferred from the data if `None`. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
## Text classification architectures {#textcat source="spacy/ml/models/textcat.py"}
@ -470,9 +548,6 @@ specific data and challenge.
### spacy.TextCatEnsemble.v1 {#TextCatEnsemble}
Stacked ensemble of a bag-of-words model and a neural network model. The neural
network has an internal CNN Tok2Vec layer and uses attention.
> #### Example Config
>
> ```ini
@ -489,18 +564,21 @@ network has an internal CNN Tok2Vec layer and uses attention.
> nO = null
> ```
| Name | Type | Description |
| --------------------------- | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `pretrained_vectors` | bool | Whether or not pretrained vectors will be used in addition to the feature vectors. |
| `width` | int | Output dimension of the feature encoding step. |
| `embed_size` | int | Input dimension of the feature encoding step. |
| `conv_depth` | int | Depth of the Tok2Vec layer. |
| `window_size` | int | The number of contextual vectors to [concatenate](https://thinc.ai/docs/api-layers#expand_window) from the left and from the right. |
| `ngram_size` | int | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. |
| `dropout` | float | The dropout rate. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when |
| `begin_training` is called. |
Stacked ensemble of a bag-of-words model and a neural network model. The neural
network has an internal CNN Tok2Vec layer and uses attention.
| Name | Description |
| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `pretrained_vectors` | Whether or not pretrained vectors will be used in addition to the feature vectors. ~~bool~~ |
| `width` | Output dimension of the feature encoding step. ~~int~~ |
| `embed_size` | Input dimension of the feature encoding step. ~~int~~ |
| `conv_depth` | Depth of the tok2vec layer. ~~int~~ |
| `window_size` | The number of contextual vectors to [concatenate](https://thinc.ai/docs/api-layers#expand_window) from the left and from the right. ~~int~~ |
| `ngram_size` | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. ~~int~~ |
| `dropout` | The dropout rate. ~~float~~ |
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.TextCatCNN.v1 {#TextCatCNN}
@ -527,11 +605,12 @@ A neural network model where token vectors are calculated using a CNN. The
vectors are mean pooled and used as features in a feed-forward network. This
architecture is usually less accurate than the ensemble, but runs faster.
| Name | Type | Description |
| ------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | The [`tok2vec`](#tok2vec) layer of the model. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. |
| Name | Description |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.TextCatBOW.v1 {#TextCatBOW}
@ -549,12 +628,13 @@ architecture is usually less accurate than the ensemble, but runs faster.
An ngram "bag-of-words" model. This architecture should run much faster than the
others, but may not be as accurate, especially if texts are short.
| Name | Type | Description |
| ------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `ngram_size` | int | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. |
| `no_output_layer` | float | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes=True`, else `Logistic`. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. |
| Name | Description |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `ngram_size` | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. ~~int~~ |
| `no_output_layer` | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes` is `True`, else `Logistic`. ~~bool~~ |
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
## Entity linking architectures {#entitylinker source="spacy/ml/models/entity_linker.py"}
@ -571,9 +651,6 @@ into the "real world". This requires 3 main components:
### spacy.EntityLinker.v1 {#EntityLinker}
The `EntityLinker` model architecture is a `Thinc` `Model` with a Linear output
layer.
> #### Example Config
>
> ```ini
@ -599,27 +676,28 @@ layer.
> @assets = "spacy.CandidateGenerator.v1"
> ```
| Name | Type | Description |
| --------- | ------------------------------------------ | ---------------------------------------------------------------------------------------- |
| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | The [`tok2vec`](#tok2vec) layer of the model. |
| `nO` | int | Output dimension, determined by the length of the vectors encoding each entity in the KB |
The `EntityLinker` model architecture is a Thinc `Model` with a
[`Linear`](https://thinc.ai/api-layers#linear) output layer.
If the `nO` dimension is not set, the Entity Linking component will set it when
`begin_training` is called.
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
| `nO` | Output dimension, determined by the length of the vectors encoding each entity in the KB. If the `nO` dimension is not set, the entity linking component will set it when `begin_training` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.EmptyKB.v1 {#EmptyKB}
A function that creates a default, empty `KnowledgeBase` from a
[`Vocab`](/api/vocab) instance.
| Name | Type | Description |
| ---------------------- | ---- | ------------------------------------------------------------------------- |
| `entity_vector_length` | int | The length of the vectors encoding each entity in the KB - 64 by default. |
| Name | Description |
| ---------------------- | ----------------------------------------------------------------------------------- |
| `entity_vector_length` | The length of the vectors encoding each entity in the KB. Defaults to `64`. ~~int~~ |
### spacy.CandidateGenerator.v1 {#CandidateGenerator}
A function that takes as input a [`KnowledgeBase`](/api/kb) and a
[`Span`](/api/span) object denoting a named entity, and returns a list of
plausible [`Candidate` objects](/api/kb/#candidate_init). The default
plausible [`Candidate`](/api/kb/#candidate) objects. The default
`CandidateGenerator` simply uses the text of a mention to find its potential
aliases in the `KnowledgeBase`. Note that this function is case-dependent.

128
website/docs/api/attributeruler.md
View File

@ -31,10 +31,10 @@ how the component should be configured. You can override its settings via the
> nlp.add_pipe("attribute_ruler", config=config)
> ```
| Setting | Type | Description | Default |
| --------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| `pattern_dicts` | `Iterable[dict]` | A list of pattern dicts with the keys as the arguments to [`AttributeRuler.add`](#add) (`patterns`/`attrs`/`index`) to add as patterns. | `None` |
| `validate` | bool | Whether patterns should be validated (passed to the `Matcher`). | `False` |
| Setting | Description |
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `pattern_dicts` | A list of pattern dicts with the keys as the arguments to [`AttributeRuler.add`](/api/attributeruler#add) (`patterns`/`attrs`/`index`) to add as patterns. Defaults to `None`. ~~Optional[Iterable[Dict[str, Union[List[dict], dict, int]]]]~~ |
| `validate` | Whether patterns should be validated (passed to the `Matcher`). Defaults to `False`. ~~bool~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/attributeruler.py
@ -47,10 +47,10 @@ be a list of dictionaries with `"patterns"`, `"attrs"`, and optional `"index"`
keys, e.g.:
```python
pattern_dicts = \[
{"patterns": \[\[{"TAG": "VB"}\]\], "attrs": {"POS": "VERB"}},
{"patterns": \[\[{"LOWER": "an"}\]\], "attrs": {"LEMMA": "a"}},
\]
pattern_dicts = [
{"patterns": [[{"TAG": "VB"}]], "attrs": {"POS": "VERB"}},
{"patterns": [[{"LOWER": "an"}]], "attrs": {"LEMMA": "a"}},
]
```
> #### Example
@ -60,23 +60,23 @@ pattern_dicts = \[
> attribute_ruler = nlp.add_pipe("attribute_ruler")
> ```
| Name | Type | Description |
| --------------- | ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared nlp object to pass the vocab to the matchers and process phrase patterns. |
| `name` | str | Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current entity ruler while creating phrase patterns with the nlp object. |
| _keyword-only_ | | |
| `pattern_dicts` | `Iterable[Dict]]` | Optional patterns to load in on initialization. Defaults to `None`. |
| `validate` | bool | Whether patterns should be validated (passed to the `Matcher`). Defaults to `False`. |
| Name | Description |
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary to pass to the matcher. ~~Vocab~~ |
| `name` | Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. ~~str~~ |
| _keyword-only_ | |
| `pattern_dicts` | Optional patterns to load in on initialization. Defaults to `None`. ~~Optional[Iterable[Dict[str, Union[List[dict], dict, int]]]]~~ |
| `validate` | Whether patterns should be validated (passed to the [`Matcher`](/api/matcher#init)). Defaults to `False`. ~~bool~~ |
## AttributeRuler.\_\_call\_\_ {#call tag="method"}
Apply the attribute ruler to a Doc, setting token attributes for tokens matched
by the provided patterns.
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------ |
| `doc` | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
| **RETURNS** | `Doc` | The modified `Doc` with added entities, if available. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## AttributeRuler.add {#add tag="method"}
@ -95,11 +95,11 @@ may be negative to index from the end of the span.
> attribute_ruler.add(patterns=patterns, attrs=attrs)
> ```
| Name | Type | Description |
| -------- | ---------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| patterns | `Iterable[List[Dict]]` | A list of Matcher patterns. |
| attrs | dict | The attributes to assign to the target token in the matched span. |
| index | int | The index of the token in the matched span to modify. May be negative to index from the end of the span. Defaults to 0. |
| Name | Description |
| ---------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `patterns` | The `Matcher` patterns to add. ~~Iterable[List[Dict[Union[int, str], Any]]]~~ |
| `attrs` | The attributes to assign to the target token in the matched span. ~~Dict[str, Any]~~ |
| `index` | The index of the token in the matched span to modify. May be negative to index from the end of the span. Defaults to `0`. ~~int~~ |
## AttributeRuler.add_patterns {#add_patterns tag="method"}
@ -107,52 +107,52 @@ may be negative to index from the end of the span.
>
> ```python
> attribute_ruler = nlp.add_pipe("attribute_ruler")
> pattern_dicts = \[
> pattern_dicts = [
> {
> "patterns": \[\[{"TAG": "VB"}\]\],
> "patterns": [[{"TAG": "VB"}]],
> "attrs": {"POS": "VERB"}
> },
> {
> "patterns": \[\[{"LOWER": "two"}, {"LOWER": "apples"}\]\],
> "patterns": [[{"LOWER": "two"}, {"LOWER": "apples"}]],
> "attrs": {"LEMMA": "apple"},
> "index": -1
> },
> \]
> ]
> attribute_ruler.add_patterns(pattern_dicts)
> ```
Add patterns from a list of pattern dicts with the keys as the arguments to
[`AttributeRuler.add`](#add).
[`AttributeRuler.add`](/api/attributeruler#add).
| Name | Type | Description |
| --------------- | ----------------- | -------------------- |
| `pattern_dicts` | `Iterable[Dict]]` | The patterns to add. |
| Name | Description |
| --------------- | -------------------------------------------------------------------------- |
| `pattern_dicts` | The patterns to add. ~~Iterable[Dict[str, Union[List[dict], dict, int]]]~~ |
## AttributeRuler.patterns {#patterns tag="property"}
Get all patterns that have been added to the attribute ruler in the
`patterns_dict` format accepted by
[`AttributeRuler.add_patterns`](#add_patterns).
[`AttributeRuler.add_patterns`](/api/attributeruler#add_patterns).
| Name | Type | Description |
| ----------- | ------------ | ------------------------------------------ |
| **RETURNS** | `List[dict]` | The patterns added to the attribute ruler. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------- |
| **RETURNS** | The patterns added to the attribute ruler. ~~List[Dict[str, Union[List[dict], dict, int]]]~~ |
## AttributeRuler.load_from_tag_map {#load_from_tag_map tag="method"}
Load attribute ruler patterns from a tag map.
| Name | Type | Description |
| --------- | ---- | ------------------------------------------------------------------------------------------ |
| `tag_map` | dict | The tag map that maps fine-grained tags to coarse-grained tags and morphological features. |
| Name | Description |
| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `tag_map` | The tag map that maps fine-grained tags to coarse-grained tags and morphological features. ~~Dict[str, Dict[Union[int, str], Union[int, str]]]~~ |
## AttributeRuler.load_from_morph_rules {#load_from_morph_rules tag="method"}
Load attribute ruler patterns from morph rules.
| Name | Type | Description |
| ------------- | ---- | -------------------------------------------------------------------------------------------------------------------- |
| `morph_rules` | dict | The morph rules that map token text and fine-grained tags to coarse-grained tags, lemmas and morphological features. |
| Name | Description |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `morph_rules` | The morph rules that map token text and fine-grained tags to coarse-grained tags, lemmas and morphological features. ~~Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]~~ |
## AttributeRuler.to_disk {#to_disk tag="method"}
@ -165,11 +165,11 @@ Serialize the pipe to disk.
> attribute_ruler.to_disk("/path/to/attribute_ruler")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## AttributeRuler.from_disk {#from_disk tag="method"}
@ -182,12 +182,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> attribute_ruler.from_disk("/path/to/attribute_ruler")
> ```
| Name | Type | Description |
| -------------- | ---------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `AttributeRuler` | The modified `AttributeRuler` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `AttributeRuler` object. ~~AttributeRuler~~ |
## AttributeRuler.to_bytes {#to_bytes tag="method"}
@ -200,11 +200,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `AttributeRuler` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `AttributeRuler` object. ~~bytes~~ |
## AttributeRuler.from_bytes {#from_bytes tag="method"}
@ -218,12 +218,12 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> attribute_ruler.from_bytes(attribute_ruler_bytes)
> ```
| Name | Type | Description |
| -------------- | ---------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `AttributeRuler` | The `AttributeRuler` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `AttributeRuler` object. ~~AttributeRuler~~ |
## Serialization fields {#serialization-fields}

631
website/docs/api/cli.md
View File

@ -3,17 +3,17 @@ title: Command Line Interface
teaser: Download, train and package models, and debug spaCy
source: spacy/cli
menu:
- ['Download', 'download']
- ['Info', 'info']
- ['Validate', 'validate']
- ['Init', 'init']
- ['Convert', 'convert']
- ['Debug', 'debug']
- ['Train', 'train']
- ['Pretrain', 'pretrain']
- ['Evaluate', 'evaluate']
- ['Package', 'package']
- ['Project', 'project']
- ['download', 'download']
- ['info', 'info']
- ['validate', 'validate']
- ['init', 'init']
- ['convert', 'convert']
- ['debug', 'debug']
- ['train', 'train']
- ['pretrain', 'pretrain']
- ['evaluate', 'evaluate']
- ['package', 'package']
- ['project', 'project']
---
spaCy's CLI provides a range of helpful commands for downloading and training
@ -22,7 +22,7 @@ list of available commands, you can type `python -m spacy --help`. You can also
add the `--help` flag to any command or subcommand to see the description,
available arguments and usage.
## Download {#download}
## download {#download tag="command"}
Download [models](/usage/models) for spaCy. The downloader finds the
best-matching compatible version and uses `pip install` to download the model as
@ -39,41 +39,41 @@ the model name to be specified with its version (e.g. `en_core_web_sm-2.2.0`).
> to a local PyPi installation and fetching it straight from there. This will
> also allow you to add it as a versioned package dependency to your project.
```bash
$ python -m spacy download [model] [--direct] [pip args]
```cli
$ python -m spacy download [model] [--direct] [pip_args]
```
| Argument | Type | Description |
| ------------------------------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | positional | Model name, e.g. [`en_core_web_sm`](/models/en#en_core_web_sm). |
| `--direct`, `-d` | flag | Force direct download of exact model version. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| pip args <Tag variant="new">2.1</Tag> | option / flag | Additional installation options to be passed to `pip install` when installing the model package. For example, `--user` to install to the user home directory or `--no-deps` to not install model dependencies. |
| **CREATES** | directory | The installed model package in your `site-packages` directory. |
| Name | Description |
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `model` | Model name, e.g. [`en_core_web_sm`](/models/en#en_core_web_sm). ~~str (positional)~~ |
| `--direct`, `-d` | Force direct download of exact model version. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| pip args <Tag variant="new">2.1</Tag> | Additional installation options to be passed to `pip install` when installing the model package. For example, `--user` to install to the user home directory or `--no-deps` to not install model dependencies. ~~Any (option/flag)~~ |
| **CREATES** | The installed model package in your `site-packages` directory. |
## Info {#info}
## info {#info tag="command"}
Print information about your spaCy installation, models and local setup, and
generate [Markdown](https://en.wikipedia.org/wiki/Markdown)-formatted markup to
copy-paste into [GitHub issues](https://github.com/explosion/spaCy/issues).
```bash
```cli
$ python -m spacy info [--markdown] [--silent]
```
```bash
```cli
$ python -m spacy info [model] [--markdown] [--silent]
```
| Argument | Type | Description |
| ------------------------------------------------ | ---------- | ---------------------------------------------- |
| `model` | positional | A model, i.e. package name or path (optional). |
| `--markdown`, `-md` | flag | Print information as Markdown. |
| `--silent`, `-s` <Tag variant="new">2.0.12</Tag> | flag | Don't print anything, just return the values. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **PRINTS** | `stdout` | Information about your spaCy installation. |
| Name | Description |
| ------------------------------------------------ | ------------------------------------------------------------------------------ |
| `model` | A model, i.e. package name or path (optional). ~~Optional[str] \(positional)~~ |
| `--markdown`, `-md` | Print information as Markdown. ~~bool (flag)~~ |
| `--silent`, `-s` <Tag variant="new">2.0.12</Tag> | Don't print anything, just return the values. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **PRINTS** | Information about your spaCy installation. |
## Validate {#validate new="2"}
## validate {#validate new="2" tag="command"}
Find all models installed in the current environment and check whether they are
compatible with the currently installed version of spaCy. Should be run after
@ -88,20 +88,20 @@ and command for updating are shown.
> suite, to ensure all models are up to date before proceeding. If incompatible
> models are found, it will return `1`.
```bash
```cli
$ python -m spacy validate
```
| Argument | Type | Description |
| ---------- | -------- | --------------------------------------------------------- |
| **PRINTS** | `stdout` | Details about the compatibility of your installed models. |
| Name | Description |
| ---------- | --------------------------------------------------------- |
| **PRINTS** | Details about the compatibility of your installed models. |
## Init {#init new="3"}
## init {#init new="3"}
The `spacy init` CLI includes helpful commands for initializing training config
files and model directories.
### init config {#init-config new="3"}
### init config {#init-config new="3" tag="command"}
Initialize and save a [`config.cfg` file](/usage/training#config) using the
**recommended settings** for your use case. It works just like the
@ -111,25 +111,25 @@ config. The settings you specify will impact the suggested model architectures
and pipeline setup, as well as the hyperparameters. You can also adjust and
customize those settings in your config file later.
> ```bash
> ### Example {wrap="true"}
> #### Example
>
> ```cli
> $ python -m spacy init config config.cfg --lang en --pipeline ner,textcat --optimize accuracy
> ```
```bash
$ python -m spacy init config [output_file] [--lang] [--pipeline]
[--optimize] [--cpu]
```cli
$ python -m spacy init config [output_file] [--lang] [--pipeline] [--optimize] [--cpu]
```
| Argument | Type | Description |
| ------------------ | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `output_file` | positional | Path to output `.cfg` file. If not set, the config is written to stdout so you can pipe it forward to a file. |
| `--lang`, `-l` | option | Optional code of the [language](/usage/models#languages) to use. Defaults to `"en"`. |
| `--pipeline`, `-p` | option | Comma-separated list of trainable [pipeline components](/usage/processing-pipelines#built-in) to include in the model. Defaults to `"tagger,parser,ner"`. |
| `--optimize`, `-o` | option | `"efficiency"` or `"accuracy"`. Whether to optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). This will impact the choice of architecture, pretrained weights and related hyperparameters. Defaults to `"efficiency"`. |
| `--cpu`, `-C` | flag | Whether the model needs to run on CPU. This will impact the choice of architecture, pretrained weights and related hyperparameters. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | file | The config file for training. |
| Name | Description |
| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `output_file` | Path to output `.cfg` file or `-` to write the config to stdout (so you can pipe it forward to a file). Note that if you're writing to stdout, no additional logging info is printed. ~~Path (positional)~~ |
| `--lang`, `-l` | Optional code of the [language](/usage/models#languages) to use. Defaults to `"en"`. ~~str (option)~~ |
| `--pipeline`, `-p` | Comma-separated list of trainable [pipeline components](/usage/processing-pipelines#built-in) to include in the model. Defaults to `"tagger,parser,ner"`. ~~str (option)~~ |
| `--optimize`, `-o` | `"efficiency"` or `"accuracy"`. Whether to optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). This will impact the choice of architecture, pretrained weights and related hyperparameters. Defaults to `"efficiency"`. ~~str (option)~~ |
| `--cpu`, `-C` | Whether the model needs to run on CPU. This will impact the choice of architecture, pretrained weights and related hyperparameters. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | The config file for training. |
### init fill-config {#init-fill-config new="3"}
@ -143,33 +143,32 @@ be created, and their signatures are used to find the defaults. If your config
contains a problem that can't be resolved automatically, spaCy will show you a
validation error with more details.
> ```bash
> ### Example {wrap="true"}
> #### Example
>
> ```cli
> $ python -m spacy init fill-config base.cfg config.cfg
> ```
```bash
```cli
$ python -m spacy init fill-config [base_path] [output_file] [--diff]
```
| Argument | Type | Description |
| -------------- | ---------- | ------------------------------------------------------------------------------------------------------------- |
| `base_path` | positional | Path to base config to fill, e.g. generated by the [quickstart widget](/usage/training#quickstart). |
| `output_file` | positional | Path to output `.cfg` file. If not set, the config is written to stdout so you can pipe it forward to a file. |
| `--diff`, `-D` | flag | Print a visual diff highlighting the changes. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | file | Complete and auto-filled config file for training. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| `base_path` | Path to base config to fill, e.g. generated by the [quickstart widget](/usage/training#quickstart). ~~Path (positional)~~ |
| `output_file` | Path to output `.cfg` file. If not set, the config is written to stdout so you can pipe it forward to a file. ~~Path (positional)~~ |
| `--diff`, `-D` | Print a visual diff highlighting the changes. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Complete and auto-filled config file for training. |
### init model {#init-model new="2"}
<!-- TODO: update for v3 -->
### init model {#init-model new="2" tag="command"}
Create a new model directory from raw data, like word frequencies, Brown
clusters and word vectors. This command is similar to the `spacy model` command
in v1.x. Note that in order to populate the model's vocab, you need to pass in a
JSONL-formatted [vocabulary file](/api/data-formats#vocab-jsonl) as
`--jsonl-loc` with optional `id` values that correspond to the vectors table.
Just loading in vectors will not automatically populate the vocab.
clusters and word vectors. Note that in order to populate the model's vocab, you
need to pass in a JSONL-formatted
[vocabulary file](/api/data-formats#vocab-jsonl) as `--jsonl-loc` with optional
`id` values that correspond to the vectors table. Just loading in vectors will
not automatically populate the vocab.
<Infobox title="New in v3.0" variant="warning">
@ -177,24 +176,23 @@ The `init-model` command is now available as a subcommand of `spacy init`.
</Infobox>
```bash
$ python -m spacy init model [lang] [output_dir] [--jsonl-loc] [--vectors-loc]
[--prune-vectors]
```cli
$ python -m spacy init model [lang] [output_dir] [--jsonl-loc] [--vectors-loc] [--prune-vectors]
```
| Argument | Type | Description |
| ------------------------------------------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `lang` | positional | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`. |
| `output_dir` | positional | Model output directory. Will be created if it doesn't exist. |
| `--jsonl-loc`, `-j` | option | Optional location of JSONL-formatted [vocabulary file](/api/data-formats#vocab-jsonl) with lexical attributes. |
| `--vectors-loc`, `-v` | option | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. |
| `--truncate-vectors`, `-t` <Tag variant="new">2.3</Tag> | option | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. |
| `--prune-vectors`, `-V` | option | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. |
| `--vectors-name`, `-vn` | option | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | model | A spaCy model containing the vocab and vectors. |
| Name | Description |
| ------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lang` | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`. ~~str (positional)~~ |
| `output_dir` | Model output directory. Will be created if it doesn't exist. ~~Path (positional)~~ |
| `--jsonl-loc`, `-j` | Optional location of JSONL-formatted [vocabulary file](/api/data-formats#vocab-jsonl) with lexical attributes. ~~Optional[Path] \(option)~~ |
| `--vectors-loc`, `-v` | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. ~~Optional[Path] \(option)~~ |
| `--truncate-vectors`, `-t` <Tag variant="new">2.3</Tag> | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. ~~int (option)~~ |
| `--prune-vectors`, `-V` | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. ~~int (option)~~ |
| `--vectors-name`, `-vn` | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. ~~str (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | A spaCy model containing the vocab and vectors. |
## Convert {#convert}
## convert {#convert tag="command"}
Convert files into spaCy's
[binary training data format](/api/data-formats#binary-training), a serialized
@ -202,28 +200,26 @@ Convert files into spaCy's
management functions. The converter can be specified on the command line, or
chosen based on the file extension of the input file.
```bash
$ python -m spacy convert [input_file] [output_dir] [--converter]
[--file-type] [--n-sents] [--seg-sents] [--model] [--morphology]
[--merge-subtokens] [--ner-map] [--lang]
```cli
$ python -m spacy convert [input_file] [output_dir] [--converter] [--file-type] [--n-sents] [--seg-sents] [--model] [--morphology] [--merge-subtokens] [--ner-map] [--lang]
```
| Argument | Type | Description |
| ------------------------------------------------ | ---------- | ------------------------------------------------------------------------------------------------------------------------ |
| `input_file` | positional | Input file. |
| `output_dir` | positional | Output directory for converted file. Defaults to `"-"`, meaning data will be written to `stdout`. |
| `--converter`, `-c` <Tag variant="new">2</Tag> | option | Name of converter to use (see below). |
| `--file-type`, `-t` <Tag variant="new">2.1</Tag> | option | Type of file to create. Either `spacy` (default) for binary [`DocBin`](/api/docbin) data or `json` for v2.x JSON format. |
| `--n-sents`, `-n` | option | Number of sentences per document. |
| `--seg-sents`, `-s` <Tag variant="new">2.2</Tag> | flag | Segment sentences (for `-c ner`) |
| `--model`, `-b` <Tag variant="new">2.2</Tag> | option | Model for parser-based sentence segmentation (for `-s`) |
| `--morphology`, `-m` | option | Enable appending morphology to tags. |
| `--ner-map`, `-nm` | option | NER tag mapping (as JSON-encoded dict of entity types). |
| `--lang`, `-l` <Tag variant="new">2.1</Tag> | option | Language code (if tokenizer required). |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | binary | Binary [`DocBin`](/api/docbin) training data that can be used with [`spacy train`](/api/cli#train). |
| Name | Description |
| ------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- |
| `input_file` | Input file. ~~Path (positional)~~ |
| `output_dir` | Output directory for converted file. Defaults to `"-"`, meaning data will be written to `stdout`. ~~Optional[Path] \(positional)~~ |
| `--converter`, `-c` <Tag variant="new">2</Tag> | Name of converter to use (see below). ~~str (option)~~ |
| `--file-type`, `-t` <Tag variant="new">2.1</Tag> | Type of file to create. Either `spacy` (default) for binary [`DocBin`](/api/docbin) data or `json` for v2.x JSON format. ~~str (option)~~ |
| `--n-sents`, `-n` | Number of sentences per document. ~~int (option)~~ |
| `--seg-sents`, `-s` <Tag variant="new">2.2</Tag> | Segment sentences (for `--converter ner`). ~~bool (flag)~~ |
| `--model`, `-b` <Tag variant="new">2.2</Tag> | Model for parser-based sentence segmentation (for `--seg-sents`). ~~Optional[str](option)~~ |
| `--morphology`, `-m` | Enable appending morphology to tags. ~~bool (flag)~~ |
| `--ner-map`, `-nm` | NER tag mapping (as JSON-encoded dict of entity types). ~~Optional[Path](option)~~ |
| `--lang`, `-l` <Tag variant="new">2.1</Tag> | Language code (if tokenizer required). ~~Optional[str] \(option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Binary [`DocBin`](/api/docbin) training data that can be used with [`spacy train`](/api/cli#train). |
### Converters
### Converters {#converters}
| ID | Description |
| ------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
@ -233,12 +229,12 @@ $ python -m spacy convert [input_file] [output_dir] [--converter]
| `ner` | NER with IOB/IOB2 tags, one token per line with columns separated by whitespace. The first column is the token and the final column is the IOB tag. Sentences are separated by blank lines and documents are separated by the line `-DOCSTART- -X- O O`. Supports CoNLL 2003 NER format. See [sample data](https://github.com/explosion/spaCy/tree/master/examples/training/ner_example_data). |
| `iob` | NER with IOB/IOB2 tags, one sentence per line with tokens separated by whitespace and annotation separated by `|`, either `word|B-ENT` or `word|POS|B-ENT`. See [sample data](https://github.com/explosion/spaCy/tree/master/examples/training/ner_example_data). |
## Debug {#debug new="3"}
## debug {#debug new="3"}
The `spacy debug` CLI includes helpful commands for debugging and profiling your
configs, data and implementations.
### debug config {#debug-config}
### debug config {#debug-config new="3" tag="command"}
Debug a [`config.cfg` file](/usage/training#config) and show validation errors.
The command will create all objects in the tree and validate them. Note that
@ -246,15 +242,15 @@ some config validation errors are blocking and will prevent the rest of the
config from being resolved. This means that you may not see all validation
errors at once and some issues are only shown once previous errors have been
fixed. To auto-fill a partial config and save the result, you can use the
[`init config`](/api/cli#init-config) command.
[`init fillconfig`](/api/cli#init-fill-config) command.
```bash
$ python -m spacy debug config [config_path] [--code_path] [--output] [--auto_fill] [--diff] [overrides]
```cli
$ python -m spacy debug config [config_path] [--code_path] [overrides]
```
> #### Example
>
> ```bash
> ```cli
> $ python -m spacy debug config ./config.cfg
> ```
@ -277,18 +273,15 @@ python -m spacy init fill-config tmp/starter-config_invalid.cfg --base tmp/start
</Accordion>
| Argument | Type | Default | Description |
| --------------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- |
| `config_path` | positional | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. |
| `--code_path`, `-c` | option | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-models) for new architectures. |
| `--auto_fill`, `-F` | option | Whether or not to auto-fill the config with built-in defaults if possible. If `False`, the provided config needs to be complete. |
| `--output_path`, `-o` | option | Output path where the filled config can be stored. Use '-' for standard output. |
| `--diff`, `-D` | option | `Show a visual diff if config was auto-filled. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| overrides | option / flag | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. |
| **PRINTS** | stdout | Config validation errors, if available. |
| Name | Description |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
| `--code_path`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
| **PRINTS** | Config validation errors, if available. |
### debug data {#debug-data}
### debug data {#debug-data tag="command"}
Analyze, debug, and validate your training and development data. Get useful
stats, and find problems like invalid entity annotations, cyclic dependencies,
@ -303,14 +296,13 @@ takes the same arguments as `train` and reads settings off the
</Infobox>
```bash
$ python -m spacy debug data [config_path] [--code] [--ignore-warnings]
[--verbose] [--no-format] [overrides]
```cli
$ python -m spacy debug data [config_path] [--code] [--ignore-warnings] [--verbose] [--no-format] [overrides]
```
> #### Example
>
> ```bash
> ```cli
> $ python -m spacy debug data ./config.cfg
> ```
@ -453,18 +445,18 @@ will not be available.
</Accordion>
| Argument | Type | Description |
| -------------------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `config_path` | positional | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. |
| `--code`, `-c` | option | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-models) for new architectures. |
| `--ignore-warnings`, `-IW` | flag | Ignore warnings, only show stats and errors. |
| `--verbose`, `-V` | flag | Print additional information and explanations. |
| `--no-format`, `-NF` | flag | Don't pretty-print the results. Use this if you want to write to a file. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| overrides | option / flag | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. |
| **PRINTS** | stdout | Debugging information. |
| Name | Description |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--ignore-warnings`, `-IW` | Ignore warnings, only show stats and errors. ~~bool (flag)~~ |
| `--verbose`, `-V` | Print additional information and explanations. ~~bool (flag)~~ |
| `--no-format`, `-NF` | Don't pretty-print the results. Use this if you want to write to a file. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
| **PRINTS** | Debugging information. |
### debug profile {#debug-profile}
### debug profile {#debug-profile tag="command"}
Profile which functions take the most time in a spaCy pipeline. Input should be
formatted as one JSON object per line with a key `"text"`. It can either be
@ -478,26 +470,25 @@ The `profile` command is now available as a subcommand of `spacy debug`.
</Infobox>
```bash
```cli
$ python -m spacy debug profile [model] [inputs] [--n-texts]
```
| Argument | Type | Description |
| ----------------- | ---------- | ----------------------------------------------------------------- |
| `model` | positional | A loadable spaCy model. |
| `inputs` | positional | Optional path to input file, or `-` for standard input. |
| `--n-texts`, `-n` | option | Maximum number of texts to use if available. Defaults to `10000`. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **PRINTS** | stdout | Profiling information for the model. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------- |
| `model` | A loadable spaCy model. ~~str (positional)~~ |
| `inputs` | Optional path to input file, or `-` for standard input. ~~Path (positional)~~ |
| `--n-texts`, `-n` | Maximum number of texts to use if available. Defaults to `10000`. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **PRINTS** | Profiling information for the model. |
### debug model {#debug-model}
### debug model {#debug-model new="3" tag="command"}
Debug a Thinc [`Model`](https://thinc.ai/docs/api-model) by running it on a
sample text and checking how it updates its internal weights and parameters.
```bash
$ python -m spacy debug model [config_path] [component] [--layers] [-DIM]
[-PAR] [-GRAD] [-ATTR] [-P0] [-P1] [-P2] [P3] [--gpu-id]
```cli
$ python -m spacy debug model [config_path] [component] [--layers] [-DIM] [-PAR] [-GRAD] [-ATTR] [-P0] [-P1] [-P2] [P3] [--gpu-id]
```
<Accordion title="Example outputs" spaced>
@ -507,7 +498,7 @@ model ("Step 0"), which helps us to understand the internal structure of the
Neural Network, and to focus on specific layers that we want to inspect further
(see next example).
```bash
```cli
$ python -m spacy debug model ./config.cfg tagger -P0
```
@ -553,7 +544,7 @@ an all-zero matrix determined by the `nO` and `nI` dimensions. After a first
training step (Step 2), this matrix has clearly updated its values through the
training feedback loop.
```bash
```cli
$ python -m spacy debug model ./config.cfg tagger -l "5,15" -DIM -PAR -P0 -P1 -P2
```
@ -596,23 +587,24 @@ $ python -m spacy debug model ./config.cfg tagger -l "5,15" -DIM -PAR -P0 -P1 -P
</Accordion>
| Argument | Type | Description |
| ----------------------- | ---------- | ----------------------------------------------------------------------------------------------------- |
| `config_path` | positional | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. | |
| `component` | positional | Name of the pipeline component of which the model should be analyzed. |   |
| `--layers`, `-l` | option | Comma-separated names of layer IDs to print. | |
| `--dimensions`, `-DIM` | option | Show dimensions of each layer. |
| `--parameters`, `-PAR` | option | Show parameters of each layer. |
| `--gradients`, `-GRAD` | option | Show gradients of each layer. |
| `--attributes`, `-ATTR` | option | Show attributes of each layer. |
| `--print-step0`, `-P0` | option | Print model before training. |
| `--print-step1`, `-P1` | option | Print model after initialization. |
| `--print-step2`, `-P2` | option | Print model after training. |
| `--print-step3`, `-P3` | option | Print final predictions. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **PRINTS** | stdout | Debugging information. |
| Name | Description |
| ----------------------- | --------------------------------------------------------------------------------------------------------------------------- |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
| `component` | Name of the pipeline component of which the model should be analyzed. ~~str (positional)~~ |
| `--layers`, `-l` | Comma-separated names of layer IDs to print. ~~str (option)~~ |
| `--dimensions`, `-DIM` | Show dimensions of each layer. ~~bool (flag)~~ |
| `--parameters`, `-PAR` | Show parameters of each layer. ~~bool (flag)~~ |
| `--gradients`, `-GRAD` | Show gradients of each layer. ~~bool (flag)~~ |
| `--attributes`, `-ATTR` | Show attributes of each layer. ~~bool (flag)~~ |
| `--print-step0`, `-P0` | Print model before training. ~~bool (flag)~~ |
| `--print-step1`, `-P1` | Print model after initialization. ~~bool (flag)~~ |
| `--print-step2`, `-P2` | Print model after training. ~~bool (flag)~~ |
| `--print-step3`, `-P3` | Print final predictions. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU ID or `-1` for CPU. Defaults to `-1`. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **PRINTS** | Debugging information. |
## Train {#train}
## train {#train tag="command"}
Train a model. Expects data in spaCy's
[binary format](/api/data-formats#training) and a
@ -620,9 +612,9 @@ Train a model. Expects data in spaCy's
Will save out the best model from all epochs, as well as the final model. The
`--code` argument can be used to provide a Python file that's imported before
the training process starts. This lets you register
[custom functions](/usage/training#custom-models) and architectures and refer to
them in your config, all while still using spaCy's built-in `train` workflow. If
you need to manage complex multi-step training workflows, check out the new
[custom functions](/usage/training#custom-functions) and architectures and refer
to them in your config, all while still using spaCy's built-in `train` workflow.
If you need to manage complex multi-step training workflows, check out the new
[spaCy projects](/usage/projects).
<Infobox title="New in v3.0" variant="warning">
@ -636,21 +628,21 @@ in the section `[paths]`.
</Infobox>
```bash
```cli
$ python -m spacy train [config_path] [--output] [--code] [--verbose] [overrides]
```
| Argument | Type | Description |
| ----------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `config_path` | positional | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. |
| `--output`, `-o` | positional | Directory to store model in. Will be created if it doesn't exist. |
| `--code`, `-c` | option | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-models) for new architectures. |
| `--verbose`, `-V` | flag | Show more detailed messages during training. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| overrides | option / flag | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. |
| **CREATES** | model | The final model and the best model. |
| Name | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
| `--output`, `-o` | Directory to store model in. Will be created if it doesn't exist. ~~Optional[Path] \(positional)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--verbose`, `-V` | Show more detailed messages during training. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
| **CREATES** | The final model and the best model. |
## Pretrain {#pretrain new="2.1" tag="experimental"}
## pretrain {#pretrain new="2.1" tag="command,experimental"}
Pretrain the "token to vector" ([`Tok2vec`](/api/tok2vec)) layer of pipeline
components on [raw text](/api/data-formats#pretrain), using an approximate
@ -673,24 +665,23 @@ the [data format](/api/data-formats#config) for details.
</Infobox>
```bash
$ python -m spacy pretrain [texts_loc] [output_dir] [config_path]
[--code] [--resume-path] [--epoch-resume] [overrides]
```cli
$ python -m spacy pretrain [texts_loc] [output_dir] [config_path] [--code] [--resume-path] [--epoch-resume] [overrides]
```
| Argument | Type | Description |
| ----------------------- | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `texts_loc` | positional | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"` or tokens as the key `"tokens"`. [See here](/api/data-formats#pretrain) for details. |
| `output_dir` | positional | Directory to write models to on each epoch. |
| `config_path` | positional | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. |
| `--code`, `-c` | option | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-models) for new architectures. |
| `--resume-path`, `-r` | option | Path to pretrained weights from which to resume pretraining. |
| `--epoch-resume`, `-er` | option | The epoch to resume counting from when using `--resume-path`. Prevents unintended overwriting of existing weight files. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| overrides | option / flag | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--training.use_gpu 1`. |
| **CREATES** | weights | The pretrained weights that can be used to initialize `spacy train`. |
| Name | Description |
| ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `texts_loc` | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"` or tokens as the key `"tokens"`. [See here](/api/data-formats#pretrain) for details. ~~Path (positional)~~ |
| `output_dir` | Directory to write models to on each epoch. ~~Path (positional)~~ |
| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--resume-path`, `-r` | Path to pretrained weights from which to resume pretraining. ~~Optional[Path] \(option)~~ |
| `--epoch-resume`, `-er` | The epoch to resume counting from when using `--resume-path`. Prevents unintended overwriting of existing weight files. ~~Optional[int] \(option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--training.dropout 0.2`. ~~Any (option/flag)~~ |
| **CREATES** | The pretrained weights that can be used to initialize `spacy train`. |
## Evaluate {#evaluate new="2"}
## evaluate {#evaluate new="2" tag="command"}
Evaluate a model. Expects a loadable spaCy model and evaluation data in the
[binary `.spacy` format](/api/data-formats#binary-training). The
@ -702,32 +693,31 @@ skew. To render a sample of dependency parses in a HTML file using the
[displaCy visualizations](/usage/visualizers), set as output directory as the
`--displacy-path` argument.
```bash
$ python -m spacy evaluate [model] [data_path] [--output] [--gold-preproc]
[--gpu-id] [--displacy-path] [--displacy-limit]
```cli
$ python -m spacy evaluate [model] [data_path] [--output] [--gold-preproc] [--gpu-id] [--displacy-path] [--displacy-limit]
```
| Argument | Type | Description |
| ------------------------- | -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | positional | Model to evaluate. Can be a package or a path to a model data directory. |
| `data_path` | positional | Location of evaluation data in spaCy's [binary format](/api/data-formats#training). |
| `--output`, `-o` | option | Output JSON file for metrics. If not set, no metrics will be exported. |
| `--gold-preproc`, `-G` | flag | Use gold preprocessing. |
| `--gpu-id`, `-g` | option | GPU to use, if any. Defaults to `-1` for CPU. |
| `--displacy-path`, `-dp` | option | Directory to output rendered parses as HTML. If not set, no visualizations will be generated. |
| `--displacy-limit`, `-dl` | option | Number of parses to generate per file. Defaults to `25`. Keep in mind that a significantly higher number might cause the `.html` files to render slowly. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | `stdout`, JSON, HTML | Training results and optional metrics and visualizations. |
| Name | Description |
| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | Model to evaluate. Can be a package or a path to a model data directory. ~~str (positional)~~ |
| `data_path` | Location of evaluation data in spaCy's [binary format](/api/data-formats#training). ~~Path (positional)~~ |
| `--output`, `-o` | Output JSON file for metrics. If not set, no metrics will be exported. ~~Optional[Path] \(option)~~ |
| `--gold-preproc`, `-G` | Use gold preprocessing. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
| `--displacy-path`, `-dp` | Directory to output rendered parses as HTML. If not set, no visualizations will be generated. ~~Optional[Path] \(option)~~ |
| `--displacy-limit`, `-dl` | Number of parses to generate per file. Defaults to `25`. Keep in mind that a significantly higher number might cause the `.html` files to render slowly. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Training results and optional metrics and visualizations. |
## Package {#package}
## package {#package tag="command"}
Generate an installable
[model Python package](/usage/training#models-generating) from an existing model
data directory. All data files are copied over. If the path to a `meta.json` is
supplied, or a `meta.json` is found in the input directory, this file is used.
Otherwise, the data can be entered directly from the command line. spaCy will
then create a `.tar.gz` archive file that you can distribute and install with
`pip install`.
data directory. All data files are copied over. If the path to a
[`meta.json`](/api/data-formats#meta) is supplied, or a `meta.json` is found in
the input directory, this file is used. Otherwise, the data can be entered
directly from the command line. spaCy will then create a `.tar.gz` archive file
that you can distribute and install with `pip install`.
<Infobox title="New in v3.0" variant="warning">
@ -737,38 +727,37 @@ this, you can set the `--no-sdist` flag.
</Infobox>
```bash
$ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta]
[--no-sdist] [--version] [--force]
```cli
$ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--no-sdist] [--version] [--force]
```
> #### Example
>
> ```bash
> python -m spacy package /input /output
> cd /output/en_model-0.0.0
> pip install dist/en_model-0.0.0.tar.gz
> ```cli
> $ python -m spacy package /input /output
> $ cd /output/en_model-0.0.0
> $ pip install dist/en_model-0.0.0.tar.gz
> ```
| Argument | Type | Description |
| ------------------------------------------------ | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `input_dir` | positional | Path to directory containing model data. |
| `output_dir` | positional | Directory to create package folder in. |
| `--meta-path`, `-m` <Tag variant="new">2</Tag> | option | Path to `meta.json` file (optional). |
| `--create-meta`, `-C` <Tag variant="new">2</Tag> | flag | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. |
| `--no-sdist`, `-NS`, | flag | Don't build the `.tar.gz` sdist automatically. Can be set if you want to run this step manually. |
| `--version`, `-v` <Tag variant="new">3</Tag> | option | Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template. |
| `--force`, `-f` | flag | Force overwriting of existing folder in output directory. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | directory | A Python package containing the spaCy model. |
| Name | Description |
| ------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `input_dir` | Path to directory containing model data. ~~Path (positional)~~ |
| `output_dir` | Directory to create package folder in. ~~Path (positional)~~ |
| `--meta-path`, `-m` <Tag variant="new">2</Tag> | Path to [`meta.json`](/api/data-formats#meta) file (optional). ~~Optional[Path] \(option)~~ |
| `--create-meta`, `-C` <Tag variant="new">2</Tag> | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. ~~bool (flag)~~ |
| `--no-sdist`, `-NS`, | Don't build the `.tar.gz` sdist automatically. Can be set if you want to run this step manually. ~~bool (flag)~~ |
| `--version`, `-v` <Tag variant="new">3</Tag> | Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template. ~~Optional[str] \(option)~~ |
| `--force`, `-f` | Force overwriting of existing folder in output directory. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | A Python package containing the spaCy model. |
## Project {#project new="3"}
## project {#project new="3"}
The `spacy project` CLI includes subcommands for working with
[spaCy projects](/usage/projects), end-to-end workflows for building and
deploying custom spaCy models.
### project clone {#project-clone}
### project clone {#project-clone tag="command"}
Clone a project template from a Git repository. Calls into `git` under the hood
and uses the sparse checkout feature, so you're only downloading what you need.
@ -779,31 +768,31 @@ can provide any other repo (public or private) that you have access to using the
<!-- TODO: update example once we've decided on repo structure -->
```bash
```cli
$ python -m spacy project clone [name] [dest] [--repo]
```
> #### Example
>
> ```bash
> ```cli
> $ python -m spacy project clone some_example
> ```
>
> Clone from custom repo:
>
> ```bash
> ```cli
> $ python -m spacy project clone template --repo https://github.com/your_org/your_repo
> ```
| Argument | Type | Description |
| -------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------- |
| `name` | positional | The name of the template to clone, relative to the repo. Can be a top-level directory or a subdirectory like `dir/template`. |
| `dest` | positional | Where to clone the project. Defaults to current working directory. |
| `--repo`, `-r` | option | The repository to clone from. Can be any public or private Git repo you have access to. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | directory | The cloned [project directory](/usage/projects#project-files). |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | The name of the template to clone, relative to the repo. Can be a top-level directory or a subdirectory like `dir/template`. ~~str (positional)~~ |
| `dest` | Where to clone the project. Defaults to current working directory. ~~Path (positional)~~ |
| `--repo`, `-r` | The repository to clone from. Can be any public or private Git repo you have access to. ~~str (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | The cloned [project directory](/usage/projects#project-files). |
### project assets {#project-assets}
### project assets {#project-assets tag="command"}
Fetch project assets like datasets and pretrained weights. Assets are defined in
the `assets` section of the [`project.yml`](/usage/projects#project-yml). If a
@ -814,23 +803,23 @@ considered "private" and you have to take care of putting them into the
destination directory yourself. If a local path is provided, the asset is copied
into the current project.
```bash
```cli
$ python -m spacy project assets [project_dir]
```
> #### Example
>
> ```bash
> ```cli
> $ python -m spacy project assets
> ```
| Argument | Type | Description |
| -------------- | ---------- | ----------------------------------------------------------------- |
| `project_dir` | positional | Path to project directory. Defaults to current working directory. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | files | Downloaded or copied assets defined in the `project.yml`. |
| Name | Description |
| -------------- | --------------------------------------------------------------------------------------- |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Downloaded or copied assets defined in the `project.yml`. |
### project run {#project-run}
### project run {#project-run tag="command"}
Run a named command or workflow defined in the
[`project.yml`](/usage/projects#project-yml). If a workflow name is specified,
@ -839,26 +828,112 @@ all commands in the workflow are run, in order. If commands define
re-run if state has changed. For example, if the input dataset changes, a
preprocessing command that depends on those files will be re-run.
```bash
```cli
$ python -m spacy project run [subcommand] [project_dir] [--force] [--dry]
```
> #### Example
>
> ```bash
> ```cli
> $ python -m spacy project run train
> ```
| Argument | Type | Description |
| --------------- | ---------- | ----------------------------------------------------------------- |
| `subcommand` | positional | Name of the command or workflow to run. |
| `project_dir` | positional | Path to project directory. Defaults to current working directory. |
| `--force`, `-F` | flag | Force re-running steps, even if nothing changed. |
| `--dry`, `-D` | flag |  Perform a dry run and don't execute scripts. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **EXECUTES** | script | The command defined in the `project.yml`. |
| Name | Description |
| --------------- | --------------------------------------------------------------------------------------- |
| `subcommand` | Name of the command or workflow to run. ~~str (positional)~~ |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `--force`, `-F` | Force re-running steps, even if nothing changed. ~~bool (flag)~~ |
| `--dry`, `-D` |  Perform a dry run and don't execute scripts. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **EXECUTES** | The command defined in the `project.yml`. |
### project dvc {#project-dvc}
### project push {#project-push tag="command"}
Upload all available files or directories listed as in the `outputs` section of
commands to a remote storage. Outputs are archived and compressed prior to
upload, and addressed in the remote storage using the output's relative path
(URL encoded), a hash of its command string and dependencies, and a hash of its
file contents. This means `push` should **never overwrite** a file in your
remote. If all the hashes match, the contents are the same and nothing happens.
If the contents are different, the new version of the file is uploaded. Deleting
obsolete files is left up to you.
Remotes can be defined in the `remotes` section of the
[`project.yml`](/usage/projects#project-yml). Under the hood, spaCy uses the
[`smart-open`](https://github.com/RaRe-Technologies/smart_open) library to
communicate with the remote storages, so you can use any protocol that
`smart-open` supports, including [S3](https://aws.amazon.com/s3/),
[Google Cloud Storage](https://cloud.google.com/storage), SSH and more, although
you may need to install extra dependencies to use certain protocols.
```cli
$ python -m spacy project push [remote] [project_dir]
```
> #### Example
>
> ```cli
> $ python -m spacy project push my_bucket
> ```
>
> ```yaml
> ### project.yml
> remotes:
> my_bucket: 's3://my-spacy-bucket'
> ```
| Name | Description |
| -------------- | --------------------------------------------------------------------------------------- |
| `remote` | The name of the remote to upload to. Defaults to `"default"`. ~~str (positional)~~ |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **UPLOADS** | All project outputs that exist and are not already stored in the remote. |
### project pull {#project-pull tag="command"}
Download all files or directories listed as `outputs` for commands, unless they
are not already present locally. When searching for files in the remote, `pull`
won't just look at the output path, but will also consider the **command
string** and the **hashes of the dependencies**. For instance, let's say you've
previously pushed a model checkpoint to the remote, but now you've changed some
hyper-parameters. Because you've changed the inputs to the command, if you run
`pull`, you won't retrieve the stale result. If you train your model and push
the outputs to the remote, the outputs will be saved alongside the prior
outputs, so if you change the config back, you'll be able to fetch back the
result.
Remotes can be defined in the `remotes` section of the
[`project.yml`](/usage/projects#project-yml). Under the hood, spaCy uses the
[`smart-open`](https://github.com/RaRe-Technologies/smart_open) library to
communicate with the remote storages, so you can use any protocol that
`smart-open` supports, including [S3](https://aws.amazon.com/s3/),
[Google Cloud Storage](https://cloud.google.com/storage), SSH and more, although
you may need to install extra dependencies to use certain protocols.
```cli
$ python -m spacy project pull [remote] [project_dir]
```
> #### Example
>
> ```cli
> $ python -m spacy project pull my_bucket
> ```
>
> ```yaml
> ### project.yml
> remotes:
> my_bucket: 's3://my-spacy-bucket'
> ```
| Name | Description |
| -------------- | --------------------------------------------------------------------------------------- |
| `remote` | The name of the remote to download from. Defaults to `"default"`. ~~str (positional)~~ |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **DOWNLOADS** | All project outputs that do not exist locally and can be found in the remote. |
### project dvc {#project-dvc tag="command"}
Auto-generate [Data Version Control](https://dvc.org) (DVC) config file. Calls
[`dvc run`](https://dvc.org/doc/command-reference/run) with `--no-exec` under
@ -878,23 +953,23 @@ You'll also need to add the assets you want to track with
</Infobox>
```bash
```cli
$ python -m spacy project dvc [project_dir] [workflow] [--force] [--verbose]
```
> #### Example
>
> ```bash
> git init
> dvc init
> python -m spacy project dvc all
> ```cli
> $ git init
> $ dvc init
> $ python -m spacy project dvc all
> ```
| Argument | Type | Description |
| ----------------- | ---------- | --------------------------------------------------------------------------------------------- |
| `project_dir` | positional | Path to project directory. Defaults to current working directory. |
| `workflow` | positional | Name of workflow defined in `project.yml`. Defaults to first workflow if not set. |
| `--force`, `-F` | flag | Force-updating config file. |
| `--verbose`, `-V` | flag |  Print more output generated by DVC. |
| `--help`, `-h` | flag | Show help message and available arguments. |
| **CREATES** | file | A `dvc.yaml` file in the project directory, based on the steps defined in the given workflow. |
| Name | Description |
| ----------------- | ----------------------------------------------------------------------------------------------------------------- |
| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
| `workflow` | Name of workflow defined in `project.yml`. Defaults to first workflow if not set. ~~Optional[str] \(positional)~~ |
| `--force`, `-F` | Force-updating config file. ~~bool (flag)~~ |
| `--verbose`, `-V` |  Print more output generated by DVC. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | A `dvc.yaml` file in the project directory, based on the steps defined in the given workflow. |

38
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@ -17,7 +17,7 @@ customize the data loading during training, you can register your own
or evaluation data. It takes the same arguments as the `Corpus` class and
returns a callable that yields [`Example`](/api/example) objects. You can
replace it with your own registered function in the
[`@readers` registry](/api/top-level#regsitry) to customize the data loading and
[`@readers` registry](/api/top-level#registry) to customize the data loading and
streaming.
> #### Example config
@ -28,18 +28,18 @@ streaming.
>
> [training.train_corpus]
> @readers = "spacy.Corpus.v1"
> path = ${paths:train}
> path = ${paths.train}
> gold_preproc = false
> max_length = 0
> limit = 0
> ```
| Name | Type | Description |
| --------------- | ------ | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| `path` | `Path` | The directory or filename to read from. Expects data in spaCy's binary [`.spacy` format](/api/data-formats#binary-training). |
|  `gold_preproc` | bool | Whether to set up the Example object with gold-standard sentences and tokens for the predictions. See [`Corpus`](/api/corpus#init) for details. |
| `max_length` | int | Maximum document length. Longer documents will be split into sentences, if sentence boundaries are available. Defaults to `0` for no limit. |
| `limit` | int | Limit corpus to a subset of examples, e.g. for debugging. Defaults to `0` for no limit. |
| Name | Description |
| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `path` | The directory or filename to read from. Expects data in spaCy's binary [`.spacy` format](/api/data-formats#binary-training). ~~Path~~ |
|  `gold_preproc` | Whether to set up the Example object with gold-standard sentences and tokens for the predictions. See [`Corpus`](/api/corpus#init) for details. ~~bool~~ |
| `max_length` | Maximum document length. Longer documents will be split into sentences, if sentence boundaries are available. Defaults to `0` for no limit. ~~int~~ |
| `limit` | Limit corpus to a subset of examples, e.g. for debugging. Defaults to `0` for no limit. ~~int~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/gold/corpus.py
@ -67,13 +67,13 @@ train/test skew.
> corpus = Corpus("./data", limit=10)
> ```
| Name | Type | Description |
| --------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | The directory or filename to read from. |
| _keyword-only_ | | |
|  `gold_preproc` | bool | Whether to set up the Example object with gold-standard sentences and tokens for the predictions. Defaults to `False`. |
| `max_length` | int | Maximum document length. Longer documents will be split into sentences, if sentence boundaries are available. Defaults to `0` for no limit. |
| `limit` | int | Limit corpus to a subset of examples, e.g. for debugging. Defaults to `0` for no limit. |
| Name | Description |
| --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |
| `path` | The directory or filename to read from. ~~Union[str, Path]~~ |
| _keyword-only_ | |
|  `gold_preproc` | Whether to set up the Example object with gold-standard sentences and tokens for the predictions. Defaults to `False`. ~~bool~~ |
| `max_length` | Maximum document length. Longer documents will be split into sentences, if sentence boundaries are available. Defaults to `0` for no limit. ~~int~~ |
| `limit` | Limit corpus to a subset of examples, e.g. for debugging. Defaults to `0` for no limit. ~~int~~ |
## Corpus.\_\_call\_\_ {#call tag="method"}
@ -90,7 +90,7 @@ Yield examples from the data.
> train_data = corpus(nlp)
> ```
| Name | Type | Description |
| ---------- | ---------- | ------------------------- |
| `nlp` | `Language` | The current `nlp` object. |
| **YIELDS** | `Example` | The examples. |
| Name | Description |
| ---------- | -------------------------------------- |
| `nlp` | The current `nlp` object. ~~Language~~ |
| **YIELDS** | The examples. ~~Example~~ |

110
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@ -23,13 +23,13 @@ accessed from Python. For the Python documentation, see [`Doc`](/api/doc).
### Attributes {#doc_attributes}
| Name | Type | Description |
| ------------ | ------------ | ----------------------------------------------------------------------------------------- |
| `mem` | `cymem.Pool` | A memory pool. Allocated memory will be freed once the `Doc` object is garbage collected. |
| `vocab` | `Vocab` | A reference to the shared `Vocab` object. |
| `c` | `TokenC*` | A pointer to a [`TokenC`](/api/cython-structs#tokenc) struct. |
| `length` | `int` | The number of tokens in the document. |
| `max_length` | `int` | The underlying size of the `Doc.c` array. |
| Name | Description |
| ------------ | -------------------------------------------------------------------------------------------------------- |
| `mem` | A memory pool. Allocated memory will be freed once the `Doc` object is garbage collected. ~~cymem.Pool~~ |
| `vocab` | A reference to the shared `Vocab` object. ~~Vocab~~ |
| `c` | A pointer to a [`TokenC`](/api/cython-structs#tokenc) struct. ~~TokenC\*~~ |
| `length` | The number of tokens in the document. ~~int~~ |
| `max_length` | The underlying size of the `Doc.c` array. ~~int~~ |
### Doc.push_back {#doc_push_back tag="method"}
@ -50,10 +50,10 @@ Append a token to the `Doc`. The token can be provided as a
> assert doc.text == "hello "
> ```
| Name | Type | Description |
| ------------ | --------------- | ----------------------------------------- |
| `lex_or_tok` | `LexemeOrToken` | The word to append to the `Doc`. |
| `has_space` | `bint` | Whether the word has trailing whitespace. |
| Name | Description |
| ------------ | -------------------------------------------------- |
| `lex_or_tok` | The word to append to the `Doc`. ~~LexemeOrToken~~ |
| `has_space` | Whether the word has trailing whitespace. ~~bint~~ |
## Token {#token tag="cdef class" source="spacy/tokens/token.pxd"}
@ -70,12 +70,12 @@ accessed from Python. For the Python documentation, see [`Token`](/api/token).
### Attributes {#token_attributes}
| Name | Type | Description |
| ------- | --------- | ------------------------------------------------------------- |
| `vocab` | `Vocab` | A reference to the shared `Vocab` object. |
| `c` | `TokenC*` | A pointer to a [`TokenC`](/api/cython-structs#tokenc) struct. |
| `i` | `int` | The offset of the token within the document. |
| `doc` | `Doc` | The parent document. |
| Name | Description |
| ------- | -------------------------------------------------------------------------- |
| `vocab` | A reference to the shared `Vocab` object. ~~Vocab~~ |
| `c` | A pointer to a [`TokenC`](/api/cython-structs#tokenc) struct. ~~TokenC\*~~ |
| `i` | The offset of the token within the document. ~~int~~ |
| `doc` | The parent document. ~~Doc~~ |
### Token.cinit {#token_cinit tag="method"}
@ -87,12 +87,12 @@ Create a `Token` object from a `TokenC*` pointer.
> token = Token.cinit(&doc.c[3], doc, 3)
> ```
| Name | Type | Description |
| -------- | --------- | ------------------------------------------------------------ |
| `vocab` | `Vocab` | A reference to the shared `Vocab`. |
| `c` | `TokenC*` | A pointer to a [`TokenC`](/api/cython-structs#tokenc)struct. |
| `offset` | `int` | The offset of the token within the document. |
| `doc` | `Doc` | The parent document. |
| Name | Description |
| -------- | -------------------------------------------------------------------------- |
| `vocab` | A reference to the shared `Vocab`. ~~Vocab~~ |
| `c` | A pointer to a [`TokenC`](/api/cython-structs#tokenc) struct. ~~TokenC\*~~ |
| `offset` | The offset of the token within the document. ~~int~~ |
| `doc` | The parent document. ~~int~~ |
## Span {#span tag="cdef class" source="spacy/tokens/span.pxd"}
@ -107,14 +107,14 @@ accessed from Python. For the Python documentation, see [`Span`](/api/span).
### Attributes {#span_attributes}
| Name | Type | Description |
| ------------ | -------------------------------------- | ------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `start` | `int` | The index of the first token of the span. |
| `end` | `int` | The index of the first token after the span. |
| `start_char` | `int` | The index of the first character of the span. |
| `end_char` | `int` | The index of the last character of the span. |
| `label` | <Abbr title="uint64_t">`attr_t`</Abbr> | A label to attach to the span, e.g. for named entities. |
| Name | Description |
| ------------ | ----------------------------------------------------------------------------- |
| `doc` | The parent document. ~~Doc~~ |
| `start` | The index of the first token of the span. ~~int~~ |
| `end` | The index of the first token after the span. ~~int~~ |
| `start_char` | The index of the first character of the span. ~~int~~ |
| `end_char` | The index of the last character of the span. ~~int~~ |
| `label` | A label to attach to the span, e.g. for named entities. ~~attr_t (uint64_t)~~ |
## Lexeme {#lexeme tag="cdef class" source="spacy/lexeme.pxd"}
@ -129,11 +129,11 @@ accessed from Python. For the Python documentation, see [`Lexeme`](/api/lexeme).
### Attributes {#lexeme_attributes}
| Name | Type | Description |
| ------- | -------------------------------------- | --------------------------------------------------------------- |
| `c` | `LexemeC*` | A pointer to a [`LexemeC`](/api/cython-structs#lexemec) struct. |
| `vocab` | `Vocab` | A reference to the shared `Vocab` object. |
| `orth` | <Abbr title="uint64_t">`attr_t`</Abbr> | ID of the verbatim text content. |
| Name | Description |
| ------- | ----------------------------------------------------------------------------- |
| `c` | A pointer to a [`LexemeC`](/api/cython-structs#lexemec) struct. ~~LexemeC\*~~ |
| `vocab` | A reference to the shared `Vocab` object. ~~Vocab~~ |
| `orth` | ID of the verbatim text content. ~~attr_t (uint64_t)~~ |
## Vocab {#vocab tag="cdef class" source="spacy/vocab.pxd"}
@ -149,11 +149,11 @@ accessed from Python. For the Python documentation, see [`Vocab`](/api/vocab).
### Attributes {#vocab_attributes}
| Name | Type | Description |
| --------- | ------------- | ------------------------------------------------------------------------------------------- |
| `mem` | `cymem.Pool` | A memory pool. Allocated memory will be freed once the `Vocab` object is garbage collected. |
| `strings` | `StringStore` | A `StringStore` that maps string to hash values and vice versa. |
| `length` | `int` | The number of entries in the vocabulary. |
| Name | Description |
| --------- | ---------------------------------------------------------------------------------------------------------- |
| `mem` | A memory pool. Allocated memory will be freed once the `Vocab` object is garbage collected. ~~cymem.Pool~~ |
| `strings` | A `StringStore` that maps string to hash values and vice versa. ~~StringStore~~ |
| `length` | The number of entries in the vocabulary. ~~int~~ |
### Vocab.get {#vocab_get tag="method"}
@ -166,11 +166,11 @@ vocabulary.
> lexeme = vocab.get(vocab.mem, "hello")
> ```
| Name | Type | Description |
| ----------- | ---------------- | ------------------------------------------------------------------------------------------- |
| `mem` | `cymem.Pool` | A memory pool. Allocated memory will be freed once the `Vocab` object is garbage collected. |
| `string` | str | The string of the word to look up. |
| **RETURNS** | `const LexemeC*` | The lexeme in the vocabulary. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------- |
| `mem` | A memory pool. Allocated memory will be freed once the `Vocab` object is garbage collected. ~~cymem.Pool~~ |
| `string` | The string of the word to look up. ~~str~~ |
| **RETURNS** | The lexeme in the vocabulary. ~~const LexemeC\*~~ |
### Vocab.get_by_orth {#vocab_get_by_orth tag="method"}
@ -183,11 +183,11 @@ vocabulary.
> lexeme = vocab.get_by_orth(doc[0].lex.norm)
> ```
| Name | Type | Description |
| ----------- | -------------------------------------- | ------------------------------------------------------------------------------------------- |
| `mem` | `cymem.Pool` | A memory pool. Allocated memory will be freed once the `Vocab` object is garbage collected. |
| `orth` | <Abbr title="uint64_t">`attr_t`</Abbr> | ID of the verbatim text content. |
| **RETURNS** | `const LexemeC*` | The lexeme in the vocabulary. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------- |
| `mem` | A memory pool. Allocated memory will be freed once the `Vocab` object is garbage collected. ~~cymem.Pool~~ |
| `orth` | ID of the verbatim text content. ~~attr_t (uint64_t)~~ |
| **RETURNS** | The lexeme in the vocabulary. ~~const LexemeC\*~~ |
## StringStore {#stringstore tag="cdef class" source="spacy/strings.pxd"}
@ -203,7 +203,7 @@ accessed from Python. For the Python documentation, see
### Attributes {#stringstore_attributes}
| Name | Type | Description |
| ------ | ------------------------------------------------------ | ------------------------------------------------------------------------------------------------ |
| `mem` | `cymem.Pool` | A memory pool. Allocated memory will be freed once the`StringStore` object is garbage collected. |
| `keys` | <Abbr title="vector[uint64_t]">`vector[hash_t]`</Abbr> | A list of hash values in the `StringStore`. |
| Name | Description |
| ------ | ---------------------------------------------------------------------------------------------------------------- |
| `mem` | A memory pool. Allocated memory will be freed once the `StringStore` object is garbage collected. ~~cymem.Pool~~ |
| `keys` | A list of hash values in the `StringStore`. ~~vector[hash_t] \(vector[uint64_t])~~ |

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@ -18,26 +18,26 @@ Cython data container for the `Token` object.
> token_ptr = &doc.c[3]
> ```
| Name | Type | Description |
| ------------ | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lex` | `const LexemeC*` | A pointer to the lexeme for the token. |
| `morph` | `uint64_t` | An ID allowing lookup of morphological attributes. |
| `pos` | `univ_pos_t` | Coarse-grained part-of-speech tag. |
| `spacy` | `bint` | A binary value indicating whether the token has trailing whitespace. |
| `tag` | <Abbr title="uint64_t">`attr_t`</Abbr> | Fine-grained part-of-speech tag. |
| `idx` | `int` | The character offset of the token within the parent document. |
| `lemma` | <Abbr title="uint64_t">`attr_t`</Abbr> | Base form of the token, with no inflectional suffixes. |
| `sense` | <Abbr title="uint64_t">`attr_t`</Abbr> | Space for storing a word sense ID, currently unused. |
| `head` | `int` | Offset of the syntactic parent relative to the token. |
| `dep` | <Abbr title="uint64_t">`attr_t`</Abbr> | Syntactic dependency relation. |
| `l_kids` | `uint32_t` | Number of left children. |
| `r_kids` | `uint32_t` | Number of right children. |
| `l_edge` | `uint32_t` | Offset of the leftmost token of this token's syntactic descendants. |
| `r_edge` | `uint32_t` | Offset of the rightmost token of this token's syntactic descendants. |
| `sent_start` | `int` | Ternary value indicating whether the token is the first word of a sentence. `0` indicates a missing value, `-1` indicates `False` and `1` indicates `True`. The default value, 0, is interpreted as no sentence break. Sentence boundary detectors will usually set 0 for all tokens except tokens that follow a sentence boundary. |
| `ent_iob` | `int` | IOB code of named entity tag. `0` indicates a missing value, `1` indicates `I`, `2` indicates `0` and `3` indicates `B`. |
| `ent_type` | <Abbr title="uint64_t">`attr_t`</Abbr> | Named entity type. |
| `ent_id` | <Abbr title="uint64_t">`attr_t`</Abbr> | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| Name | Description |
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lex` | A pointer to the lexeme for the token. ~~const LexemeC\*~~ |
| `morph` | An ID allowing lookup of morphological attributes. ~~uint64_t~~ |
| `pos` | Coarse-grained part-of-speech tag. ~~univ_pos_t~~ |
| `spacy` | A binary value indicating whether the token has trailing whitespace. ~~bint~~ |
| `tag` | Fine-grained part-of-speech tag. ~~attr_t (uint64_t)~~ |
| `idx` | The character offset of the token within the parent document. ~~int~~ |
| `lemma` | Base form of the token, with no inflectional suffixes. ~~attr_t (uint64_t)~~ |
| `sense` | Space for storing a word sense ID, currently unused. ~~attr_t (uint64_t)~~ |
| `head` | Offset of the syntactic parent relative to the token. ~~int~~ |
| `dep` | Syntactic dependency relation. ~~attr_t (uint64_t)~~ |
| `l_kids` | Number of left children. ~~uint32_t~~ |
| `r_kids` | Number of right children. ~~uint32_t~~ |
| `l_edge` | Offset of the leftmost token of this token's syntactic descendants. ~~uint32_t~~ |
| `r_edge` | Offset of the rightmost token of this token's syntactic descendants. ~~uint32_t~~ |
| `sent_start` | Ternary value indicating whether the token is the first word of a sentence. `0` indicates a missing value, `-1` indicates `False` and `1` indicates `True`. The default value, 0, is interpreted as no sentence break. Sentence boundary detectors will usually set 0 for all tokens except tokens that follow a sentence boundary. ~~int~~ |
| `ent_iob` | IOB code of named entity tag. `0` indicates a missing value, `1` indicates `I`, `2` indicates `0` and `3` indicates `B`. ~~int~~ |
| `ent_type` | Named entity type. ~~attr_t (uint64_t)~~ |
| `ent_id` | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. ~~attr_t (uint64_t)~~ |
### Token.get_struct_attr {#token_get_struct_attr tag="staticmethod, nogil" source="spacy/tokens/token.pxd"}
@ -52,11 +52,11 @@ Get the value of an attribute from the `TokenC` struct by attribute ID.
> is_alpha = Token.get_struct_attr(&doc.c[3], IS_ALPHA)
> ```
| Name | Type | Description |
| ----------- | -------------------------------------- | -------------------------------------------------------------------------------------- |
| `token` | `const TokenC*` | A pointer to a `TokenC` struct. |
| `feat_name` | `attr_id_t` | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. |
| **RETURNS** | <Abbr title="uint64_t">`attr_t`</Abbr> | The value of the attribute. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------- |
| `token` | A pointer to a `TokenC` struct. ~~const TokenC\*~~ |
| `feat_name` | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. ~~attr_id_t~~ |
| **RETURNS** | The value of the attribute. ~~attr_t (uint64_t)~~ |
### Token.set_struct_attr {#token_set_struct_attr tag="staticmethod, nogil" source="spacy/tokens/token.pxd"}
@ -72,11 +72,11 @@ Set the value of an attribute of the `TokenC` struct by attribute ID.
> Token.set_struct_attr(token, TAG, 0)
> ```
| Name | Type | Description |
| ----------- | -------------------------------------- | -------------------------------------------------------------------------------------- |
| `token` | `const TokenC*` | A pointer to a `TokenC` struct. |
| `feat_name` | `attr_id_t` | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. |
| `value` | <Abbr title="uint64_t">`attr_t`</Abbr> | The value to set. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------- |
| `token` | A pointer to a `TokenC` struct. ~~const TokenC\*~~ |
| `feat_name` | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. ~~attr_id_t~~ |
| `value` | The value to set. ~~attr_t (uint64_t)~~ |
### token_by_start {#token_by_start tag="function" source="spacy/tokens/doc.pxd"}
@ -93,12 +93,12 @@ Find a token in a `TokenC*` array by the offset of its first character.
> assert token_by_start(doc.c, doc.length, 4) == -1
> ```
| Name | Type | Description |
| ------------ | --------------- | --------------------------------------------------------- |
| `tokens` | `const TokenC*` | A `TokenC*` array. |
| `length` | `int` | The number of tokens in the array. |
| `start_char` | `int` | The start index to search for. |
| **RETURNS** | `int` | The index of the token in the array or `-1` if not found. |
| Name | Description |
| ------------ | ----------------------------------------------------------------- |
| `tokens` | A `TokenC*` array. ~~const TokenC\*~~ |
| `length` | The number of tokens in the array. ~~int~~ |
| `start_char` | The start index to search for. ~~int~~ |
| **RETURNS** | The index of the token in the array or `-1` if not found. ~~int~~ |
### token_by_end {#token_by_end tag="function" source="spacy/tokens/doc.pxd"}
@ -115,12 +115,12 @@ Find a token in a `TokenC*` array by the offset of its final character.
> assert token_by_end(doc.c, doc.length, 1) == -1
> ```
| Name | Type | Description |
| ----------- | --------------- | --------------------------------------------------------- |
| `tokens` | `const TokenC*` | A `TokenC*` array. |
| `length` | `int` | The number of tokens in the array. |
| `end_char` | `int` | The end index to search for. |
| **RETURNS** | `int` | The index of the token in the array or `-1` if not found. |
| Name | Description |
| ----------- | ----------------------------------------------------------------- |
| `tokens` | A `TokenC*` array. ~~const TokenC\*~~ |
| `length` | The number of tokens in the array. ~~int~~ |
| `end_char` | The end index to search for. ~~int~~ |
| **RETURNS** | The index of the token in the array or `-1` if not found. ~~int~~ |
### set_children_from_heads {#set_children_from_heads tag="function" source="spacy/tokens/doc.pxd"}
@ -143,10 +143,10 @@ attribute, in order to make the parse tree navigation consistent.
> assert doc.c[3].l_kids == 1
> ```
| Name | Type | Description |
| -------- | --------------- | ---------------------------------- |
| `tokens` | `const TokenC*` | A `TokenC*` array. |
| `length` | `int` | The number of tokens in the array. |
| Name | Description |
| -------- | ------------------------------------------ |
| `tokens` | A `TokenC*` array. ~~const TokenC\*~~ |
| `length` | The number of tokens in the array. ~~int~~ |
## LexemeC {#lexemec tag="C struct" source="spacy/structs.pxd"}
@ -160,17 +160,17 @@ struct.
> lex = doc.c[3].lex
> ```
| Name | Type | Description |
| ----------- | --------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| `flags` | <Abbr title="uint64_t">`flags_t`</Abbr> | Bit-field for binary lexical flag values. |
| `id` | <Abbr title="uint64_t">`attr_t`</Abbr> | Usually used to map lexemes to rows in a matrix, e.g. for word vectors. Does not need to be unique, so currently misnamed. |
| `length` | <Abbr title="uint64_t">`attr_t`</Abbr> | Number of unicode characters in the lexeme. |
| `orth` | <Abbr title="uint64_t">`attr_t`</Abbr> | ID of the verbatim text content. |
| `lower` | <Abbr title="uint64_t">`attr_t`</Abbr> | ID of the lowercase form of the lexeme. |
| `norm` | <Abbr title="uint64_t">`attr_t`</Abbr> | ID of the lexeme's norm, i.e. a normalized form of the text. |
| `shape` | <Abbr title="uint64_t">`attr_t`</Abbr> | Transform of the lexeme's string, to show orthographic features. |
| `prefix` | <Abbr title="uint64_t">`attr_t`</Abbr> | Length-N substring from the start of the lexeme. Defaults to `N=1`. |
| `suffix` | <Abbr title="uint64_t">`attr_t`</Abbr> | Length-N substring from the end of the lexeme. Defaults to `N=3`. |
| Name | Description |
| -------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `flags` | Bit-field for binary lexical flag values. ~~flags_t (uint64_t)~~ |
| `id` | Usually used to map lexemes to rows in a matrix, e.g. for word vectors. Does not need to be unique, so currently misnamed. ~~attr_t (uint64_t)~~ |
| `length` | Number of unicode characters in the lexeme. ~~attr_t (uint64_t)~~ |
| `orth` | ID of the verbatim text content. ~~attr_t (uint64_t)~~ |
| `lower` | ID of the lowercase form of the lexeme. ~~attr_t (uint64_t)~~ |
| `norm` | ID of the lexeme's norm, i.e. a normalized form of the text. ~~attr_t (uint64_t)~~ |
| `shape` | Transform of the lexeme's string, to show orthographic features. ~~attr_t (uint64_t)~~ |
| `prefix` | Length-N substring from the start of the lexeme. Defaults to `N=1`. ~~attr_t (uint64_t)~~ |
| `suffix` | Length-N substring from the end of the lexeme. Defaults to `N=3`. ~~attr_t (uint64_t)~~ |
### Lexeme.get_struct_attr {#lexeme_get_struct_attr tag="staticmethod, nogil" source="spacy/lexeme.pxd"}
@ -186,11 +186,11 @@ Get the value of an attribute from the `LexemeC` struct by attribute ID.
> is_alpha = Lexeme.get_struct_attr(lexeme, IS_ALPHA)
> ```
| Name | Type | Description |
| ----------- | -------------------------------------- | -------------------------------------------------------------------------------------- |
| `lex` | `const LexemeC*` | A pointer to a `LexemeC` struct. |
| `feat_name` | `attr_id_t` | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. |
| **RETURNS** | <Abbr title="uint64_t">`attr_t`</Abbr> | The value of the attribute. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------- |
| `lex` | A pointer to a `LexemeC` struct. ~~const LexemeC\*~~ |
| `feat_name` | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. ~~attr_id_t~~ |
| **RETURNS** | The value of the attribute. ~~attr_t (uint64_t)~~ |
### Lexeme.set_struct_attr {#lexeme_set_struct_attr tag="staticmethod, nogil" source="spacy/lexeme.pxd"}
@ -206,11 +206,11 @@ Set the value of an attribute of the `LexemeC` struct by attribute ID.
> Lexeme.set_struct_attr(lexeme, NORM, lexeme.lower)
> ```
| Name | Type | Description |
| ----------- | -------------------------------------- | -------------------------------------------------------------------------------------- |
| `lex` | `const LexemeC*` | A pointer to a `LexemeC` struct. |
| `feat_name` | `attr_id_t` | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. |
| `value` | <Abbr title="uint64_t">`attr_t`</Abbr> | The value to set. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------- |
| `lex` | A pointer to a `LexemeC` struct. ~~const LexemeC\*~~ |
| `feat_name` | The ID of the attribute to look up. The attributes are enumerated in `spacy.typedefs`. ~~attr_id_t~~ |
| `value` | The value to set. ~~attr_t (uint64_t)~~ |
### Lexeme.c_check_flag {#lexeme_c_check_flag tag="staticmethod, nogil" source="spacy/lexeme.pxd"}
@ -226,11 +226,11 @@ Check the value of a binary flag attribute.
> is_stop = Lexeme.c_check_flag(lexeme, IS_STOP)
> ```
| Name | Type | Description |
| ----------- | ---------------- | ------------------------------------------------------------------------------- |
| `lexeme` | `const LexemeC*` | A pointer to a `LexemeC` struct. |
| `flag_id` | `attr_id_t` | The ID of the flag to look up. The flag IDs are enumerated in `spacy.typedefs`. |
| **RETURNS** | `bint` | The boolean value of the flag. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------- |
| `lexeme` | A pointer to a `LexemeC` struct. ~~const LexemeC\*~~ |
| `flag_id` | The ID of the flag to look up. The flag IDs are enumerated in `spacy.typedefs`. ~~attr_id_t~~ |
| **RETURNS** | The boolean value of the flag. ~~bint~~ |
### Lexeme.c_set_flag {#lexeme_c_set_flag tag="staticmethod, nogil" source="spacy/lexeme.pxd"}
@ -246,8 +246,8 @@ Set the value of a binary flag attribute.
> Lexeme.c_set_flag(lexeme, IS_STOP, 0)
> ```
| Name | Type | Description |
| --------- | ---------------- | ------------------------------------------------------------------------------- |
| `lexeme` | `const LexemeC*` | A pointer to a `LexemeC` struct. |
| `flag_id` | `attr_id_t` | The ID of the flag to look up. The flag IDs are enumerated in `spacy.typedefs`. |
| `value` | `bint` | The value to set. |
| Name | Description |
| --------- | --------------------------------------------------------------------------------------------- |
| `lexeme` | A pointer to a `LexemeC` struct. ~~const LexemeC\*~~ |
| `flag_id` | The ID of the flag to look up. The flag IDs are enumerated in `spacy.typedefs`. ~~attr_id_t~~ |
| `value` | The value to set. ~~bint~~ |

219
website/docs/api/data-formats.md
View File

@ -5,7 +5,8 @@ menu:
- ['Training Config', 'config']
- ['Training Data', 'training']
- ['Pretraining Data', 'pretraining']
- ['Vocabulary', 'vocab']
- ['Vocabulary', 'vocab-jsonl']
- ['Model Meta', 'meta']
---
This section documents input and output formats of data used by spaCy, including
@ -73,15 +74,15 @@ your config and check that it's valid, you can run the
Defines the `nlp` object, its tokenizer and
[processing pipeline](/usage/processing-pipelines) component names.
| Name | Type | Description | Default |
| ------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------- |
| `lang` | str | The language code to use. | `null` |
| `pipeline` | `List[str]` | Names of pipeline components in order. Should correspond to sections in the `[components]` block, e.g. `[components.ner]`. See docs on [defining components](/usage/training#config-components). | `[]` |
| `load_vocab_data` | bool | Whether to load additional lexeme and vocab data from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) if available. | `true` |
| `before_creation` | callable | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `Language` subclass before it's initialized. | `null` |
| `after_creation` | callable | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object right after it's initialized. | `null` |
| `after_pipeline_creation` | callable | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object after the pipeline components have been added. | `null` |
| `tokenizer` | callable | The tokenizer to use. | [`Tokenizer`](/api/tokenizer) |
| Name | Description |
| ------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lang` | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). Defaults to `null`. ~~str~~ |
| `pipeline` | Names of pipeline components in order. Should correspond to sections in the `[components]` block, e.g. `[components.ner]`. See docs on [defining components](/usage/training#config-components). Defaults to `[]`. ~~List[str]~~ |
| `load_vocab_data` | Whether to load additional lexeme and vocab data from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) if available. Defaults to `true`. ~~bool~~ |
| `before_creation` | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `Language` subclass before it's initialized. Defaults to `null`. ~~Optional[Callable[[Type[Language]], Type[Language]]]~~ |
| `after_creation` | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object right after it's initialized. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~ |
| `after_pipeline_creation` | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object after the pipeline components have been added. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~ |
| `tokenizer` | The tokenizer to use. Defaults to [`Tokenizer`](/api/tokenizer). ~~Callable[[str], Doc]~~ |
### components {#config-components tag="section"}
@ -110,15 +111,15 @@ model to copy components from). See the docs on
### paths, system {#config-variables tag="variables"}
These sections define variables that can be referenced across the other sections
as variables. For example `${paths:train}` uses the value of `train` defined in
as variables. For example `${paths.train}` uses the value of `train` defined in
the block `[paths]`. If your config includes custom registered functions that
need paths, you can define them here. All config values can also be
[overwritten](/usage/training#config-overrides) on the CLI when you run
[`spacy train`](/api/cli#train), which is especially relevant for data paths
that you don't want to hard-code in your config file.
```bash
$ python -m spacy train ./config.cfg --paths.train ./corpus/train.spacy
```cli
$ python -m spacy train config.cfg --paths.train ./corpus/train.spacy
```
### training {#config-training tag="section"}
@ -126,26 +127,24 @@ $ python -m spacy train ./config.cfg --paths.train ./corpus/train.spacy
This section defines settings and controls for the training and evaluation
process that are used when you run [`spacy train`](/api/cli#train).
<!-- TODO: complete -->
| Name | Type | Description | Default |
| --------------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------- |
| `seed` | int | The random seed. | `${system:seed}` |
| `dropout` | float | The dropout rate. | `0.1` |
| `accumulate_gradient` | int | Whether to divide the batch up into substeps. | `1` |
| `init_tok2vec` | str | Optional path to pretrained tok2vec weights created with [`spacy pretrain`](/api/cli#pretrain). | `${paths:init_tok2vec}` |
| `raw_text` | str | | `${paths:raw}` |
| `vectors` | str | | `null` |
| `patience` | int | How many steps to continue without improvement in evaluation score. | `1600` |
| `max_epochs` | int | Maximum number of epochs to train for. | `0` |
| `max_steps` | int | Maximum number of update steps to train for. | `20000` |
| `eval_frequency` | int | How often to evaluate during training (steps). | `200` |
| `score_weights` | `Dict[str, float]` | Score names shown in metrics mapped to their weight towards the final weighted score. See [here](/usage/training#metrics) for details. | `{}` |
| `frozen_components` | `List[str]` | Pipeline component names that are "frozen" and shouldn't be updated during training. See [here](/usage/training#config-components) for details. | `[]` |
| `train_corpus` | callable | Callable that takes the current `nlp` object and yields [`Example`](/api/example) objects. | [`Corpus`](/api/corpus) |
| `dev_corpus` | callable | Callable that takes the current `nlp` object and yields [`Example`](/api/example) objects. | [`Corpus`](/api/corpus) |
| `batcher` | callable | Callable that takes an iterator of [`Doc`](/api/doc) objects and yields batches of `Doc`s. | [`batch_by_words`](/api/top-level#batch_by_words) |
| `optimizer` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. | [`Adam`](https://thinc.ai/docs/api-optimizers#adam) |
| Name | Description |
| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `accumulate_gradient` | Whether to divide the batch up into substeps. Defaults to `1`. ~~int~~ |
| `batcher` | Callable that takes an iterator of [`Doc`](/api/doc) objects and yields batches of `Doc`s. Defaults to [`batch_by_words`](/api/top-level#batch_by_words). ~~Callable[[Iterator[Doc], Iterator[List[Doc]]]]~~ |
| `dev_corpus` | Callable that takes the current `nlp` object and yields [`Example`](/api/example) objects. Defaults to [`Corpus`](/api/corpus). ~~Callable[[Language], Iterator[Example]]~~ |
| `dropout` | The dropout rate. Defaults to `0.1`. ~~float~~ |
| `eval_frequency` | How often to evaluate during training (steps). Defaults to `200`. ~~int~~ |
| `frozen_components` | Pipeline component names that are "frozen" and shouldn't be updated during training. See [here](/usage/training#config-components) for details. Defaults to `[]`. ~~List[str]~~ |
| `init_tok2vec` | Optional path to pretrained tok2vec weights created with [`spacy pretrain`](/api/cli#pretrain). Defaults to variable `${paths.init_tok2vec}`. ~~Optional[str]~~ |
| `max_epochs` | Maximum number of epochs to train for. Defaults to `0`. ~~int~~ |
| `max_steps` | Maximum number of update steps to train for. Defaults to `20000`. ~~int~~ |
| `optimizer` | The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. Defaults to [`Adam`](https://thinc.ai/docs/api-optimizers#adam). ~~Optimizer~~ |
| `patience` | How many steps to continue without improvement in evaluation score. Defaults to `1600`. ~~int~~ |
| `raw_text` | Optional path to a jsonl file with unlabelled text documents for a [rehearsal](/api/language#rehearse) step. Defaults to variable `${paths.raw}`. ~~Optional[str]~~ |
| `score_weights` | Score names shown in metrics mapped to their weight towards the final weighted score. See [here](/usage/training#metrics) for details. Defaults to `{}`. ~~Dict[str, float]~~ |
| `seed` | The random seed. Defaults to variable `${system.seed}`. ~~int~~ |
| `train_corpus` | Callable that takes the current `nlp` object and yields [`Example`](/api/example) objects. Defaults to [`Corpus`](/api/corpus). ~~Callable[[Language], Iterator[Example]]~~ |
| `vectors` | Model name or path to model containing pretrained word vectors to use, e.g. created with [`init model`](/api/cli#init-model). Defaults to `null`. ~~Optional[str]~~ |
### pretraining {#config-pretraining tag="section,optional"}
@ -153,19 +152,19 @@ This section is optional and defines settings and controls for
[language model pretraining](/usage/training#pretraining). It's used when you
run [`spacy pretrain`](/api/cli#pretrain).
| Name | Type | Description | Default |
| ---------------------------- | --------------------------------------------------- | ----------------------------------------------------------------------------- | --------------------------------------------------- |
| `max_epochs` | int | Maximum number of epochs. | `1000` |
| `min_length` | int | Minimum length of examples. | `5` |
| `max_length` | int | Maximum length of examples. | `500` |
| `dropout` | float | The dropout rate. | `0.2` |
| `n_save_every` | int | Saving frequency. | `null` |
| `batch_size` | int / `Sequence[int]` | The batch size or batch size [schedule](https://thinc.ai/docs/api-schedules). | `3000` |
| `seed` | int | The random seed. | `${system.seed}` |
| `use_pytorch_for_gpu_memory` | bool | Allocate memory via PyTorch. | `${system:use_pytorch_for_gpu_memory}` |
| `tok2vec_model` | str | tok2vec model section in the config. | `"components.tok2vec.model"` |
| `objective` | dict | The pretraining objective. | `{"type": "characters", "n_characters": 4}` |
| `optimizer` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. | [`Adam`](https://thinc.ai/docs/api-optimizers#adam) |
| Name | Description |
| ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------- |
| `max_epochs` | Maximum number of epochs. Defaults to `1000`. ~~int~~ |
| `min_length` | Minimum length of examples. Defaults to `5`. ~~int~~ |
| `max_length` | Maximum length of examples. Defaults to `500`. ~~int~~ |
| `dropout` | The dropout rate. Defaults to `0.2`. ~~float~~ |
| `n_save_every` | Saving frequency. Defaults to `null`. ~~Optional[int]~~ |
| `batch_size` | The batch size or batch size [schedule](https://thinc.ai/docs/api-schedules). Defaults to `3000`. ~~Union[int, Sequence[int]]~~ |
| `seed` | The random seed. Defaults to variable `${system.seed}`. ~~int~~ |
| `use_pytorch_for_gpu_memory` | Allocate memory via PyTorch. Defaults to variable `${system.use_pytorch_for_gpu_memory}`. ~~bool~~ |
| `tok2vec_model` | The model section of the embedding component in the config. Defaults to `"components.tok2vec.model"`. ~~str~~ |
| `objective` | The pretraining objective. Defaults to `{"type": "characters", "n_characters": 4}`. ~~Dict[str, Any]~~ |
| `optimizer` | The optimizer. Defaults to [`Adam`](https://thinc.ai/docs/api-optimizers#adam). ~~Optimizer~~ |
## Training data {#training}
@ -208,8 +207,8 @@ objects to JSON, you can now serialize them directly using the
[`spacy convert`](/api/cli) lets you convert your JSON data to the new `.spacy`
format:
```bash
$ python -m spacy convert ./data.json ./output
```cli
$ python -m spacy convert ./data.json ./output.spacy
```
</Infobox>
@ -313,22 +312,22 @@ to keep track of your settings and hyperparameters and your own
> }
> ```
| Name | Type | Description |
| ------------- | ---------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `text` | str | Raw text. |
| `words` | `List[str]` | List of gold-standard tokens. |
| `lemmas` | `List[str]` | List of lemmas. |
| `spaces` | `List[bool]` | List of boolean values indicating whether the corresponding tokens is followed by a space or not. |
| `tags` | `List[str]` | List of fine-grained [POS tags](/usage/linguistic-features#pos-tagging). |
| `pos` | `List[str]` | List of coarse-grained [POS tags](/usage/linguistic-features#pos-tagging). |
| `morphs` | `List[str]` | List of [morphological features](/usage/linguistic-features#rule-based-morphology). |
| `sent_starts` | `List[bool]` | List of boolean values indicating whether each token is the first of a sentence or not. |
| `deps` | `List[str]` | List of string values indicating the [dependency relation](/usage/linguistic-features#dependency-parse) of a token to its head. |
| `heads` | `List[int]` | List of integer values indicating the dependency head of each token, referring to the absolute index of each token in the text. |
| `entities` | `List[str]` | **Option 1:** List of [BILUO tags](/usage/linguistic-features#accessing-ner) per token of the format `"{action}-{label}"`, or `None` for unannotated tokens. |
| `entities` | `List[Tuple[int, int, str]]` | **Option 2:** List of `"(start, end, label)"` tuples defining all entities in the text. |
| `cats` | `Dict[str, float]` | Dictionary of `label`/`value` pairs indicating how relevant a certain [text category](/api/textcategorizer) is for the text. |
| `links` | `Dict[(int, int), Dict]` | Dictionary of `offset`/`dict` pairs defining [named entity links](/usage/linguistic-features#entity-linking). The character offsets are linked to a dictionary of relevant knowledge base IDs. |
| Name | Description |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `text` | Raw text. ~~str~~ |
| `words` | List of gold-standard tokens. ~~List[str]~~ |
| `lemmas` | List of lemmas. ~~List[str]~~ |
| `spaces` | List of boolean values indicating whether the corresponding tokens is followed by a space or not. ~~List[bool]~~ |
| `tags` | List of fine-grained [POS tags](/usage/linguistic-features#pos-tagging). ~~List[str]~~ |
| `pos` | List of coarse-grained [POS tags](/usage/linguistic-features#pos-tagging). ~~List[str]~~ |
| `morphs` | List of [morphological features](/usage/linguistic-features#rule-based-morphology). ~~List[str]~~ |
| `sent_starts` | List of boolean values indicating whether each token is the first of a sentence or not. ~~List[bool]~~ |
| `deps` | List of string values indicating the [dependency relation](/usage/linguistic-features#dependency-parse) of a token to its head. ~~List[str]~~ |
| `heads` | List of integer values indicating the dependency head of each token, referring to the absolute index of each token in the text. ~~List[int]~~ |
| `entities` | **Option 1:** List of [BILUO tags](/usage/linguistic-features#accessing-ner) per token of the format `"{action}-{label}"`, or `None` for unannotated tokens. ~~List[str]~~ |
| `entities` | **Option 2:** List of `"(start, end, label)"` tuples defining all entities in the text. ~~List[Tuple[int, int, str]]~~ |
| `cats` | Dictionary of `label`/`value` pairs indicating how relevant a certain [text category](/api/textcategorizer) is for the text. ~~Dict[str, float]~~ |
| `links` | Dictionary of `offset`/`dict` pairs defining [named entity links](/usage/linguistic-features#entity-linking). The character offsets are linked to a dictionary of relevant knowledge base IDs. ~~Dict[Tuple[int, int], Dict]~~ |
<Infobox title="Notes and caveats">
@ -372,11 +371,11 @@ example = Example.from_dict(doc, gold_dict)
## Pretraining data {#pretraining}
The [`spacy pretrain`](/api/cli#pretrain) command lets you pretrain the tok2vec
layer of pipeline components from raw text. Raw text can be provided as a
`.jsonl` (newline-delimited JSON) file containing one input text per line
(roughly paragraph length is good). Optionally, custom tokenization can be
provided.
The [`spacy pretrain`](/api/cli#pretrain) command lets you pretrain the
"token-to-vector" embedding layer of pipeline components from raw text. Raw text
can be provided as a `.jsonl` (newline-delimited JSON) file containing one input
text per line (roughly paragraph length is good). Optionally, custom
tokenization can be provided.
> #### Tip: Writing JSONL
>
@ -390,10 +389,10 @@ provided.
> srsly.write_jsonl("/path/to/text.jsonl", data)
> ```
| Key | Type | Description |
| -------- | ---- | ---------------------------------------------------------- |
| `text` | str | The raw input text. Is not required if `tokens` available. |
| `tokens` | list | Optional tokenization, one string per token. |
| Key | Description |
| -------- | --------------------------------------------------------------------- |
| `text` | The raw input text. Is not required if `tokens` is available. ~~str~~ |
| `tokens` | Optional tokenization, one string per token. ~~List[str]~~ |
```json
### Example
@ -406,7 +405,7 @@ provided.
## Lexical data for vocabulary {#vocab-jsonl new="2"}
To populate a model's vocabulary, you can use the
[`spacy init-model`](/api/cli#init-model) command and load in a
[`spacy init model`](/api/cli#init-model) command and load in a
[newline-delimited JSON](http://jsonlines.org/) (JSONL) file containing one
lexical entry per line via the `--jsonl-loc` option. The first line defines the
language and vocabulary settings. All other lines are expected to be JSON
@ -457,3 +456,75 @@ Here's an example of the 20 most frequent lexemes in the English training data:
```json
https://github.com/explosion/spaCy/tree/master/examples/training/vocab-data.jsonl
```
## Model meta {#meta}
The model meta is available as the file `meta.json` and exported automatically
when you save an `nlp` object to disk. Its contents are available as
[`nlp.meta`](/api/language#meta).
<Infobox variant="warning" title="Changed in v3.0">
As of spaCy v3.0, the `meta.json` **isn't** used to construct the language class
and pipeline anymore and only contains meta information for reference and for
creating a Python package with [`spacy package`](/api/cli#package). How to set
up the `nlp` object is now defined in the
[`config.cfg`](/api/data-formats#config), which includes detailed information
about the pipeline components and their model architectures, and all other
settings and hyperparameters used to train the model. It's the **single source
of truth** used for loading a model.
</Infobox>
> #### Example
>
> ```json
> {
> "name": "example_model",
> "lang": "en",
> "version": "1.0.0",
> "spacy_version": ">=3.0.0,<3.1.0",
> "parent_package": "spacy",
> "description": "Example model for spaCy",
> "author": "You",
> "email": "you@example.com",
> "url": "https://example.com",
> "license": "CC BY-SA 3.0",
> "sources": [{ "name": "My Corpus", "license": "MIT" }],
> "vectors": { "width": 0, "vectors": 0, "keys": 0, "name": null },
> "pipeline": ["tok2vec", "ner", "textcat"],
> "labels": {
> "ner": ["PERSON", "ORG", "PRODUCT"],
> "textcat": ["POSITIVE", "NEGATIVE"]
> },
> "accuracy": {
> "ents_f": 82.7300930714,
> "ents_p": 82.135523614,
> "ents_r": 83.3333333333,
> "textcat_score": 88.364323811
> },
> "speed": { "cpu": 7667.8, "gpu": null, "nwords": 10329 },
> "spacy_git_version": "61dfdd9fb"
> }
> ```
| Name | Description |
| ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `lang` | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). Defaults to `"en"`. ~~str~~ |
| `name` | Model name, e.g. `"core_web_sm"`. The final model package name will be `{lang}_{name}`. Defaults to `"model"`. ~~str~~ |
| `version` | Model version. Will be used to version a Python package created with [`spacy package`](/api/cli#package). Defaults to `"0.0.0"`. ~~str~~ |
| `spacy_version` | spaCy version range the model is compatible with. Defaults to the spaCy version used to create the model, up to next minor version, which is the default compatibility for the available [pretrained models](/models). For instance, a model trained with v3.0.0 will have the version range `">=3.0.0,<3.1.0"`. ~~str~~ |
| `parent_package` | Name of the spaCy package. Typically `"spacy"` or `"spacy_nightly"`. Defaults to `"spacy"`. ~~str~~ |
| `description` | Model description. Also used for Python package. Defaults to `""`. ~~str~~ |
| `author` | Model author name. Also used for Python package. Defaults to `""`. ~~str~~ |
| `email` | Model author email. Also used for Python package. Defaults to `""`. ~~str~~ |
| `url` | Model author URL. Also used for Python package. Defaults to `""`. ~~str~~ |
| `license` | Model license. Also used for Python package. Defaults to `""`. ~~str~~ |
| `sources` | Data sources used to train the model. Typically a list of dicts with the keys `"name"`, `"url"`, `"author"` and `"license"`. [See here](https://github.com/explosion/spacy-models/tree/master/meta) for examples. Defaults to `None`. ~~Optional[List[Dict[str, str]]]~~ |
| `vectors` | Information about the word vectors included with the model. Typically a dict with the keys `"width"`, `"vectors"` (number of vectors), `"keys"` and `"name"`. ~~Dict[str, Any]~~ |
| `pipeline` | Names of pipeline component names in the model, in order. Corresponds to [`nlp.pipe_names`](/api/language#pipe_names). Only exists for reference and is not used to create the components. This information is defined in the [`config.cfg`](/api/data-formats#config). Defaults to `[]`. ~~List[str]~~ |
| `labels` | Label schemes of the trained pipeline components, keyed by component name. Corresponds to [`nlp.pipe_labels`](/api/language#pipe_labels). [See here](https://github.com/explosion/spacy-models/tree/master/meta) for examples. Defaults to `{}`. ~~Dict[str, Dict[str, List[str]]]~~ |
| `accuracy` | Training accuracy, added automatically by [`spacy train`](/api/cli#train). Dictionary of [score names](/usage/training#metrics) mapped to scores. Defaults to `{}`. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
| `speed` | Model speed, added automatically by [`spacy train`](/api/cli#train). Typically a dictionary with the keys `"cpu"`, `"gpu"` and `"nwords"` (words per second). Defaults to `{}`. ~~Dict[str, Optional[Union[float, str]]]~~ |
| `spacy_git_version` <Tag variant="new">3</Tag> | Git commit of [`spacy`](https://github.com/explosion/spaCy) used to create model. ~~str~~ |
| other | Any other custom meta information you want to add. The data is preserved in [`nlp.meta`](/api/language#meta). ~~Any~~ |

78
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@ -44,18 +44,18 @@ A pattern added to the `DependencyMatcher` consists of a list of dictionaries,
with each dictionary describing a node to match. Each pattern should have the
following top-level keys:
| Name | Type | Description |
| --------- | ---- | --------------------------------------------------------------------------------------------------------------------------- |
| `PATTERN` | dict | The token attributes to match in the same format as patterns provided to the regular token-based [`Matcher`](/api/matcher). |
| `SPEC` | dict | The relationships of the nodes in the subtree that should be matched. |
| Name | Description |
| --------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `PATTERN` | The token attributes to match in the same format as patterns provided to the regular token-based [`Matcher`](/api/matcher). ~~Dict[str, Any]~~ |
| `SPEC` | The relationships of the nodes in the subtree that should be matched. ~~Dict[str, str]~~ |
The `SPEC` includes the following fields:
| Name | Type | Description |
| ------------ | ---- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `NODE_NAME` | str | A unique name for this node to refer to it in other specs. |
| `NBOR_RELOP` | str | A [Semgrex](https://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/semgraph/semgrex/SemgrexPattern.html) operator that describes how the two nodes are related. |
| `NBOR_NAME` | str | The unique name of the node that this node is connected to. |
| Name | Description |
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `NODE_NAME` | A unique name for this node to refer to it in other specs. ~~str~~ |
| `NBOR_RELOP` | A [Semgrex](https://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/semgraph/semgrex/SemgrexPattern.html) operator that describes how the two nodes are related. ~~str~~ |
| `NBOR_NAME` | The unique name of the node that this node is connected to. ~~str~~ |
## DependencyMatcher.\_\_init\_\_ {#init tag="method"}
@ -68,9 +68,9 @@ Create a rule-based `DependencyMatcher`.
> matcher = DependencyMatcher(nlp.vocab)
> ```
| Name | Type | Description |
| ------- | ------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. |
| Name | Description |
| ------- | ----------------------------------------------------------------------------------------------------- |
| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ |
## DependencyMatcher.\_\call\_\_ {#call tag="method"}
@ -79,9 +79,9 @@ Find all token sequences matching the supplied patterns on the `Doc` or `Span`.
> #### Example
>
> ```python
> from spacy.matcher import Matcher
> from spacy.matcher import DependencyMatcher
>
> matcher = Matcher(nlp.vocab)
> matcher = DependencyMatcher(nlp.vocab)
> pattern = [
> {"SPEC": {"NODE_NAME": "founded"}, "PATTERN": {"ORTH": "founded"}},
> {"SPEC": {"NODE_NAME": "founder", "NBOR_RELOP": ">", "NBOR_NAME": "founded"}, "PATTERN": {"DEP": "nsubj"}},
@ -91,10 +91,10 @@ Find all token sequences matching the supplied patterns on the `Doc` or `Span`.
> matches = matcher(doc)
> ```
| Name | Type | Description |
| ----------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `doclike` | `Doc`/`Span` | The `Doc` or `Span` to match over. |
| **RETURNS** | list | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doclike` | The `Doc` or `Span` to match over. ~~Union[Doc, Span]~~ |
| **RETURNS** | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. ~~List[Tuple[int, int, int]]~~ |
## DependencyMatcher.\_\_len\_\_ {#len tag="method"}
@ -115,9 +115,9 @@ number of individual patterns.
> assert len(matcher) == 1
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------- |
| **RETURNS** | int | The number of rules. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The number of rules. ~~int~~ |
## DependencyMatcher.\_\_contains\_\_ {#contains tag="method"}
@ -132,10 +132,10 @@ Check whether the matcher contains rules for a match ID.
> assert "Rule" in matcher
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------------------------- |
| `key` | str | The match ID. |
| **RETURNS** | bool | Whether the matcher contains rules for this match ID. |
| Name | Description |
| ----------- | -------------------------------------------------------------- |
| `key` | The match ID. ~~str~~ |
| **RETURNS** | Whether the matcher contains rules for this match ID. ~~bool~~ |
## DependencyMatcher.add {#add tag="method"}
@ -151,16 +151,16 @@ will be overwritten.
> def on_match(matcher, doc, id, matches):
> print('Matched!', matches)
>
> matcher = Matcher(nlp.vocab)
> matcher = DependencyMatcher(nlp.vocab)
> matcher.add("TEST_PATTERNS", patterns)
> ```
| Name | Type | Description |
| -------------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `patterns` | list | Match pattern. A pattern consists of a list of dicts, where each dict describes a token. |
| _keyword-only_ | | |
| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
| Name | Description |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `match_id` | An ID for the thing you're matching. ~~str~~ |
| `patterns` | list | Match pattern. A pattern consists of a list of dicts, where each dict describes a `"PATTERN"` and `"SPEC"`. ~~List[List[Dict[str, dict]]]~~ |
| _keyword-only_ | | |
| `on_match` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. ~~Optional[Callable[[Matcher, Doc, int, List[tuple], Any]]~~ |
## DependencyMatcher.remove {#remove tag="method"}
@ -176,9 +176,9 @@ exist.
> assert "Rule" not in matcher
> ```
| Name | Type | Description |
| ----- | ---- | ------------------------- |
| `key` | str | The ID of the match rule. |
| Name | Description |
| ----- | --------------------------------- |
| `key` | The ID of the match rule. ~~str~~ |
## DependencyMatcher.get {#get tag="method"}
@ -192,7 +192,7 @@ Retrieve the pattern stored for a key. Returns the rule as an
> on_match, patterns = matcher.get("Rule")
> ```
| Name | Type | Description |
| ----------- | ----- | --------------------------------------------- |
| `key` | str | The ID of the match rule. |
| **RETURNS** | tuple | The rule, as an `(on_match, patterns)` tuple. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------- |
| `key` | The ID of the match rule. ~~str~~ |
| **RETURNS** | The rule, as an `(on_match, patterns)` tuple. ~~Tuple[Optional[Callable], List[List[dict]]]~~ |

190
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@ -48,13 +48,13 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("parser", config=config)
> ```
| Setting | Type | Description | Default |
| ----------------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------- |
| `moves` | `List[str]` | A list of transition names. Inferred from the data if not provided. | `None` |
| `update_with_oracle_cut_size` | int | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. | `100` |
| `learn_tokens` | bool | Whether to learn to merge subtokens that are split relative to the gold standard. Experimental. | `False` |
| `min_action_freq` | int | The minimum frequency of labelled actions to retain. Rarer labelled actions have their label backed-off to "dep". While this primarily affects the label accuracy, it can also affect the attachment structure, as the labels are used to represent the pseudo-projectivity transformation. | `30` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TransitionBasedParser](/api/architectures#TransitionBasedParser) |
| Setting | Description |
| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `moves` | A list of transition names. Inferred from the data if not provided. Defaults to `None`. ~~Optional[List[str]]~~ |
| `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. Defaults to `100`. ~~int~~ |
| `learn_tokens` | Whether to learn to merge subtokens that are split relative to the gold standard. Experimental. Defaults to `False`. ~~bool~~ |
| `min_action_freq` | The minimum frequency of labelled actions to retain. Rarer labelled actions have their label backed-off to "dep". While this primarily affects the label accuracy, it can also affect the attachment structure, as the labels are used to represent the pseudo-projectivity transformation. Defaults to `30`. ~~int~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [TransitionBasedParser](/api/architectures#TransitionBasedParser). ~~Model[List[Doc], List[Floats2d]]~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/dep_parser.pyx
@ -81,16 +81,16 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| ----------------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| `moves` | `List[str]` | A list of transition names. Inferred from the data if not provided. |
| _keyword-only_ | | |
| `update_with_oracle_cut_size` | int | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. `100` is a good default. |
| `learn_tokens` | bool | Whether to learn to merge subtokens that are split relative to the gold standard. Experimental. |
| `min_action_freq` | int | The minimum frequency of labelled actions to retain. Rarer labelled actions have their label backed-off to "dep". While this primarily affects the label accuracy, it can also affect the attachment structure, as the labels are used to represent the pseudo-projectivity transformation. |
| Name | Description |
| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| `moves` | A list of transition names. Inferred from the data if not provided. ~~Optional[List[str]]~~ |
| _keyword-only_ | |
| `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. `100` is a good default. ~~int~~ |
| `learn_tokens` | Whether to learn to merge subtokens that are split relative to the gold standard. Experimental. ~~bool~~ |
| `min_action_freq` | The minimum frequency of labelled actions to retain. Rarer labelled actions have their label backed-off to "dep". While this primarily affects the label accuracy, it can also affect the attachment structure, as the labels are used to represent the pseudo-projectivity transformation. ~~int~~ |
## DependencyParser.\_\_call\_\_ {#call tag="method"}
@ -111,10 +111,10 @@ and all pipeline components are applied to the `Doc` in order. Both
> processed = parser(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## DependencyParser.pipe {#pipe tag="method"}
@ -133,12 +133,12 @@ applied to the `Doc` in order. Both [`__call__`](/api/dependencyparser#call) and
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `docs` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## DependencyParser.begin_training {#begin_training tag="method"}
@ -158,13 +158,13 @@ setting up the label scheme based on the data.
> optimizer = parser.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/dependencyparser#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## DependencyParser.predict {#predict tag="method"}
@ -178,10 +178,10 @@ modifying them.
> scores = parser.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | ------------------- | ---------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | `syntax.StateClass` | A helper class for the parse state (internal). |
| Name | Description |
| ----------- | ------------------------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | A helper class for the parse state (internal). ~~StateClass~~ |
## DependencyParser.set_annotations {#set_annotations tag="method"}
@ -195,10 +195,10 @@ Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
> parser.set_annotations([doc1, doc2], scores)
> ```
| Name | Type | Description |
| -------- | ------------------- | ---------------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | `syntax.StateClass` | The scores to set, produced by `DependencyParser.predict`. |
| Name | Description |
| -------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `DependencyParser.predict`. Returns an internal helper class for the parse state. ~~List[StateClass]~~ |
## DependencyParser.update {#update tag="method"}
@ -214,15 +214,15 @@ model. Delegates to [`predict`](/api/dependencyparser#predict) and
> losses = parser.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/dependencyparser#set_annotations). |
| `sgd` | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## DependencyParser.get_loss {#get_loss tag="method"}
@ -237,11 +237,11 @@ predicted scores.
> loss, d_loss = parser.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | `syntax.StateClass` | Scores representing the model's predictions. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------- |
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
| `scores` | Scores representing the model's predictions. ~~StateClass~~ |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
## DependencyParser.score {#score tag="method" new="3"}
@ -253,10 +253,10 @@ Score a batch of examples.
> scores = parser.score(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans) and [`Scorer.score_deps`](/api/scorer#score_deps). |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | The examples to score. ~~Iterable[Example]~~ |
| **RETURNS** | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans) and [`Scorer.score_deps`](/api/scorer#score_deps). ~~Dict[str, Union[float, Dict[str, float]]]~~ |
## DependencyParser.create_optimizer {#create_optimizer tag="method"}
@ -270,9 +270,9 @@ component.
> optimizer = parser.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## DependencyParser.use_params {#use_params tag="method, contextmanager"}
@ -287,9 +287,9 @@ context, the original parameters are restored.
> parser.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## DependencyParser.add_label {#add_label tag="method"}
@ -302,10 +302,10 @@ Add a new label to the pipe.
> parser.add_label("MY_LABEL")
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------------------------- |
| `label` | str | The label to add. |
| **RETURNS** | int | `0` if the label is already present, otherwise `1`. |
| Name | Description |
| ----------- | ----------------------------------------------------------- |
| `label` | The label to add. ~~str~~ |
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
## DependencyParser.to_disk {#to_disk tag="method"}
@ -318,11 +318,11 @@ Serialize the pipe to disk.
> parser.to_disk("/path/to/parser")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## DependencyParser.from_disk {#from_disk tag="method"}
@ -335,12 +335,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> parser.from_disk("/path/to/parser")
> ```
| Name | Type | Description |
| -------------- | ------------------ | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `DependencyParser` | The modified `DependencyParser` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `DependencyParser` object. ~~DependencyParser~~ |
## DependencyParser.to_bytes {#to_bytes tag="method"}
@ -353,11 +353,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `DependencyParser` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `DependencyParser` object. ~~bytes~~ |
## DependencyParser.from_bytes {#from_bytes tag="method"}
@ -371,12 +371,12 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> parser.from_bytes(parser_bytes)
> ```
| Name | Type | Description |
| -------------- | ------------------ | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `DependencyParser` | The `DependencyParser` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `DependencyParser` object. ~~DependencyParser~~ |
## DependencyParser.labels {#labels tag="property"}
@ -389,9 +389,9 @@ The labels currently added to the component.
> assert "MY_LABEL" in parser.labels
> ```
| Name | Type | Description |
| ----------- | ----- | ---------------------------------- |
| **RETURNS** | tuple | The labels added to the component. |
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
## Serialization fields {#serialization-fields}

319
website/docs/api/doc.md
View File

@ -30,11 +30,11 @@ Construct a `Doc` object. The most common way to get a `Doc` object is via the
> doc = Doc(nlp.vocab, words=words, spaces=spaces)
> ```
| Name | Type | Description |
| -------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | A storage container for lexical types. |
| `words` | iterable | A list of strings to add to the container. |
| `spaces` | iterable | A list of boolean values indicating whether each word has a subsequent space. Must have the same length as `words`, if specified. Defaults to a sequence of `True`. |
| Name | Description |
| -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | A storage container for lexical types. ~~Vocab~~ |
| `words` | A list of strings to add to the container. ~~Optional[List[str]]~~ |
| `spaces` | A list of boolean values indicating whether each word has a subsequent space. Must have the same length as `words`, if specified. Defaults to a sequence of `True`. ~~Optional[List[bool]]~~ |
## Doc.\_\_getitem\_\_ {#getitem tag="method"}
@ -52,10 +52,10 @@ Negative indexing is supported, and follows the usual Python semantics, i.e.
> assert span.text == "it back"
> ```
| Name | Type | Description |
| ----------- | ------- | ----------------------- |
| `i` | int | The index of the token. |
| **RETURNS** | `Token` | The token at `doc[i]`. |
| Name | Description |
| ----------- | -------------------------------- |
| `i` | The index of the token. ~~int~~ |
| **RETURNS** | The token at `doc[i]`. ~~Token~~ |
Get a [`Span`](/api/span) object, starting at position `start` (token index) and
ending at position `end` (token index). For instance, `doc[2:5]` produces a span
@ -64,10 +64,10 @@ are not supported, as `Span` objects must be contiguous (cannot have gaps). You
can use negative indices and open-ended ranges, which have their normal Python
semantics.
| Name | Type | Description |
| ----------- | ------ | --------------------------------- |
| `start_end` | tuple | The slice of the document to get. |
| **RETURNS** | `Span` | The span at `doc[start:end]`. |
| Name | Description |
| ----------- | ----------------------------------------------------- |
| `start_end` | The slice of the document to get. ~~Tuple[int, int]~~ |
| **RETURNS** | The span at `doc[start:end]`. ~~Span~~ |
## Doc.\_\_iter\_\_ {#iter tag="method"}
@ -85,9 +85,9 @@ main way annotations are accessed from Python. If faster-than-Python speeds are
required, you can instead access the annotations as a numpy array, or access the
underlying C data directly from Cython.
| Name | Type | Description |
| ---------- | ------- | ----------------- |
| **YIELDS** | `Token` | A `Token` object. |
| Name | Description |
| ---------- | --------------------------- |
| **YIELDS** | A `Token` object. ~~Token~~ |
## Doc.\_\_len\_\_ {#len tag="method"}
@ -100,9 +100,9 @@ Get the number of tokens in the document.
> assert len(doc) == 7
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------- |
| **RETURNS** | int | The number of tokens in the document. |
| Name | Description |
| ----------- | --------------------------------------------- |
| **RETURNS** | The number of tokens in the document. ~~int~~ |
## Doc.set_extension {#set_extension tag="classmethod" new="2"}
@ -120,14 +120,14 @@ details, see the documentation on
> assert doc._.has_city
> ```
| Name | Type | Description |
| --------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | Name of the attribute to set by the extension. For example, `"my_attr"` will be available as `doc._.my_attr`. |
| `default` | - | Optional default value of the attribute if no getter or method is defined. |
| `method` | callable | Set a custom method on the object, for example `doc._.compare(other_doc)`. |
| `getter` | callable | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute. |
| `setter` | callable | Setter function that takes the `Doc` and a value, and modifies the object. Is called when the user writes to the `Doc._` attribute. |
| `force` | bool | Force overwriting existing attribute. |
| Name | Description |
| --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the attribute to set by the extension. For example, `"my_attr"` will be available as `doc._.my_attr`. ~~str~~ |
| `default` | Optional default value of the attribute if no getter or method is defined. ~~Optional[Any]~~ |
| `method` | Set a custom method on the object, for example `doc._.compare(other_doc)`. ~~Optional[Callable[[Doc, ...], Any]]~~ |
| `getter` | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute. ~~Optional[Callable[[Doc], Any]]~~ |
| `setter` | Setter function that takes the `Doc` and a value, and modifies the object. Is called when the user writes to the `Doc._` attribute. ~~Optional[Callable[[Doc, Any], None]]~~ |
| `force` | Force overwriting existing attribute. ~~bool~~ |
## Doc.get_extension {#get_extension tag="classmethod" new="2"}
@ -144,10 +144,10 @@ Look up a previously registered extension by name. Returns a 4-tuple
> assert extension == (False, None, None, None)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------- |
| `name` | str | Name of the extension. |
| **RETURNS** | tuple | A `(default, method, getter, setter)` tuple of the extension. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Doc.has_extension {#has_extension tag="classmethod" new="2"}
@ -161,10 +161,10 @@ Check whether an extension has been registered on the `Doc` class.
> assert Doc.has_extension("has_city")
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------------ |
| `name` | str | Name of the extension to check. |
| **RETURNS** | bool | Whether the extension has been registered. |
| Name | Description |
| ----------- | --------------------------------------------------- |
| `name` | Name of the extension to check. ~~str~~ |
| **RETURNS** | Whether the extension has been registered. ~~bool~~ |
## Doc.remove_extension {#remove_extension tag="classmethod" new="2.0.12"}
@ -179,10 +179,10 @@ Remove a previously registered extension.
> assert not Doc.has_extension("has_city")
> ```
| Name | Type | Description |
| ----------- | ----- | --------------------------------------------------------------------- |
| `name` | str | Name of the extension. |
| **RETURNS** | tuple | A `(default, method, getter, setter)` tuple of the removed extension. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the removed extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Doc.char_span {#char_span tag="method" new="2"}
@ -197,14 +197,14 @@ the character indices don't map to a valid span.
> assert span.text == "New York"
> ```
| Name | Type | Description |
| ------------------------------------ | ---------------------------------------- | --------------------------------------------------------------------- |
| `start` | int | The index of the first character of the span. |
| `end` | int | The index of the last character after the span. |
| `label` | uint64 / str | A label to attach to the span, e.g. for named entities. |
| `kb_id` <Tag variant="new">2.2</Tag> | uint64 / str | An ID from a knowledge base to capture the meaning of a named entity. |
| `vector` | `numpy.ndarray[ndim=1, dtype="float32"]` | A meaning representation of the span. |
| **RETURNS** | `Span` | The newly constructed object or `None`. |
| Name | Description |
| ------------------------------------ | ----------------------------------------------------------------------------------------- |
| `start` | The index of the first character of the span. ~~int~~ |
| `end` | The index of the last character after the span. ~int~~ |
| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ |
| `kb_id` <Tag variant="new">2.2</Tag> | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ |
## Doc.similarity {#similarity tag="method" model="vectors"}
@ -221,10 +221,10 @@ using an average of word vectors.
> assert apples_oranges == oranges_apples
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------------------------------------------------------- |
| `other` | - | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
| **RETURNS** | float | A scalar similarity score. Higher is more similar. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------- |
| `other` | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. ~~Union[Doc, Span, Token, Lexeme]~~ |
| **RETURNS** | A scalar similarity score. Higher is more similar. ~~float~~ |
## Doc.count_by {#count_by tag="method"}
@ -237,15 +237,15 @@ attribute ID.
> ```python
> from spacy.attrs import ORTH
> doc = nlp("apple apple orange banana")
> assert doc.count_by(ORTH) == {7024L: 1, 119552L: 1, 2087L: 2}
> assert doc.count_by(ORTH) == {7024: 1, 119552: 1, 2087: 2}
> doc.to_array([ORTH])
> # array([[11880], [11880], [7561], [12800]])
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------------------- |
| `attr_id` | int | The attribute ID |
| **RETURNS** | dict | A dictionary mapping attributes to integer counts. |
| Name | Description |
| ----------- | --------------------------------------------------------------------- |
| `attr_id` | The attribute ID. ~~int~~ |
| **RETURNS** | A dictionary mapping attributes to integer counts. ~~Dict[int, int]~~ |
## Doc.get_lca_matrix {#get_lca_matrix tag="method"}
@ -261,9 +261,9 @@ ancestor is found, e.g. if span excludes a necessary ancestor.
> # array([[0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 2, 3], [1, 1, 3, 3]], dtype=int32)
> ```
| Name | Type | Description |
| ----------- | -------------------------------------- | ----------------------------------------------- |
| **RETURNS** | `numpy.ndarray[ndim=2, dtype="int32"]` | The lowest common ancestor matrix of the `Doc`. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------- |
| **RETURNS** | The lowest common ancestor matrix of the `Doc`. ~~numpy.ndarray[ndim=2, dtype=int32]~~ |
## Doc.to_array {#to_array tag="method"}
@ -288,10 +288,10 @@ Returns a 2D array with one row per token and one column per attribute (when
> np_array = doc.to_array("POS")
> ```
| Name | Type | Description |
| ----------- | ---------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
| `attr_ids` | list or int or string | A list of attributes (int IDs or string names) or a single attribute (int ID or string name) |
| **RETURNS** | `numpy.ndarray[ndim=2, dtype="uint64"]` or `numpy.ndarray[ndim=1, dtype="uint64"]` | The exported attributes as a numpy array. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| `attr_ids` | A list of attributes (int IDs or string names) or a single attribute (int ID or string name). ~~Union[int, str, List[Union[int, str]]]~~ |
| **RETURNS** | The exported attributes as a numpy array. ~~Union[numpy.ndarray[ndim=2, dtype=uint64], numpy.ndarray[ndim=1, dtype=uint64]]~~ |
## Doc.from_array {#from_array tag="method"}
@ -310,14 +310,14 @@ array of attributes.
> assert doc[0].pos_ == doc2[0].pos_
> ```
| Name | Type | Description |
| ----------- | -------------------------------------- | ------------------------------------------------------------------------- |
| `attrs` | list | A list of attribute ID ints. |
| `array` | `numpy.ndarray[ndim=2, dtype="int32"]` | The attribute values to load. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Doc` | Itself. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------- |
| `attrs` | A list of attribute ID ints. ~~List[int]~~ |
| `array` | The attribute values to load. ~~numpy.ndarray[ndim=2, dtype=int32]~~ |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Doc` itself. ~~Doc~~ |
## Doc.from_docs {#from_docs tag="staticmethod"}
## Doc.from_docs {#from_docs tag="staticmethod" new="3"}
Concatenate multiple `Doc` objects to form a new one. Raises an error if the
`Doc` objects do not all share the same `Vocab`.
@ -337,12 +337,12 @@ Concatenate multiple `Doc` objects to form a new one. Raises an error if the
> [str(ent) for doc in docs for ent in doc.ents]
> ```
| Name | Type | Description |
| ------------------- | ----- | ----------------------------------------------------------------------------------------------- |
| `docs` | list | A list of `Doc` objects. |
| `ensure_whitespace` | bool | Insert a space between two adjacent docs whenever the first doc does not end in whitespace. |
| `attrs` | list | Optional list of attribute ID ints or attribute name strings. |
| **RETURNS** | `Doc` | The new `Doc` object that is containing the other docs or `None`, if `docs` is empty or `None`. |
| Name | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------- |
| `docs` | A list of `Doc` objects. ~~List[Doc]~~ |
| `ensure_whitespace` | Insert a space between two adjacent docs whenever the first doc does not end in whitespace. ~~bool~~ |
| `attrs` | Optional list of attribute ID ints or attribute name strings. ~~Optional[List[Union[str, int]]]~~ |
| **RETURNS** | The new `Doc` object that is containing the other docs or `None`, if `docs` is empty or `None`. ~~Optional[Doc]~~ |
## Doc.to_disk {#to_disk tag="method" new="2"}
@ -354,11 +354,11 @@ Save the current state to a directory.
> doc.to_disk("/path/to/doc")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Doc.from_disk {#from_disk tag="method" new="2"}
@ -372,12 +372,12 @@ Loads state from a directory. Modifies the object in place and returns it.
> doc = Doc(Vocab()).from_disk("/path/to/doc")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Doc` | The modified `Doc` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Doc` object. ~~Doc~~ |
## Doc.to_bytes {#to_bytes tag="method"}
@ -390,11 +390,11 @@ Serialize, i.e. export the document contents to a binary string.
> doc_bytes = doc.to_bytes()
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | A losslessly serialized copy of the `Doc`, including all annotations. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | A losslessly serialized copy of the `Doc`, including all annotations. ~~bytes~~ |
## Doc.from_bytes {#from_bytes tag="method"}
@ -410,12 +410,12 @@ Deserialize, i.e. import the document contents from a binary string.
> assert doc.text == doc2.text
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `data` | bytes | The string to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Doc` | The `Doc` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `data` | The string to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Doc` object. ~~Doc~~ |
## Doc.retokenize {#retokenize tag="contextmanager" new="2.1"}
@ -433,9 +433,9 @@ invalidated, although they may accidentally continue to work.
> retokenizer.merge(doc[0:2])
> ```
| Name | Type | Description |
| ----------- | ------------- | ---------------- |
| **RETURNS** | `Retokenizer` | The retokenizer. |
| Name | Description |
| ----------- | -------------------------------- |
| **RETURNS** | The retokenizer. ~~Retokenizer~~ |
### Retokenizer.merge {#retokenizer.merge tag="method"}
@ -454,10 +454,10 @@ dictionary mapping attribute names to values as the `"_"` key.
> retokenizer.merge(doc[2:4], attrs=attrs)
> ```
| Name | Type | Description |
| ------- | ------ | -------------------------------------- |
| `span` | `Span` | The span to merge. |
| `attrs` | dict | Attributes to set on the merged token. |
| Name | Description |
| ------- | --------------------------------------------------------------------- |
| `span` | The span to merge. ~~Span~~ |
| `attrs` | Attributes to set on the merged token. ~~Dict[Union[str, int], Any]~~ |
### Retokenizer.split {#retokenizer.split tag="method"}
@ -488,33 +488,12 @@ underlying lexeme (if they're context-independent lexical attributes like
> retokenizer.split(doc[3], ["New", "York"], heads=heads, attrs=attrs)
> ```
| Name | Type | Description |
| ------- | ------- | ----------------------------------------------------------------------------------------------------------- |
| `token` | `Token` | The token to split. |
| `orths` | list | The verbatim text of the split tokens. Needs to match the text of the original token. |
| `heads` | list | List of `token` or `(token, subtoken)` tuples specifying the tokens to attach the newly split subtokens to. |
| `attrs` | dict | Attributes to set on all split tokens. Attribute names mapped to list of per-token attribute values. |
## Doc.merge {#merge tag="method"}
Retokenize the document, such that the span at `doc.text[start_idx : end_idx]`
is merged into a single token. If `start_idx` and `end_idx` do not mark start
and end token boundaries, the document remains unchanged.
> #### Example
>
> ```python
> doc = nlp("Los Angeles start.")
> doc.merge(0, len("Los Angeles"), "NNP", "Los Angeles", "GPE")
> assert [t.text for t in doc] == ["Los Angeles", "start", "."]
> ```
| Name | Type | Description |
| -------------- | ------- | ------------------------------------------------------------------------------------------------------------------------- |
| `start_idx` | int | The character index of the start of the slice to merge. |
| `end_idx` | int | The character index after the end of the slice to merge. |
| `**attributes` | - | Attributes to assign to the merged token. By default, attributes are inherited from the syntactic root token of the span. |
| **RETURNS** | `Token` | The newly merged token, or `None` if the start and end indices did not fall at token boundaries |
| Name | Description |
| ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `token` | The token to split. ~~Token~~ |
| `orths` | The verbatim text of the split tokens. Needs to match the text of the original token. ~~List[str]~~ |
| `heads` | List of `token` or `(token, subtoken)` tuples specifying the tokens to attach the newly split subtokens to. ~~List[Union[Token, Tuple[Token, int]]]~~ |
| `attrs` | Attributes to set on all split tokens. Attribute names mapped to list of per-token attribute values. ~~Dict[Union[str, int], List[Any]]~~ |
## Doc.ents {#ents tag="property" model="NER"}
@ -531,9 +510,9 @@ objects, if the entity recognizer has been applied.
> assert ents[0].text == "Mr. Best"
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------ |
| **RETURNS** | tuple | Entities in the document, one `Span` per entity. |
| Name | Description |
| ----------- | --------------------------------------------------------------------- |
| **RETURNS** | Entities in the document, one `Span` per entity. ~~Tuple[Span, ...]~~ |
## Doc.noun_chunks {#noun_chunks tag="property" model="parser"}
@ -552,9 +531,9 @@ relative clauses.
> assert chunks[1].text == "another phrase"
> ```
| Name | Type | Description |
| ---------- | ------ | ---------------------------- |
| **YIELDS** | `Span` | Noun chunks in the document. |
| Name | Description |
| ---------- | ------------------------------------- |
| **YIELDS** | Noun chunks in the document. ~~Span~~ |
## Doc.sents {#sents tag="property" model="parser"}
@ -572,9 +551,9 @@ will be unavailable.
> assert [s.root.text for s in sents] == ["is", "'s"]
> ```
| Name | Type | Description |
| ---------- | ------ | -------------------------- |
| **YIELDS** | `Span` | Sentences in the document. |
| Name | Description |
| ---------- | ----------------------------------- |
| **YIELDS** | Sentences in the document. ~~Span~~ |
## Doc.has_vector {#has_vector tag="property" model="vectors"}
@ -587,9 +566,9 @@ A boolean value indicating whether a word vector is associated with the object.
> assert doc.has_vector
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------------------ |
| **RETURNS** | bool | Whether the document has a vector data attached. |
| Name | Description |
| ----------- | --------------------------------------------------------- |
| **RETURNS** | Whether the document has a vector data attached. ~~bool~~ |
## Doc.vector {#vector tag="property" model="vectors"}
@ -604,9 +583,9 @@ vectors.
> assert doc.vector.shape == (300,)
> ```
| Name | Type | Description |
| ----------- | ---------------------------------------- | ------------------------------------------------------- |
| **RETURNS** | `numpy.ndarray[ndim=1, dtype="float32"]` | A 1D numpy array representing the document's semantics. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------- |
| **RETURNS** | A 1-dimensional array representing the document's vector. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Doc.vector_norm {#vector_norm tag="property" model="vectors"}
@ -622,32 +601,32 @@ The L2 norm of the document's vector representation.
> assert doc1.vector_norm != doc2.vector_norm
> ```
| Name | Type | Description |
| ----------- | ----- | ----------------------------------------- |
| **RETURNS** | float | The L2 norm of the vector representation. |
| Name | Description |
| ----------- | --------------------------------------------------- |
| **RETURNS** | The L2 norm of the vector representation. ~~float~~ |
## Attributes {#attributes}
| Name | Type | Description |
| --------------------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `text` | str | A string representation of the document text. |
| `text_with_ws` | str | An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`. |
| `mem` | `Pool` | The document's local memory heap, for all C data it owns. |
| `vocab` | `Vocab` | The store of lexical types. |
| `tensor` <Tag variant="new">2</Tag> | `ndarray` | Container for dense vector representations. |
| `cats` <Tag variant="new">2</Tag> | dict | Maps a label to a score for categories applied to the document. The label is a string and the score should be a float. |
| `user_data` | - | A generic storage area, for user custom data. |
| `lang` <Tag variant="new">2.1</Tag> | int | Language of the document's vocabulary. |
| `lang_` <Tag variant="new">2.1</Tag> | str | Language of the document's vocabulary. |
| `is_tagged` | bool | A flag indicating that the document has been part-of-speech tagged. Returns `True` if the `Doc` is empty. |
| `is_parsed` | bool | A flag indicating that the document has been syntactically parsed. Returns `True` if the `Doc` is empty. |
| `is_sentenced` | bool | A flag indicating that sentence boundaries have been applied to the document. Returns `True` if the `Doc` is empty. |
| `is_nered` <Tag variant="new">2.1</Tag> | bool | A flag indicating that named entities have been set. Will return `True` if the `Doc` is empty, or if _any_ of the tokens has an entity tag set, even if the others are unknown. |
| `sentiment` | float | The document's positivity/negativity score, if available. |
| `user_hooks` | dict | A dictionary that allows customization of the `Doc`'s properties. |
| `user_token_hooks` | dict | A dictionary that allows customization of properties of `Token` children. |
| `user_span_hooks` | dict | A dictionary that allows customization of properties of `Span` children. |
| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |
| Name | Description |
| --------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `text` | A string representation of the document text. ~~str~~ |
| `text_with_ws` | An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`. ~~str~~ |
| `mem` | The document's local memory heap, for all C data it owns. ~~cymem.Pool~~ |
| `vocab` | The store of lexical types. ~~Vocab~~ |
| `tensor` <Tag variant="new">2</Tag> | Container for dense vector representations. ~~numpy.ndarray~~ |
| `cats` <Tag variant="new">2</Tag> | Maps a label to a score for categories applied to the document. The label is a string and the score should be a float. ~~Dict[str, float]~~ |
| `user_data` | A generic storage area, for user custom data. ~~Dict[str, Any]~~ |
| `lang` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~int~~ |
| `lang_` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~str~~ |
| `is_tagged` | A flag indicating that the document has been part-of-speech tagged. Returns `True` if the `Doc` is empty. ~~bool~~ |
| `is_parsed` | A flag indicating that the document has been syntactically parsed. Returns `True` if the `Doc` is empty. ~~bool~~ |
| `is_sentenced` | A flag indicating that sentence boundaries have been applied to the document. Returns `True` if the `Doc` is empty. ~~bool~~ |
| `is_nered` <Tag variant="new">2.1</Tag> | A flag indicating that named entities have been set. Will return `True` if the `Doc` is empty, or if _any_ of the tokens has an entity tag set, even if the others are unknown. ~~bool~~ |
| `sentiment` | The document's positivity/negativity score, if available. ~~float~~ |
| `user_hooks` | A dictionary that allows customization of the `Doc`'s properties. ~~Dict[str, Callable]~~ |
| `user_token_hooks` | A dictionary that allows customization of properties of `Token` children. ~~Dict[str, Callable]~~ |
| `user_span_hooks` | A dictionary that allows customization of properties of `Span` children. ~~Dict[str, Callable]~~ |
| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ |
## Serialization fields {#serialization-fields}

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@ -44,11 +44,11 @@ Create a `DocBin` object to hold serialized annotations.
> doc_bin = DocBin(attrs=["ENT_IOB", "ENT_TYPE"])
> ```
| Argument | Type | Description |
| ----------------- | --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `attrs` | `Iterable[str]` | List of attributes to serialize. `ORTH` (hash of token text) and `SPACY` (whether the token is followed by whitespace) are always serialized, so they're not required. Defaults to `("ORTH", "TAG", "HEAD", "DEP", "ENT_IOB", "ENT_TYPE", "ENT_KB_ID", "LEMMA", "MORPH", "POS")`. |
| `store_user_data` | bool | Whether to include the `Doc.user_data` and the values of custom extension attributes. Defaults to `False`. |
| `docs` | `Iterable[Doc]` | `Doc` objects to add on initialization. |
| Argument | Description |
| ----------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `attrs` | List of attributes to serialize. `ORTH` (hash of token text) and `SPACY` (whether the token is followed by whitespace) are always serialized, so they're not required. Defaults to `("ORTH", "TAG", "HEAD", "DEP", "ENT_IOB", "ENT_TYPE", "ENT_KB_ID", "LEMMA", "MORPH", "POS")`. ~~Iterable[str]~~ |
| `store_user_data` | Whether to include the `Doc.user_data` and the values of custom extension attributes. Defaults to `False`. ~~bool~~ |
| `docs` | `Doc` objects to add on initialization. ~~Iterable[Doc]~~ |
## DocBin.\_\len\_\_ {#len tag="method"}
@ -63,9 +63,9 @@ Get the number of `Doc` objects that were added to the `DocBin`.
> assert len(doc_bin) == 1
> ```
| Argument | Type | Description |
| ----------- | ---- | ------------------------------------------- |
| **RETURNS** | int | The number of `Doc`s added to the `DocBin`. |
| Argument | Description |
| ----------- | --------------------------------------------------- |
| **RETURNS** | The number of `Doc`s added to the `DocBin`. ~~int~~ |
## DocBin.add {#add tag="method"}
@ -79,9 +79,9 @@ Add a `Doc`'s annotations to the `DocBin` for serialization.
> doc_bin.add(doc)
> ```
| Argument | Type | Description |
| -------- | ----- | ------------------------ |
| `doc` | `Doc` | The `Doc` object to add. |
| Argument | Description |
| -------- | -------------------------------- |
| `doc` | The `Doc` object to add. ~~Doc~~ |
## DocBin.get_docs {#get_docs tag="method"}
@ -93,15 +93,15 @@ Recover `Doc` objects from the annotations, using the given vocab.
> docs = list(doc_bin.get_docs(nlp.vocab))
> ```
| Argument | Type | Description |
| ---------- | ------- | ------------------ |
| `vocab` | `Vocab` | The shared vocab. |
| **YIELDS** | `Doc` | The `Doc` objects. |
| Argument | Description |
| ---------- | --------------------------- |
| `vocab` | The shared vocab. ~~Vocab~~ |
| **YIELDS** | The `Doc` objects. ~~Doc~~ |
## DocBin.merge {#merge tag="method"}
Extend the annotations of this `DocBin` with the annotations from another. Will
raise an error if the pre-defined attrs of the two `DocBin`s don't match.
raise an error if the pre-defined `attrs` of the two `DocBin`s don't match.
> #### Example
>
@ -114,9 +114,9 @@ raise an error if the pre-defined attrs of the two `DocBin`s don't match.
> assert len(doc_bin1) == 2
> ```
| Argument | Type | Description |
| -------- | -------- | ------------------------------------------- |
| `other` | `DocBin` | The `DocBin` to merge into the current bin. |
| Argument | Description |
| -------- | ------------------------------------------------------ |
| `other` | The `DocBin` to merge into the current bin. ~~DocBin~~ |
## DocBin.to_bytes {#to_bytes tag="method"}
@ -130,9 +130,9 @@ Serialize the `DocBin`'s annotations to a bytestring.
> doc_bin_bytes = doc_bin.to_bytes()
> ```
| Argument | Type | Description |
| ----------- | ----- | ------------------------ |
| **RETURNS** | bytes | The serialized `DocBin`. |
| Argument | Description |
| ----------- | ---------------------------------- |
| **RETURNS** | The serialized `DocBin`. ~~bytes~~ |
## DocBin.from_bytes {#from_bytes tag="method"}
@ -145,10 +145,10 @@ Deserialize the `DocBin`'s annotations from a bytestring.
> new_doc_bin = DocBin().from_bytes(doc_bin_bytes)
> ```
| Argument | Type | Description |
| ------------ | -------- | ---------------------- |
| `bytes_data` | bytes | The data to load from. |
| **RETURNS** | `DocBin` | The loaded `DocBin`. |
| Argument | Description |
| ------------ | -------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| **RETURNS** | The loaded `DocBin`. ~~DocBin~~ |
## DocBin.to_disk {#to_disk tag="method" new="3"}
@ -164,9 +164,9 @@ and the result can be used as the input data for
> doc_bin.to_disk("./data.spacy")
> ```
| Argument | Type | Description |
| -------- | ------------ | ----------------------------------------------------- |
| `path` | str / `Path` | The file path, typically with the `.spacy` extension. |
| Argument | Description |
| -------- | -------------------------------------------------------------------------- |
| `path` | The file path, typically with the `.spacy` extension. ~~Union[str, Path]~~ |
## DocBin.from_disk {#from_disk tag="method" new="3"}
@ -178,7 +178,7 @@ Load a serialized `DocBin` from a file. Typically uses the `.spacy` extension.
> doc_bin = DocBin().from_disk("./data.spacy")
> ```
| Argument | Type | Description |
| ----------- | ------------ | ----------------------------------------------------- |
| `path` | str / `Path` | The file path, typically with the `.spacy` extension. |
| **RETURNS** | `DocBin` | The loaded `DocBin`. |
| Argument | Description |
| ----------- | -------------------------------------------------------------------------- |
| `path` | The file path, typically with the `.spacy` extension. ~~Union[str, Path]~~ |
| **RETURNS** | The loaded `DocBin`. ~~DocBin~~ |

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@ -40,14 +40,14 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("entity_linker", config=config)
> ```
| Setting | Type | Description | Default |
| ---------------- | -------------------------------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------ |
| `labels_discard` | `Iterable[str]` | NER labels that will automatically get a "NIL" prediction. | `[]` |
| `incl_prior` | bool | Whether or not to include prior probabilities from the KB in the model. | `True` |
| `incl_context` | bool | Whether or not to include the local context in the model. | `True` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [EntityLinker](/api/architectures#EntityLinker) |
| `kb_loader` | `Callable[[Vocab], KnowledgeBase]` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. | An empty KnowledgeBase with `entity_vector_length` 64. |
| `get_candidates` | `Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]` | Function that generates plausible candidates for a given `Span` object. | Built-in dictionary-lookup function. |
| Setting | Description |
| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. Defaults to [EmptyKB](/api/architectures#EmptyKB), a function returning an empty `KnowledgeBase` with an `entity_vector_length` of `64`. ~~Callable[[Vocab], KnowledgeBase]~~ |
| `get_candidates` | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entity_linker.py
@ -66,7 +66,7 @@ https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entity_linker.py
> entity_linker = nlp.add_pipe("entity_linker", config=config)
>
> # Construction via add_pipe with custom KB and candidate generation
> config = {"kb_loader": {"@assets": "my_kb.v1"}, "get_candidates": {"@assets": "my_candidates.v1"},}
> config = {"kb": {"@assets": "my_kb.v1"}}
> entity_linker = nlp.add_pipe("entity_linker", config=config)
>
> # Construction from class
@ -76,22 +76,21 @@ https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entity_linker.py
Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
[`nlp.add_pipe`](/api/language#add_pipe). Note that both the internal
`KnowledgeBase` as well as the Candidate generator can be customized by
providing custom registered functions.
Note that both the internal KB as well as the Candidate generator can be
customized by providing custom registered functions.
| Name | Type | Description |
| ---------------- | -------------------------------------------------------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
| `kb_loader` | `Callable[[Vocab], KnowledgeBase]` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. |
| `get_candidates` | `Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]` | Function that generates plausible candidates for a given `Span` object. |
| `labels_discard` | `Iterable[str]` | NER labels that will automatically get a "NIL" prediction. |
| `incl_prior` | bool | Whether or not to include prior probabilities from the KB in the model. |
| `incl_context` | bool | Whether or not to include the local context in the model. |
| Name | Description |
| ---------------- | -------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | | |
| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
## EntityLinker.\_\_call\_\_ {#call tag="method"}
@ -111,10 +110,10 @@ delegate to the [`predict`](/api/entitylinker#predict) and
> processed = entity_linker(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## EntityLinker.pipe {#pipe tag="method"}
@ -133,12 +132,12 @@ applied to the `Doc` in order. Both [`__call__`](/api/entitylinker#call) and
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## EntityLinker.begin_training {#begin_training tag="method"}
@ -158,13 +157,13 @@ setting up the label scheme based on the data.
> optimizer = entity_linker.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/dependencyparser#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## EntityLinker.predict {#predict tag="method"}
@ -179,10 +178,10 @@ if there is no prediction.
> kb_ids = entity_linker.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ------------------------------------------------------------ |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | `List[str]` | The predicted KB identifiers for the entities in the `docs`. |
| Name | Description |
| ----------- | ------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | `List[str]` | The predicted KB identifiers for the entities in the `docs`. ~~List[str]~~ |
## EntityLinker.set_annotations {#set_annotations tag="method"}
@ -197,10 +196,10 @@ entities.
> entity_linker.set_annotations([doc1, doc2], kb_ids)
> ```
| Name | Type | Description |
| -------- | --------------- | ------------------------------------------------------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `kb_ids` | `List[str]` | The knowledge base identifiers for the entities in the docs, predicted by `EntityLinker.predict`. |
| Name | Description |
| -------- | --------------------------------------------------------------------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `kb_ids` | The knowledge base identifiers for the entities in the docs, predicted by `EntityLinker.predict`. ~~List[str]~~ |
## EntityLinker.update {#update tag="method"}
@ -216,15 +215,15 @@ pipe's entity linking model and context encoder. Delegates to
> losses = entity_linker.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/textcategorizer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## EntityLinker.create_optimizer {#create_optimizer tag="method"}
@ -237,9 +236,9 @@ Create an optimizer for the pipeline component.
> optimizer = entity_linker.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## EntityLinker.use_params {#use_params tag="method, contextmanager"}
@ -254,9 +253,9 @@ context, the original parameters are restored.
> entity_linker.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## EntityLinker.to_disk {#to_disk tag="method"}
@ -269,11 +268,11 @@ Serialize the pipe to disk.
> entity_linker.to_disk("/path/to/entity_linker")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## EntityLinker.from_disk {#from_disk tag="method"}
@ -286,12 +285,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> entity_linker.from_disk("/path/to/entity_linker")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `EntityLinker` | The modified `EntityLinker` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `EntityLinker` object. ~~EntityLinker~~ |
## Serialization fields {#serialization-fields}

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@ -41,11 +41,11 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("ner", config=config)
> ```
| Setting | Type | Description | Default |
| ----------------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------- |
| `moves` | `List[str]` | A list of transition names. Inferred from the data if not provided. |
| `update_with_oracle_cut_size` | int | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. | `100` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TransitionBasedParser](/api/architectures#TransitionBasedParser) |
| Setting | Description |
| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `moves` | A list of transition names. Inferred from the data if not provided. Defaults to `None`. ~~Optional[List[str]] |
| `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. Defaults to `100`. ~~int~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [TransitionBasedParser](/api/architectures#TransitionBasedParser). ~~Model[List[Doc], List[Floats2d]]~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/ner.pyx
@ -72,14 +72,14 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| ----------------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| `moves` | `List[str]` | A list of transition names. Inferred from the data if not provided. |
| _keyword-only_ | | |
| `update_with_oracle_cut_size` | int | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. `100` is a good default. |
| Name | Description |
| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| `moves` | A list of transition names. Inferred from the data if not provided. ~~Optional[List[str]]~~ |
| _keyword-only_ | |
| `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. `100` is a good default. ~~int~~ |
## EntityRecognizer.\_\_call\_\_ {#call tag="method"}
@ -100,10 +100,10 @@ and all pipeline components are applied to the `Doc` in order. Both
> processed = ner(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## EntityRecognizer.pipe {#pipe tag="method"}
@ -122,12 +122,12 @@ applied to the `Doc` in order. Both [`__call__`](/api/entityrecognizer#call) and
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------ |
| `docs` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `docs` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## EntityRecognizer.begin_training {#begin_training tag="method"}
@ -147,13 +147,13 @@ setting up the label scheme based on the data.
> optimizer = ner.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/entityrecognizer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## EntityRecognizer.predict {#predict tag="method"}
@ -167,10 +167,10 @@ modifying them.
> scores = ner.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | ------------------ | ---------------------------------------------------------------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | `List[StateClass]` | List of `syntax.StateClass` objects. `syntax.StateClass` is a helper class for the parse state (internal). |
| Name | Description |
| ----------- | ------------------------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | A helper class for the parse state (internal). ~~StateClass~~ |
## EntityRecognizer.set_annotations {#set_annotations tag="method"}
@ -184,10 +184,10 @@ Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
> ner.set_annotations([doc1, doc2], scores)
> ```
| Name | Type | Description |
| -------- | ------------------ | ---------------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | `List[StateClass]` | The scores to set, produced by `EntityRecognizer.predict`. |
| Name | Description |
| -------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `EntityRecognizer.predict`. Returns an internal helper class for the parse state. ~~List[StateClass]~~ |
## EntityRecognizer.update {#update tag="method"}
@ -203,15 +203,15 @@ model. Delegates to [`predict`](/api/entityrecognizer#predict) and
> losses = ner.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/entityrecognizer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## EntityRecognizer.get_loss {#get_loss tag="method"}
@ -226,11 +226,11 @@ predicted scores.
> loss, d_loss = ner.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | `List[StateClass]` | Scores representing the model's predictions. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------- |
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
| `scores` | Scores representing the model's predictions. ~~StateClass~~ |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
## EntityRecognizer.score {#score tag="method" new="3"}
@ -242,10 +242,10 @@ Score a batch of examples.
> scores = ner.score(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | ------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------- |
| `examples` | The examples to score. ~~Iterable[Example]~~ |
| **RETURNS** | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). ~~Dict[str, Union[float, Dict[str, float]]]~~ |
## EntityRecognizer.create_optimizer {#create_optimizer tag="method"}
@ -258,9 +258,9 @@ Create an optimizer for the pipeline component.
> optimizer = ner.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## EntityRecognizer.use_params {#use_params tag="method, contextmanager"}
@ -275,9 +275,9 @@ context, the original parameters are restored.
> ner.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## EntityRecognizer.add_label {#add_label tag="method"}
@ -290,10 +290,10 @@ Add a new label to the pipe.
> ner.add_label("MY_LABEL")
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------------------------- |
| `label` | str | The label to add. |
| **RETURNS** | int | `0` if the label is already present, otherwise `1`. |
| Name | Description |
| ----------- | ----------------------------------------------------------- |
| `label` | The label to add. ~~str~~ |
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
## EntityRecognizer.to_disk {#to_disk tag="method"}
@ -306,11 +306,11 @@ Serialize the pipe to disk.
> ner.to_disk("/path/to/ner")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## EntityRecognizer.from_disk {#from_disk tag="method"}
@ -323,12 +323,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> ner.from_disk("/path/to/ner")
> ```
| Name | Type | Description |
| -------------- | ------------------ | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `EntityRecognizer` | The modified `EntityRecognizer` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `EntityRecognizer` object. ~~EntityRecognizer~~ |
## EntityRecognizer.to_bytes {#to_bytes tag="method"}
@ -341,11 +341,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `EntityRecognizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `EntityRecognizer` object. ~~bytes~~ |
## EntityRecognizer.from_bytes {#from_bytes tag="method"}
@ -359,12 +359,12 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> ner.from_bytes(ner_bytes)
> ```
| Name | Type | Description |
| -------------- | ------------------ | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `EntityRecognizer` | The `EntityRecognizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `EntityRecognizer` object. ~~EntityRecognizer~~ |
## EntityRecognizer.labels {#labels tag="property"}
@ -377,9 +377,9 @@ The labels currently added to the component.
> assert "MY_LABEL" in ner.labels
> ```
| Name | Type | Description |
| ----------- | ----- | ---------------------------------- |
| **RETURNS** | tuple | The labels added to the component. |
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
## Serialization fields {#serialization-fields}

120
website/docs/api/entityruler.md
View File

@ -34,12 +34,12 @@ how the component should be configured. You can override its settings via the
> nlp.add_pipe("entity_ruler", config=config)
> ```
| Setting | Type | Description | Default |
| --------------------- | ---- | ------------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| `phrase_matcher_attr` | str | Optional attribute name match on for the internal [`PhraseMatcher`](/api/phrasematcher), e.g. `LOWER` to match on the lowercase token text. | `None` |
| `validate` | bool | Whether patterns should be validated (passed to the `Matcher` and `PhraseMatcher`). | `False` |
| `overwrite_ents` | bool | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. | `False` |
| `ent_id_sep` | str | Separator used internally for entity IDs. | `"||"` |
| Setting | Description |
| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `phrase_matcher_attr` | Optional attribute name match on for the internal [`PhraseMatcher`](/api/phrasematcher), e.g. `LOWER` to match on the lowercase token text. Defaults to `None`. ~~Optional[Union[int, str]]~~ |
| `validate` | Whether patterns should be validated (passed to the `Matcher` and `PhraseMatcher`). Defaults to `False`. ~~bool~~ |
| `overwrite_ents` | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. ~~bool~~ |
| `ent_id_sep` | Separator used internally for entity IDs. Defaults to `"||"`. ~~str~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entityruler.py
@ -63,16 +63,16 @@ be a token pattern (list) or a phrase pattern (string). For example:
> ruler = EntityRuler(nlp, overwrite_ents=True)
> ```
| Name | Type | Description |
| --------------------------------- | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nlp` | `Language` | The shared nlp object to pass the vocab to the matchers and process phrase patterns. |
| `name` <Tag variant="new">3</Tag> | str | Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current entity ruler while creating phrase patterns with the nlp object. |
| _keyword-only_ | | |
| `phrase_matcher_attr` | int / str | Optional attribute name match on for the internal [`PhraseMatcher`](/api/phrasematcher), e.g. `LOWER` to match on the lowercase token text. Defaults to `None`. |
| `validate` | bool | Whether patterns should be validated, passed to Matcher and PhraseMatcher as `validate`. Defaults to `False`. |
| `overwrite_ents` | bool | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. |
| `ent_id_sep` | str | Separator used internally for entity IDs. Defaults to `"||"`. |
| `patterns` | iterable | Optional patterns to load in on initialization. |
| Name | Description |
| --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nlp` | The shared nlp object to pass the vocab to the matchers and process phrase patterns. ~~Language~~ |
| `name` <Tag variant="new">3</Tag> | Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current entity ruler while creating phrase patterns with the nlp object. ~~str~~ |
| _keyword-only_ | |
| `phrase_matcher_attr` | Optional attribute name match on for the internal [`PhraseMatcher`](/api/phrasematcher), e.g. `LOWER` to match on the lowercase token text. Defaults to `None`. ~~Optional[Union[int, str]]~~ |
| `validate` | Whether patterns should be validated, passed to Matcher and PhraseMatcher as `validate`. Defaults to `False`. ~~bool~~ |
| `overwrite_ents` | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. ~~bool~~ |
| `ent_id_sep` | Separator used internally for entity IDs. Defaults to `"||"`. ~~str~~ |
| `patterns` | Optional patterns to load in on initialization. ~~Optional[List[Dict[str, Union[str, List[dict]]]]]~~ |
## EntityRuler.\_\len\_\_ {#len tag="method"}
@ -87,9 +87,9 @@ The number of all patterns added to the entity ruler.
> assert len(ruler) == 1
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------- |
| **RETURNS** | int | The number of patterns. |
| Name | Description |
| ----------- | ------------------------------- |
| **RETURNS** | The number of patterns. ~~int~~ |
## EntityRuler.\_\_contains\_\_ {#contains tag="method"}
@ -104,10 +104,10 @@ Whether a label is present in the patterns.
> assert not "PERSON" in ruler
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------------- |
| `label` | str | The label to check. |
| **RETURNS** | bool | Whether the entity ruler contains the label. |
| Name | Description |
| ----------- | ----------------------------------------------------- |
| `label` | The label to check. ~~str~~ |
| **RETURNS** | Whether the entity ruler contains the label. ~~bool~~ |
## EntityRuler.\_\_call\_\_ {#call tag="method"}
@ -130,10 +130,10 @@ is chosen.
> assert ents == [("Apple", "ORG")]
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------ |
| `doc` | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
| **RETURNS** | `Doc` | The modified `Doc` with added entities, if available. |
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
| **RETURNS** | The modified `Doc` with added entities, if available. ~~Doc~~ |
## EntityRuler.add_patterns {#add_patterns tag="method"}
@ -152,9 +152,9 @@ of dicts) or a phrase pattern (string). For more details, see the usage guide on
> ruler.add_patterns(patterns)
> ```
| Name | Type | Description |
| ---------- | ---- | -------------------- |
| `patterns` | list | The patterns to add. |
| Name | Description |
| ---------- | ---------------------------------------------------------------- |
| `patterns` | The patterns to add. ~~List[Dict[str, Union[str, List[dict]]]]~~ |
## EntityRuler.to_disk {#to_disk tag="method"}
@ -171,9 +171,9 @@ only the patterns are saved as JSONL. If a directory name is provided, a
> ruler.to_disk("/path/to/entity_ruler") # saves patterns and config
> ```
| Name | Type | Description |
| ------ | ------------ | ----------------------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a JSONL file or directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| Name | Description |
| ------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `path` | A path to a JSONL file or directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
## EntityRuler.from_disk {#from_disk tag="method"}
@ -190,10 +190,10 @@ configuration.
> ruler.from_disk("/path/to/entity_ruler") # loads patterns and config
> ```
| Name | Type | Description |
| ----------- | ------------- | ---------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a JSONL file or directory. Paths may be either strings or `Path`-like objects. |
| **RETURNS** | `EntityRuler` | The modified `EntityRuler` object. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------- |
| `path` | A path to a JSONL file or directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| **RETURNS** | The modified `EntityRuler` object. ~~EntityRuler~~ |
## EntityRuler.to_bytes {#to_bytes tag="method"}
@ -206,9 +206,9 @@ Serialize the entity ruler patterns to a bytestring.
> ruler_bytes = ruler.to_bytes()
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| **RETURNS** | bytes | The serialized patterns. |
| Name | Description |
| ----------- | ---------------------------------- |
| **RETURNS** | The serialized patterns. ~~bytes~~ |
## EntityRuler.from_bytes {#from_bytes tag="method"}
@ -222,40 +222,40 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> ruler.from_bytes(ruler_bytes)
> ```
| Name | Type | Description |
| ------------ | ------------- | ---------------------------------- |
| `bytes_data` | bytes | The bytestring to load. |
| **RETURNS** | `EntityRuler` | The modified `EntityRuler` object. |
| Name | Description |
| ------------ | -------------------------------------------------- |
| `bytes_data` | The bytestring to load. ~~bytes~~ |
| **RETURNS** | The modified `EntityRuler` object. ~~EntityRuler~~ |
## EntityRuler.labels {#labels tag="property"}
All labels present in the match patterns.
| Name | Type | Description |
| ----------- | ----- | ------------------ |
| **RETURNS** | tuple | The string labels. |
| Name | Description |
| ----------- | -------------------------------------- |
| **RETURNS** | The string labels. ~~Tuple[str, ...]~~ |
## EntityRuler.ent_ids {#labels tag="property" new="2.2.2"}
All entity ids present in the match patterns `id` properties.
All entity IDs present in the `id` properties of the match patterns.
| Name | Type | Description |
| ----------- | ----- | ------------------- |
| **RETURNS** | tuple | The string ent_ids. |
| Name | Description |
| ----------- | ----------------------------------- |
| **RETURNS** | The string IDs. ~~Tuple[str, ...]~~ |
## EntityRuler.patterns {#patterns tag="property"}
Get all patterns that were added to the entity ruler.
| Name | Type | Description |
| ----------- | ---- | -------------------------------------------------- |
| **RETURNS** | list | The original patterns, one dictionary per pattern. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------- |
| **RETURNS** | The original patterns, one dictionary per pattern. ~~List[Dict[str, Union[str, dict]]]~~ |
## Attributes {#attributes}
| Name | Type | Description |
| ----------------- | ------------------------------------- | ---------------------------------------------------------------- |
| `matcher` | [`Matcher`](/api/matcher) | The underlying matcher used to process token patterns. |
| `phrase_matcher` | [`PhraseMatcher`](/api/phrasematcher) | The underlying phrase matcher, used to process phrase patterns. |
| `token_patterns` | dict | The token patterns present in the entity ruler, keyed by label. |
| `phrase_patterns` | dict | The phrase patterns present in the entity ruler, keyed by label. |
| Name | Description |
| ----------------- | --------------------------------------------------------------------------------------------------------------------- |
| `matcher` | The underlying matcher used to process token patterns. ~~Matcher~~ | |
| `phrase_matcher` | The underlying phrase matcher, used to process phrase patterns. ~~PhraseMatcher~~ |
| `token_patterns` | The token patterns present in the entity ruler, keyed by label. ~~Dict[str, List[Dict[str, Union[str, List[dict]]]]~~ |
| `phrase_patterns` | The phrase patterns present in the entity ruler, keyed by label. ~~Dict[str, List[Doc]]~~ |

181
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@ -8,9 +8,9 @@ new: 3.0
An `Example` holds the information for one training instance. It stores two
`Doc` objects: one for holding the gold-standard reference data, and one for
holding the predictions of the pipeline. An [`Alignment`](#alignment-object)
object stores the alignment between these two documents, as they can differ in
tokenization.
holding the predictions of the pipeline. An
[`Alignment`](/api/example#alignment-object) object stores the alignment between
these two documents, as they can differ in tokenization.
## Example.\_\_init\_\_ {#init tag="method"}
@ -31,12 +31,12 @@ both documents.
> example = Example(predicted, reference)
> ```
| Name | Type | Description |
| -------------- | ----------- | ------------------------------------------------------------------------------------------------ |
| `predicted` | `Doc` | The document containing (partial) predictions. Can not be `None`. |
| `reference` | `Doc` | The document containing gold-standard annotations. Can not be `None`. |
| _keyword-only_ | | |
| `alignment` | `Alignment` | An object holding the alignment between the tokens of the `predicted` and `reference` documents. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `predicted` | The document containing (partial) predictions. Can not be `None`. ~~Doc~~ |
| `reference` | The document containing gold-standard annotations. Can not be `None`. ~~Doc~~ |
| _keyword-only_ | |
| `alignment` | An object holding the alignment between the tokens of the `predicted` and `reference` documents. ~~Optional[Alignment]~~ |
## Example.from_dict {#from_dict tag="classmethod"}
@ -56,11 +56,11 @@ see the [training format documentation](/api/data-formats#dict-input).
> example = Example.from_dict(predicted, {"words": token_ref, "tags": tags_ref})
> ```
| Name | Type | Description |
| -------------- | ---------------- | ----------------------------------------------------------------- |
| `predicted` | `Doc` | The document containing (partial) predictions. Can not be `None`. |
| `example_dict` | `Dict[str, obj]` | The gold-standard annotations as a dictionary. Can not be `None`. |
| **RETURNS** | `Example` | The newly constructed object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------- |
| `predicted` | The document containing (partial) predictions. Can not be `None`. ~~Doc~~ |
| `example_dict` | `Dict[str, obj]` | The gold-standard annotations as a dictionary. Can not be `None`. ~~Dict[str, Any]~~ |
| **RETURNS** | The newly constructed object. ~~Example~~ |
## Example.text {#text tag="property"}
@ -72,12 +72,14 @@ The text of the `predicted` document in this `Example`.
> raw_text = example.text
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------- |
| **RETURNS** | str | The text of the `predicted` document. |
| Name | Description |
| ----------- | --------------------------------------------- |
| **RETURNS** | The text of the `predicted` document. ~~str~~ |
## Example.predicted {#predicted tag="property"}
The `Doc` holding the predictions. Occasionally also referred to as `example.x`.
> #### Example
>
> ```python
@ -86,14 +88,15 @@ The text of the `predicted` document in this `Example`.
> set_annotations(docs, predictions)
> ```
The `Doc` holding the predictions. Occassionally also refered to as `example.x`.
| Name | Type | Description |
| ----------- | ----- | ---------------------------------------------- |
| **RETURNS** | `Doc` | The document containing (partial) predictions. |
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | The document containing (partial) predictions. ~~Doc~~ |
## Example.reference {#reference tag="property"}
The `Doc` holding the gold-standard annotations. Occasionally also referred to
as `example.y`.
> #### Example
>
> ```python
@ -102,15 +105,15 @@ The `Doc` holding the predictions. Occassionally also refered to as `example.x`.
> gold_labels[i][j] = eg.reference.cats.get(label, 0.0)
> ```
The `Doc` holding the gold-standard annotations. Occassionally also refered to
as `example.y`.
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------------- |
| **RETURNS** | `Doc` | The document containing gold-standard annotations. |
| Name | Description |
| ----------- | ---------------------------------------------------------- |
| **RETURNS** | The document containing gold-standard annotations. ~~Doc~~ |
## Example.alignment {#alignment tag="property"}
The [`Alignment`](/api/example#alignment-object) object mapping the tokens of
the `predicted` document to those of the `reference` document.
> #### Example
>
> ```python
@ -122,15 +125,15 @@ as `example.y`.
> assert list(alignment.y2x.data) == [[0], [1], [2], [2]]
> ```
The `Alignment` object mapping the tokens of the `predicted` document to those
of the `reference` document.
| Name | Type | Description |
| ----------- | ----------- | -------------------------------------------------- |
| **RETURNS** | `Alignment` | The document containing gold-standard annotations. |
| Name | Description |
| ----------- | ---------------------------------------------------------------- |
| **RETURNS** | The document containing gold-standard annotations. ~~Alignment~~ |
## Example.get_aligned {#get_aligned tag="method"}
Get the aligned view of a certain token attribute, denoted by its int ID or
string name.
> #### Example
>
> ```python
@ -141,17 +144,18 @@ of the `reference` document.
> assert example.get_aligned("TAG", as_string=True) == ["VERB", "DET", "NOUN"]
> ```
Get the aligned view of a certain token attribute, denoted by its int ID or
string name.
| Name | Type | Description | Default |
| ----------- | -------------------------- | ------------------------------------------------------------------ | ------- |
| `field` | int or str | Attribute ID or string name | |
| `as_string` | bool | Whether or not to return the list of values as strings. | `False` |
| **RETURNS** | `List[int]` or `List[str]` | List of integer values, or string values if `as_string` is `True`. | |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------- |
| `field` | Attribute ID or string name. ~~Union[int, str]~~ |
| `as_string` | Whether or not to return the list of values as strings. Defaults to `False`. ~~bool~~ |
| **RETURNS** | List of integer values, or string values if `as_string` is `True`. ~~Union[List[int], List[str]]~~ |
## Example.get_aligned_parse {#get_aligned_parse tag="method"}
Get the aligned view of the dependency parse. If `projectivize` is set to
`True`, non-projective dependency trees are made projective through the
Pseudo-Projective Dependency Parsing algorithm by Nivre and Nilsson (2005).
> #### Example
>
> ```python
@ -161,17 +165,16 @@ string name.
> assert proj_heads == [3, 2, 3, 0, 3]
> ```
Get the aligned view of the dependency parse. If `projectivize` is set to
`True`, non-projective dependency trees are made projective through the
Pseudo-Projective Dependency Parsing algorithm by Nivre and Nilsson (2005).
| Name | Type | Description | Default |
| -------------- | -------------------------- | ------------------------------------------------------------------ | ------- |
| `projectivize` | bool | Whether or not to projectivize the dependency trees | `True` |
| **RETURNS** | `List[int]` or `List[str]` | List of integer values, or string values if `as_string` is `True`. | |
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------- |
| `projectivize` | Whether or not to projectivize the dependency trees. Defaults to `True`. ~~bool~~ |
| **RETURNS** | List of integer values, or string values if `as_string` is `True`. ~~Union[List[int], List[str]]~~ |
## Example.get_aligned_ner {#get_aligned_ner tag="method"}
Get the aligned view of the NER
[BILUO](/usage/linguistic-features#accessing-ner) tags.
> #### Example
>
> ```python
@ -184,15 +187,16 @@ Pseudo-Projective Dependency Parsing algorithm by Nivre and Nilsson (2005).
> assert ner_tags == ["B-PERSON", "L-PERSON", "O", "O", "U-LOC"]
> ```
Get the aligned view of the NER
[BILUO](/usage/linguistic-features#accessing-ner) tags.
| Name | Type | Description |
| ----------- | ----------- | ----------------------------------------------------------------------------------- |
| **RETURNS** | `List[str]` | List of BILUO values, denoting whether tokens are part of an NER annotation or not. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------- |
| **RETURNS** | List of BILUO values, denoting whether tokens are part of an NER annotation or not. ~~List[str]~~ |
## Example.get_aligned_spans_y2x {#get_aligned_spans_y2x tag="method"}
Get the aligned view of any set of [`Span`](/api/span) objects defined over
[`Example.reference`](/api/example#reference). The resulting span indices will
align to the tokenization in [`Example.predicted`](/api/example#predicted).
> #### Example
>
> ```python
@ -207,17 +211,19 @@ Get the aligned view of the NER
> assert [(ent.start, ent.end) for ent in ents_y2x] == [(0, 1)]
> ```
Get the aligned view of any set of [`Span`](/api/span) objects defined over
`example.reference`. The resulting span indices will align to the tokenization
in `example.predicted`.
| Name | Type | Description |
| ----------- | ---------------- | --------------------------------------------------------------- |
| `y_spans` | `Iterable[Span]` | `Span` objects aligned to the tokenization of `self.reference`. |
| **RETURNS** | `Iterable[Span]` | `Span` objects aligned to the tokenization of `self.predicted`. |
| Name | Description |
| ----------- | ----------------------------------------------------------------------------- |
| `y_spans` | `Span` objects aligned to the tokenization of `reference`. ~~Iterable[Span]~~ |
| **RETURNS** | `Span` objects aligned to the tokenization of `predicted`. ~~List[Span]~~ |
## Example.get_aligned_spans_x2y {#get_aligned_spans_x2y tag="method"}
Get the aligned view of any set of [`Span`](/api/span) objects defined over
[`Example.predicted`](/api/example#predicted). The resulting span indices will
align to the tokenization in [`Example.reference`](/api/example#reference). This
method is particularly useful to assess the accuracy of predicted entities
against the original gold-standard annotation.
> #### Example
>
> ```python
@ -232,15 +238,10 @@ in `example.predicted`.
> assert [(ent.start, ent.end) for ent in ents_x2y] == [(0, 2)]
> ```
Get the aligned view of any set of [`Span`](/api/span) objects defined over
`example.predicted`. The resulting span indices will align to the tokenization
in `example.reference`. This method is particularly useful to assess the
accuracy of predicted entities against the original gold-standard annotation.
| Name | Type | Description |
| ----------- | ---------------- | --------------------------------------------------------------- |
| `x_spans` | `Iterable[Span]` | `Span` objects aligned to the tokenization of `self.predicted`. |
| **RETURNS** | `Iterable[Span]` | `Span` objects aligned to the tokenization of `self.reference`. |
| Name | Description |
| ----------- | ----------------------------------------------------------------------------- |
| `x_spans` | `Span` objects aligned to the tokenization of `predicted`. ~~Iterable[Span]~~ |
| **RETURNS** | `Span` objects aligned to the tokenization of `reference`. ~~List[Span]~~ |
## Example.to_dict {#to_dict tag="method"}
@ -253,12 +254,14 @@ reference annotation contained in this `Example`.
> eg_dict = example.to_dict()
> ```
| Name | Type | Description |
| ----------- | ---------------- | ------------------------------------------------------ |
| **RETURNS** | `Dict[str, Any]` | Dictionary representation of the reference annotation. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------- |
| **RETURNS** | Dictionary representation of the reference annotation. ~~Dict[str, Any]~~ |
## Example.split_sents {#split_sents tag="method"}
Split one `Example` into multiple `Example` objects, one for each sentence.
> #### Example
>
> ```python
@ -271,11 +274,9 @@ reference annotation contained in this `Example`.
> assert split_examples[1].text == "had lots of fun"
> ```
Split one `Example` into multiple `Example` objects, one for each sentence.
| Name | Type | Description |
| ----------- | --------------- | ---------------------------------------------------------- |
| **RETURNS** | `List[Example]` | List of `Example` objects, one for each original sentence. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------- |
| **RETURNS** | List of `Example` objects, one for each original sentence. ~~List[Example]~~ |
## Alignment {#alignment-object new="3"}
@ -283,10 +284,10 @@ Calculate alignment tables between two tokenizations.
### Alignment attributes {#alignment-attributes"}
| Name | Type | Description |
| ----- | -------------------------------------------------- | ---------------------------------------------------------- |
| `x2y` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | The `Ragged` object holding the alignment from `x` to `y`. |
| `y2x` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | The `Ragged` object holding the alignment from `y` to `x`. |
| Name | Description |
| ----- | --------------------------------------------------------------------- |
| `x2y` | The `Ragged` object holding the alignment from `x` to `y`. ~~Ragged~~ |
| `y2x` | The `Ragged` object holding the alignment from `y` to `x`. ~~Ragged~~ |
<Infobox title="Important note" variant="warning">
@ -314,8 +315,8 @@ tokenizations add up to the same string. For example, you'll be able to align
### Alignment.from_strings {#classmethod tag="function"}
| Name | Type | Description |
| ----------- | ----------- | ----------------------------------------------- |
| `A` | list | String values of candidate tokens to align. |
| `B` | list | String values of reference tokens to align. |
| **RETURNS** | `Alignment` | An `Alignment` object describing the alignment. |
| Name | Description |
| ----------- | ------------------------------------------------------------- |
| `A` | String values of candidate tokens to align. ~~List[str]~~ |
| `B` | String values of reference tokens to align. ~~List[str]~~ |
| **RETURNS** | An `Alignment` object describing the alignment. ~~Alignment~~ |

164
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View File

@ -9,7 +9,7 @@ new: 2.2
---
The `KnowledgeBase` object provides a method to generate
[`Candidate`](/api/kb/#candidate_init) objects, which are plausible external
[`Candidate`](/api/kb/#candidate) objects, which are plausible external
identifiers given a certain textual mention. Each such `Candidate` holds
information from the relevant KB entities, such as its frequency in text and
possible aliases. Each entity in the knowledge base also has a pretrained entity
@ -27,18 +27,18 @@ Create the knowledge base.
> kb = KnowledgeBase(vocab=vocab, entity_vector_length=64)
> ```
| Name | Type | Description |
| ---------------------- | ------- | ---------------------------------------- |
| `vocab` | `Vocab` | A `Vocab` object. |
| `entity_vector_length` | int | Length of the fixed-size entity vectors. |
| Name | Description |
| ---------------------- | ------------------------------------------------ |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `entity_vector_length` | Length of the fixed-size entity vectors. ~~int~~ |
## KnowledgeBase.entity_vector_length {#entity_vector_length tag="property"}
The length of the fixed-size entity vectors in the knowledge base.
| Name | Type | Description |
| ----------- | ---- | ---------------------------------------- |
| **RETURNS** | int | Length of the fixed-size entity vectors. |
| Name | Description |
| ----------- | ------------------------------------------------ |
| **RETURNS** | Length of the fixed-size entity vectors. ~~int~~ |
## KnowledgeBase.add_entity {#add_entity tag="method"}
@ -53,11 +53,11 @@ vector, which should be of length
> kb.add_entity(entity="Q463035", freq=111, entity_vector=vector2)
> ```
| Name | Type | Description |
| --------------- | ------ | ----------------------------------------------- |
| `entity` | str | The unique entity identifier |
| `freq` | float | The frequency of the entity in a typical corpus |
| `entity_vector` | vector | The pretrained vector of the entity |
| Name | Description |
| --------------- | ---------------------------------------------------------- |
| `entity` | The unique entity identifier. ~~str~~ |
| `freq` | The frequency of the entity in a typical corpus. ~~float~~ |
| `entity_vector` | The pretrained vector of the entity. ~~numpy.ndarray~~ |
## KnowledgeBase.set_entities {#set_entities tag="method"}
@ -70,11 +70,11 @@ frequency and entity vector for each entity.
> kb.set_entities(entity_list=["Q42", "Q463035"], freq_list=[32, 111], vector_list=[vector1, vector2])
> ```
| Name | Type | Description |
| ------------- | -------- | --------------------------------- |
| `entity_list` | iterable | List of unique entity identifiers |
| `freq_list` | iterable | List of entity frequencies |
| `vector_list` | iterable | List of entity vectors |
| Name | Description |
| ------------- | ---------------------------------------------------------------- |
| `entity_list` | List of unique entity identifiers. ~~Iterable[Union[str, int]]~~ |
| `freq_list` | List of entity frequencies. ~~Iterable[int]~~ |
| `vector_list` | List of entity vectors. ~~Iterable[numpy.ndarray]~~ |
## KnowledgeBase.add_alias {#add_alias tag="method"}
@ -90,11 +90,11 @@ should not exceed 1.
> kb.add_alias(alias="Douglas", entities=["Q42", "Q463035"], probabilities=[0.6, 0.3])
> ```
| Name | Type | Description |
| --------------- | -------- | -------------------------------------------------- |
| `alias` | str | The textual mention or alias |
| `entities` | iterable | The potential entities that the alias may refer to |
| `probabilities` | iterable | The prior probabilities of each entity |
| Name | Description |
| --------------- | --------------------------------------------------------------------------------- |
| `alias` | The textual mention or alias. ~~str~~ |
| `entities` | The potential entities that the alias may refer to. ~~Iterable[Union[str, int]]~~ |
| `probabilities` | The prior probabilities of each entity. ~~Iterable[float]~~ |
## KnowledgeBase.\_\_len\_\_ {#len tag="method"}
@ -106,9 +106,9 @@ Get the total number of entities in the knowledge base.
> total_entities = len(kb)
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------------------- |
| **RETURNS** | int | The number of entities in the knowledge base. |
| Name | Description |
| ----------- | ----------------------------------------------------- |
| **RETURNS** | The number of entities in the knowledge base. ~~int~~ |
## KnowledgeBase.get_entity_strings {#get_entity_strings tag="method"}
@ -120,9 +120,9 @@ Get a list of all entity IDs in the knowledge base.
> all_entities = kb.get_entity_strings()
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------------- |
| **RETURNS** | list | The list of entities in the knowledge base. |
| Name | Description |
| ----------- | --------------------------------------------------------- |
| **RETURNS** | The list of entities in the knowledge base. ~~List[str]~~ |
## KnowledgeBase.get_size_aliases {#get_size_aliases tag="method"}
@ -134,9 +134,9 @@ Get the total number of aliases in the knowledge base.
> total_aliases = kb.get_size_aliases()
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------------- |
| **RETURNS** | int | The number of aliases in the knowledge base. |
| Name | Description |
| ----------- | ---------------------------------------------------- |
| **RETURNS** | The number of aliases in the knowledge base. ~~int~~ |
## KnowledgeBase.get_alias_strings {#get_alias_strings tag="method"}
@ -148,14 +148,14 @@ Get a list of all aliases in the knowledge base.
> all_aliases = kb.get_alias_strings()
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------------ |
| **RETURNS** | list | The list of aliases in the knowledge base. |
| Name | Description |
| ----------- | -------------------------------------------------------- |
| **RETURNS** | The list of aliases in the knowledge base. ~~List[str]~~ |
## KnowledgeBase.get_candidates {#get_candidates tag="method"}
Given a certain textual mention as input, retrieve a list of candidate entities
of type [`Candidate`](/api/kb/#candidate_init).
of type [`Candidate`](/api/kb/#candidate).
> #### Example
>
@ -163,10 +163,10 @@ of type [`Candidate`](/api/kb/#candidate_init).
> candidates = kb.get_candidates("Douglas")
> ```
| Name | Type | Description |
| ----------- | -------- | ---------------------------------------- |
| `alias` | str | The textual mention or alias |
| **RETURNS** | iterable | The list of relevant `Candidate` objects |
| Name | Description |
| ----------- | ------------------------------------- |
| `alias` | The textual mention or alias. ~~str~~ |
| **RETURNS** | iterable | The list of relevant `Candidate` objects. ~~List[Candidate]~~ |
## KnowledgeBase.get_vector {#get_vector tag="method"}
@ -178,10 +178,10 @@ Given a certain entity ID, retrieve its pretrained entity vector.
> vector = kb.get_vector("Q42")
> ```
| Name | Type | Description |
| ----------- | ------ | ----------------- |
| `entity` | str | The entity ID |
| **RETURNS** | vector | The entity vector |
| Name | Description |
| ----------- | ------------------------------------ |
| `entity` | The entity ID. ~~str~~ |
| **RETURNS** | The entity vector. ~~numpy.ndarray~~ |
## KnowledgeBase.get_prior_prob {#get_prior_prob tag="method"}
@ -194,27 +194,27 @@ probability of the fact that the mention links to the entity ID.
> probability = kb.get_prior_prob("Q42", "Douglas")
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------------------------- |
| `entity` | str | The entity ID |
| `alias` | str | The textual mention or alias |
| **RETURNS** | float | The prior probability of the `alias` referring to the `entity` |
| Name | Description |
| ----------- | ------------------------------------------------------------------------- |
| `entity` | The entity ID. ~~str~~ |
| `alias` | The textual mention or alias. ~~str~~ |
| **RETURNS** | The prior probability of the `alias` referring to the `entity`. ~~float~~ |
## KnowledgeBase.dump {#dump tag="method"}
## KnowledgeBase.to_disk {#to_disk tag="method"}
Save the current state of the knowledge base to a directory.
> #### Example
>
> ```python
> kb.dump(loc)
> kb.to_disk(loc)
> ```
| Name | Type | Description |
| ----- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| `loc` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| Name | Description |
| ----- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `loc` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
## KnowledgeBase.load_bulk {#load_bulk tag="method"}
## KnowledgeBase.from_disk {#from_disk tag="method"}
Restore the state of the knowledge base from a given directory. Note that the
[`Vocab`](/api/vocab) should also be the same as the one used to create the KB.
@ -226,15 +226,23 @@ Restore the state of the knowledge base from a given directory. Note that the
> from spacy.vocab import Vocab
> vocab = Vocab().from_disk("/path/to/vocab")
> kb = KnowledgeBase(vocab=vocab, entity_vector_length=64)
> kb.load_bulk("/path/to/kb")
> kb.from_disk("/path/to/kb")
> ```
| Name | Type | Description |
| ----------- | --------------- | -------------------------------------------------------------------------- |
| `loc` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| **RETURNS** | `KnowledgeBase` | The modified `KnowledgeBase` object. |
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| `loc` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| **RETURNS** | The modified `KnowledgeBase` object. ~~KnowledgeBase~~ |
## Candidate.\_\_init\_\_ {#candidate_init tag="method"}
## Candidate {#candidate tag="class"}
A `Candidate` object refers to a textual mention (alias) that may or may not be
resolved to a specific entity from a `KnowledgeBase`. This will be used as input
for the entity linking algorithm which will disambiguate the various candidates
to the correct one. Each candidate `(alias, entity)` pair is assigned to a
certain prior probability.
### Candidate.\_\_init\_\_ {#candidate-init tag="method"}
Construct a `Candidate` object. Usually this constructor is not called directly,
but instead these objects are returned by the
@ -247,22 +255,22 @@ but instead these objects are returned by the
> candidate = Candidate(kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob)
> ```
| Name | Type | Description |
| ------------- | --------------- | -------------------------------------------------------------- |
| `kb` | `KnowledgeBase` | The knowledge base that defined this candidate. |
| `entity_hash` | int | The hash of the entity's KB ID. |
| `entity_freq` | float | The entity frequency as recorded in the KB. |
| `alias_hash` | int | The hash of the textual mention or alias. |
| `prior_prob` | float | The prior probability of the `alias` referring to the `entity` |
| Name | Description |
| ------------- | ------------------------------------------------------------------------- |
| `kb` | The knowledge base that defined this candidate. ~~KnowledgeBase~~ |
| `entity_hash` | The hash of the entity's KB ID. ~~int~~ |
| `entity_freq` | The entity frequency as recorded in the KB. ~~float~~ |
| `alias_hash` | The hash of the textual mention or alias. ~~int~~ |
| `prior_prob` | The prior probability of the `alias` referring to the `entity`. ~~float~~ |
## Candidate attributes {#candidate_attributes}
## Candidate attributes {#candidate-attributes}
| Name | Type | Description |
| --------------- | ------ | -------------------------------------------------------------- |
| `entity` | int | The entity's unique KB identifier |
| `entity_` | str | The entity's unique KB identifier |
| `alias` | int | The alias or textual mention |
| `alias_` | str | The alias or textual mention |
| `prior_prob` | long | The prior probability of the `alias` referring to the `entity` |
| `entity_freq` | long | The frequency of the entity in a typical corpus |
| `entity_vector` | vector | The pretrained vector of the entity |
| Name | Description |
| --------------- | ------------------------------------------------------------------------ |
| `entity` | The entity's unique KB identifier. ~~int~~ |
| `entity_` | The entity's unique KB identifier. ~~str~~ |
| `alias` | The alias or textual mention. ~~int~~ |
| `alias_` | The alias or textual mention. ~~str~~ |
| `prior_prob` | The prior probability of the `alias` referring to the `entity`. ~~long~~ |
| `entity_freq` | The frequency of the entity in a typical corpus. ~~long~~ |
| `entity_vector` | The pretrained vector of the entity. ~~numpy.ndarray~~ |

480
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@ -32,15 +32,15 @@ Initialize a `Language` object.
> nlp = Language(Vocab())
> ```
| Name | Type | Description |
| ------------------ | ----------- | ------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. |
| _keyword-only_ | | |
| `max_length` | int | Maximum number of characters allowed in a single text. Defaults to `10 ** 6`. |
| `meta` | dict | Custom meta data for the `Language` class. Is written to by models to add model meta data. |
| `create_tokenizer` |  `Callable` | Optional function that receives the `nlp` object and returns a tokenizer. |
| Name | Description |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------ |
| `vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~ |
| _keyword-only_ | |
| `max_length` | Maximum number of characters allowed in a single text. Defaults to `10 ** 6`. ~~int~~ |
| `meta` | Custom meta data for the `Language` class. Is written to by models to add model meta data. ~~dict~~ |
| `create_tokenizer` | Optional function that receives the `nlp` object and returns a tokenizer. ~~Callable[[Language], Callable[[str], Doc]]~~ |
## Language.from_config {#from_config tag="classmethod"}
## Language.from_config {#from_config tag="classmethod" new="3"}
Create a `Language` object from a loaded config. Will set up the tokenizer and
language data, add pipeline components based on the pipeline and components
@ -58,14 +58,14 @@ model under the hood based on its [`config.cfg`](/api/data-formats#config).
> nlp = Language.from_config(config)
> ```
| Name | Type | Description |
| -------------- | ---------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `config` | `Dict[str, Any]` / [`Config`](https://thinc.ai/docs/api-config#config) | The loaded config. |
| _keyword-only_ | |
| `disable` | `Iterable[str]` | List of pipeline component names to disable. |
| `auto_fill` | bool | Whether to automatically fill in missing values in the config, based on defaults and function argument annotations. Defaults to `True`. |
| `validate` | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| **RETURNS** | `Language` | The initialized object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `config` | The loaded config. ~~Union[Dict[str, Any], Config]~~ |
| _keyword-only_ | |
| `disable` | List of pipeline component names to disable. ~~Iterable[str]~~ |
| `auto_fill` | Whether to automatically fill in missing values in the config, based on defaults and function argument annotations. Defaults to `True`. ~~bool~~ |
| `validate` | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ |
| **RETURNS** | The initialized object. ~~Language~~ |
## Language.component {#component tag="classmethod" new="3"}
@ -94,16 +94,14 @@ decorator. For more details and examples, see the
> Language.component("my_component2", func=my_component)
> ```
| Name | Type | Description |
| ----------------------- | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | The name of the component factory. |
| _keyword-only_ | | |
| `assigns` | `Iterable[str]` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).. |
| `requires` | `Iterable[str]` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). |
| `retokenizes` | bool | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis). |
| `scores` | `Iterable[str]` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). |
| `default_score_weights` | `Dict[str, float]` | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. |
| `func` | `Optional[Callable]` | Optional function if not used a a decorator. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `name` | The name of the component factory. ~~str~~ |
| _keyword-only_ | |
| `assigns` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
| `requires` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
| `retokenizes` | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~bool~~ |
| `func` | Optional function if not used a a decorator. ~~Optional[Callable[[Doc], Doc]]~~ |
## Language.factory {#factory tag="classmethod"}
@ -141,17 +139,17 @@ examples, see the
> )
> ```
| Name | Type | Description |
| ----------------------- | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | The name of the component factory. |
| _keyword-only_ | | |
| `default_config` | `Dict[str, any]` | The default config, describing the default values of the factory arguments. |
| `assigns` | `Iterable[str]` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). |
| `requires` | `Iterable[str]` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). |
| `retokenizes` | bool | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis). |
| `scores` | `Iterable[str]` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). |
| `default_score_weights` | `Dict[str, float]` | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. |
| `func` | `Optional[Callable]` | Optional function if not used a a decorator. |
| Name | Description |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `name` | The name of the component factory. ~~str~~ |
| _keyword-only_ | |
| `default_config` | The default config, describing the default values of the factory arguments. ~~Dict[str, Any]~~ |
| `assigns` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
| `requires` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
| `retokenizes` | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~bool~~ |
| `scores` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
| `default_score_weights` | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. ~~Dict[str, float]~~ |
| `func` | Optional function if not used a a decorator. ~~Optional[Callable[[...], Callable[[Doc], Doc]]]~~ |
## Language.\_\_call\_\_ {#call tag="method"}
@ -165,13 +163,13 @@ contain arbitrary whitespace. Alignment into the original string is preserved.
> assert (doc[0].text, doc[0].head.tag_) == ("An", "NN")
> ```
| Name | Type | Description |
| --------------- | ----------------- | ------------------------------------------------------------------------------------------------------ |
| `text` | str | The text to be processed. |
| _keyword-only_ | | |
| `disable` | `List[str]` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
| `component_cfg` | `Dict[str, dict]` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. |
| **RETURNS** | [`Doc`](/api/doc) | A container for accessing the annotations. |
| Name | Description |
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `text` | The text to be processed. ~~str~~ |
| _keyword-only_ | |
| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~ |
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
| **RETURNS** | A container for accessing the annotations. ~~Doc~~ |
## Language.pipe {#pipe tag="method"}
@ -186,17 +184,17 @@ more efficient than processing texts one-by-one.
> assert doc.is_parsed
> ```
| Name | Type | Description |
| ------------------------------------------ | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `texts` | `Iterable[str]` | A sequence of strings. |
| _keyword-only_ | | |
| `as_tuples` | bool | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. |
| `batch_size` | int | The number of texts to buffer. |
| `disable` | `List[str]` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
| `cleanup` | bool | If `True`, unneeded strings are freed to control memory use. Experimental. |
| `component_cfg` | `Dict[str, dict]` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. |
| `n_process` <Tag variant="new">2.2.2</Tag> | int | Number of processors to use, only supported in Python 3. Defaults to `1`. |
| **YIELDS** | `Doc` | Documents in the order of the original text. |
| Name | Description |
| ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `texts` | A sequence of strings. ~~Iterable[str]~~ |
| _keyword-only_ | |
| `as_tuples` | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. ~~bool~~ |
| `batch_size` | The number of texts to buffer. ~~int~~ |
| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~ |
| `cleanup` | If `True`, unneeded strings are freed to control memory use. Experimental. ~~bool~~ |
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
| `n_process` <Tag variant="new">2.2.2</Tag> | Number of processors to use. Defaults to `1`. ~~int~~ |
| **YIELDS** | Documents in the order of the original text. ~~Doc~~ |
## Language.begin_training {#begin_training tag="method"}
@ -225,12 +223,12 @@ tuples of `Doc` and `GoldParse` objects.
> optimizer = nlp.begin_training(get_examples)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/language#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Optional[Callable[[], Iterable[Example]]]~~ |
| _keyword-only_ | |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Language.resume_training {#resume_training tag="method,experimental" new="3"}
@ -248,11 +246,11 @@ a batch of [Example](/api/example) objects.
> nlp.rehearse(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/language#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Language.update {#update tag="method"}
@ -282,15 +280,15 @@ and custom registered functions if needed. See the
> nlp.update([example], sgd=optimizer)
> ```
| Name | Type | Description |
| --------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Dictionary to update with the loss, keyed by pipeline component. |
| `component_cfg` | `Dict[str, dict]` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Dictionary to update with the loss, keyed by pipeline component. ~~Optional[Dict[str, float]]~~ |
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Language.rehearse {#rehearse tag="method,experimental" new="3"}
@ -305,14 +303,14 @@ the "catastrophic forgetting" problem. This feature is experimental.
> losses = nlp.rehearse(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Dictionary to update with the loss, keyed by pipeline component. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Language.evaluate {#evaluate tag="method"}
@ -328,20 +326,19 @@ objects instead of tuples of `Doc` and `GoldParse` objects.
> #### Example
>
> ```python
> scores = nlp.evaluate(examples, verbose=True)
> scores = nlp.evaluate(examples)
> print(scores)
> ```
| Name | Type | Description |
| --------------- | ------------------------------- | ------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `verbose` | bool | Print debugging information. |
| `batch_size` | int | The batch size to use. |
| `scorer` | `Scorer` | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. |
| `component_cfg` | `Dict[str, dict]` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. |
| `scorer_cfg` | `Dict[str, Any]` | Optional dictionary of keyword arguments for the `Scorer`. Defaults to `None`. |
| **RETURNS** | `Dict[str, Union[float, dict]]` | A dictionary of evaluation scores. |
| Name | Description |
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `batch_size` | The batch size to use. ~~int~~ |
| `scorer` | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. ~~Optional[Scorer]~~ |
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
| `scorer_cfg` | Optional dictionary of keyword arguments for the `Scorer`. Defaults to `None`. ~~Optional[Dict[str, Any]]~~ |
| **RETURNS** | A dictionary of evaluation scores. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
## Language.use_params {#use_params tag="contextmanager, method"}
@ -356,9 +353,9 @@ their original weights after the block.
> nlp.to_disk("/tmp/checkpoint")
> ```
| Name | Type | Description |
| -------- | ---- | --------------------------------------------- |
| `params` | dict | A dictionary of parameters keyed by model ID. |
| Name | Description |
| -------- | ------------------------------------------------------ |
| `params` | A dictionary of parameters keyed by model ID. ~~dict~~ |
## Language.create_pipe {#create_pipe tag="method" new="2"}
@ -380,14 +377,14 @@ To create a component and add it to the pipeline, you should always use
> parser = nlp.create_pipe("parser")
> ```
| Name | Type | Description |
| ------------------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory_name` | str | Name of the registered component factory. |
| `name` | str | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. |
| _keyword-only_ | | |
| `config` <Tag variant="new">3</Tag> | `Dict[str, Any]` | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. |
| `validate` <Tag variant="new">3</Tag> | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| **RETURNS** | callable | The pipeline component. |
| Name | Description |
| ------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory_name` | Name of the registered component factory. ~~str~~ |
| `name` | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. ~~Optional[str]~~ |
| _keyword-only_ | |
| `config` <Tag variant="new">3</Tag> | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. ~~Optional[Dict[str, Any]]~~ |
| `validate` <Tag variant="new">3</Tag> | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ |
| **RETURNS** | The pipeline component. ~~Callable[[Doc], Doc]~~ |
## Language.add_pipe {#add_pipe tag="method" new="2"}
@ -423,19 +420,19 @@ component, adds it to the pipeline and returns it.
> nlp.add_pipe("ner", source=source_nlp)
> ```
| Name | Type | Description |
| -------------------------------------- | ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory_name` | str | Name of the registered component factory. |
| `name` | str | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. |
| _keyword-only_ | | |
| `before` | str / int | Component name or index to insert component directly before. |
| `after` | str / int | Component name or index to insert component directly after: |
| `first` | bool | Insert component first / not first in the pipeline. |
| `last` | bool | Insert component last / not last in the pipeline. |
| `config` <Tag variant="new">3</Tag> | `Dict[str, Any]` | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. |
| `source` <Tag variant="new">3</Tag> | `Language` | Optional source model to copy component from. If a source is provided, the `factory_name` is interpreted as the name of the component in the source pipeline. Make sure that the vocab, vectors and settings of the source model match the target model. |
| `validate` <Tag variant="new">3</Tag> | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| **RETURNS** <Tag variant="new">3</Tag> | callable | The pipeline component. |
| Name | Description |
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory_name` | Name of the registered component factory. ~~str~~ |
| `name` | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. ~~Optional[str]~~ |
| _keyword-only_ | |
| `before` | Component name or index to insert component directly before. ~~Optional[Union[str, int]]~~ |
| `after` | Component name or index to insert component directly after. ~~Optional[Union[str, int]]~~ |
| `first` | Insert component first / not first in the pipeline. ~~Optional[bool]~~ |
| `last` | Insert component last / not last in the pipeline. ~~Optional[bool]~~ |
| `config` <Tag variant="new">3</Tag> | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. ~~Optional[Dict[str, Any]]~~ |
| `source` <Tag variant="new">3</Tag> | Optional source model to copy component from. If a source is provided, the `factory_name` is interpreted as the name of the component in the source pipeline. Make sure that the vocab, vectors and settings of the source model match the target model. ~~Optional[Language]~~ |
| `validate` <Tag variant="new">3</Tag> | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ |
| **RETURNS** | The pipeline component. ~~Callable[[Doc], Doc]~~ |
## Language.has_factory {#has_factory tag="classmethod" new="3"}
@ -459,10 +456,10 @@ the `Language` base class, available to all subclasses.
> assert not Language.has_factory("component")
> ```
| Name | Type | Description |
| ----------- | ---- | ---------------------------------------------------------- |
| `name` | str | Name of the pipeline factory to check. |
| **RETURNS** | bool | Whether a factory of that name is registered on the class. |
| Name | Description |
| ----------- | ------------------------------------------------------------------- |
| `name` | Name of the pipeline factory to check. ~~str~~ |
| **RETURNS** | Whether a factory of that name is registered on the class. ~~bool~~ |
## Language.has_pipe {#has_pipe tag="method" new="2"}
@ -481,10 +478,10 @@ Check whether a component is present in the pipeline. Equivalent to
> assert nlp.has_pipe("my_component")
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------------------------- |
| `name` | str | Name of the pipeline component to check. |
| **RETURNS** | bool | Whether a component of that name exists in the pipeline. |
| Name | Description |
| ----------- | ----------------------------------------------------------------- |
| `name` | Name of the pipeline component to check. ~~str~~ |
| **RETURNS** | Whether a component of that name exists in the pipeline. ~~bool~~ |
## Language.get_pipe {#get_pipe tag="method" new="2"}
@ -497,28 +494,37 @@ Get a pipeline component for a given component name.
> custom_component = nlp.get_pipe("custom_component")
> ```
| Name | Type | Description |
| ----------- | -------- | -------------------------------------- |
| `name` | str | Name of the pipeline component to get. |
| **RETURNS** | callable | The pipeline component. |
| Name | Description |
| ----------- | ------------------------------------------------ |
| `name` | Name of the pipeline component to get. ~~str~~ |
| **RETURNS** | The pipeline component. ~~Callable[[Doc], Doc]~~ |
## Language.replace_pipe {#replace_pipe tag="method" new="2"}
Replace a component in the pipeline.
<Infobox title="Changed in v3.0" variant="warning">
As of v3.0, the `Language.replace_pipe` method doesn't take callables anymore
and instead expects the **name of a component factory** registered using
[`@Language.component`](/api/language#component) or
[`@Language.factory`](/api/language#factory).
</Infobox>
> #### Example
>
> ```python
> nlp.replace_pipe("parser", my_custom_parser)
> ```
| Name | Type | Description |
| ------------------------------------- | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | Name of the component to replace. |
| `component` | callable | The pipeline component to insert. |
| _keyword-only_ | | |
| `config` <Tag variant="new">3</Tag> | `Dict[str, Any]` | Optional config parameters to use for the new component. Will be merged with the `default_config` specified by the component factory. |
| `validate` <Tag variant="new">3</Tag> | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| Name | Description |
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `name` | Name of the component to replace. ~~str~~ |
| `component` | The factory name of the component to insert. ~~str~~ |
| _keyword-only_ | |
| `config` <Tag variant="new">3</Tag> | Optional config parameters to use for the new component. Will be merged with the `default_config` specified by the component factory. ~~Optional[Dict[str, Any]]~~ |
| `validate` <Tag variant="new">3</Tag> | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ |
## Language.rename_pipe {#rename_pipe tag="method" new="2"}
@ -533,10 +539,10 @@ added to the pipeline, you can also use the `name` argument on
> nlp.rename_pipe("parser", "spacy_parser")
> ```
| Name | Type | Description |
| ---------- | ---- | -------------------------------- |
| `old_name` | str | Name of the component to rename. |
| `new_name` | str | New name of the component. |
| Name | Description |
| ---------- | ---------------------------------------- |
| `old_name` | Name of the component to rename. ~~str~~ |
| `new_name` | New name of the component. ~~str~~ |
## Language.remove_pipe {#remove_pipe tag="method" new="2"}
@ -550,10 +556,10 @@ component function.
> assert name == "parser"
> ```
| Name | Type | Description |
| ----------- | ----- | ----------------------------------------------------- |
| `name` | str | Name of the component to remove. |
| **RETURNS** | tuple | A `(name, component)` tuple of the removed component. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------ |
| `name` | Name of the component to remove. ~~str~~ |
| **RETURNS** | A `(name, component)` tuple of the removed component. ~~Tuple[str, Callable[[Doc], Doc]]~~ |
## Language.select_pipes {#select_pipes tag="contextmanager, method" new="3"}
@ -589,12 +595,12 @@ As of spaCy v3.0, the `disable_pipes` method has been renamed to `select_pipes`:
</Infobox>
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------------------ |
| _keyword-only_ | | |
| `disable` | str / list | Name(s) of pipeline components to disable. |
| `enable` | str / list | Names(s) of pipeline components that will not be disabled. |
| **RETURNS** | `DisabledPipes` | The disabled pipes that can be restored by calling the object's `.restore()` method. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------ |
| _keyword-only_ | |
| `disable` | Name(s) of pipeline components to disable. ~~Optional[Union[str, Iterable[str]]]~~ |
| `enable` | Names(s) of pipeline components that will not be disabled. ~~Optional[Union[str, Iterable[str]]]~~ |
| **RETURNS** | The disabled pipes that can be restored by calling the object's `.restore()` method. ~~DisabledPipes~~ |
## Language.get_factory_meta {#get_factory_meta tag="classmethod" new="3"}
@ -613,10 +619,10 @@ information about the component and its default provided by the
> print(factory_meta.default_config)
> ```
| Name | Type | Description |
| ----------- | ----------------------------- | ------------------ |
| `name` | str | The factory name. |
| **RETURNS** | [`FactoryMeta`](#factorymeta) |  The factory meta. |
| Name | Description |
| ----------- | --------------------------------- |
| `name` | The factory name. ~~str~~ |
| **RETURNS** | The factory meta. ~~FactoryMeta~~ |
## Language.get_pipe_meta {#get_pipe_meta tag="method" new="3"}
@ -636,10 +642,10 @@ contains the information about the component and its default provided by the
> print(factory_meta.default_config)
> ```
| Name | Type | Description |
| ----------- | ----------------------------- | ---------------------------- |
| `name` | str | The pipeline component name. |
| **RETURNS** | [`FactoryMeta`](#factorymeta) |  The factory meta. |
| Name | Description |
| ----------- | ------------------------------------ |
| `name` | The pipeline component name. ~~str~~ |
| **RETURNS** | The factory meta. ~~FactoryMeta~~ |
## Language.analyze_pipes {#analyze_pipes tag="method" new="3"}
@ -725,18 +731,18 @@ token.ent_iob, token.ent_type
</Accordion>
| Name | Type | Description |
| -------------- | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `keys` | `List[str]` | The values to display in the table. Corresponds to attributes of the [`FactoryMeta`](/api/language#factorymeta). Defaults to `["assigns", "requires", "scores", "retokenizes"]`. |
| `pretty` | bool | Pretty-print the results as a table. Defaults to `False`. |
| **RETURNS** | dict | Dictionary containing the pipe analysis, keyed by `"summary"` (component meta by pipe), `"problems"` (attribute names by pipe) and `"attrs"` (pipes that assign and require an attribute, keyed by attribute). |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `keys` | The values to display in the table. Corresponds to attributes of the [`FactoryMeta`](/api/language#factorymeta). Defaults to `["assigns", "requires", "scores", "retokenizes"]`. ~~List[str]~~ |
| `pretty` | Pretty-print the results as a table. Defaults to `False`. ~~bool~~ |
| **RETURNS** | Dictionary containing the pipe analysis, keyed by `"summary"` (component meta by pipe), `"problems"` (attribute names by pipe) and `"attrs"` (pipes that assign and require an attribute, keyed by attribute). ~~Optional[Dict[str, Any]]~~ |
## Language.meta {#meta tag="property"}
Custom meta data for the Language class. If a model is loaded, contains meta
data of the model. The `Language.meta` is also what's serialized as the
`meta.json` when you save an `nlp` object to disk.
[`meta.json`](/api/data-formats#meta) when you save an `nlp` object to disk.
> #### Example
>
@ -744,9 +750,9 @@ data of the model. The `Language.meta` is also what's serialized as the
> print(nlp.meta)
> ```
| Name | Type | Description |
| ----------- | ---- | -------------- |
| **RETURNS** | dict | The meta data. |
| Name | Description |
| ----------- | --------------------------------- |
| **RETURNS** | The meta data. ~~Dict[str, Any]~~ |
## Language.config {#config tag="property" new="3"}
@ -765,9 +771,9 @@ subclass of the built-in `dict`. It supports the additional methods `to_disk`
> print(nlp.config.to_str())
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | ----------- |
| **RETURNS** | [`Config`](https://thinc.ai/docs/api-config#config) | The config. |
| Name | Description |
| ----------- | ---------------------- |
| **RETURNS** | The config. ~~Config~~ |
## Language.to_disk {#to_disk tag="method" new="2"}
@ -780,11 +786,11 @@ the model**.
> nlp.to_disk("/path/to/models")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Language.from_disk {#from_disk tag="method" new="2"}
@ -806,12 +812,12 @@ loaded object.
> nlp = English().from_disk("/path/to/en_model")
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Language` | The modified `Language` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Language` object. ~~Language~~ |
## Language.to_bytes {#to_bytes tag="method"}
@ -823,11 +829,11 @@ Serialize the current state to a binary string.
> nlp_bytes = nlp.to_bytes()
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Language` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------ |
| _keyword-only_ | |
| `exclude` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. ~~iterable~~ |
| **RETURNS** | The serialized form of the `Language` object. ~~bytes~~ |
## Language.from_bytes {#from_bytes tag="method"}
@ -845,35 +851,35 @@ available to the loaded object.
> nlp2.from_bytes(nlp_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Language` | The `Language` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Language` object. ~~Language~~ |
## Attributes {#attributes}
| Name | Type | Description |
| --------------------------------------------- | ---------------------- | ---------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | A container for the lexical types. |
| `tokenizer` | `Tokenizer` | The tokenizer. |
| `make_doc` | `Callable` | Callable that takes a string and returns a `Doc`. |
| `pipeline` | `List[str, Callable]` | List of `(name, component)` tuples describing the current processing pipeline, in order. |
| `pipe_names` <Tag variant="new">2</Tag> | `List[str]` | List of pipeline component names, in order. |
| `pipe_labels` <Tag variant="new">2.2</Tag> | `Dict[str, List[str]]` | List of labels set by the pipeline components, if available, keyed by component name. |
| `pipe_factories` <Tag variant="new">2.2</Tag> | `Dict[str, str]` | Dictionary of pipeline component names, mapped to their factory names. |
| `factories` | `Dict[str, Callable]` | All available factory functions, keyed by name. |
| `factory_names` <Tag variant="new">3</Tag> | `List[str]` | List of all available factory names. |
| `path` <Tag variant="new">2</Tag> | `Path` | Path to the model data directory, if a model is loaded. Otherwise `None`. |
| Name | Description |
| --------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | A container for the lexical types. ~~Vocab~~ |
| `tokenizer` | The tokenizer. ~~Tokenizer~~ |
| `make_doc` | Callable that takes a string and returns a `Doc`. ~~Callable[[str], Doc]~~ |
| `pipeline` | List of `(name, component)` tuples describing the current processing pipeline, in order. ~~List[str, Callable[[Doc], Doc]]~~ |
| `pipe_names` <Tag variant="new">2</Tag> | List of pipeline component names, in order. ~~List[str]~~ |
| `pipe_labels` <Tag variant="new">2.2</Tag> | List of labels set by the pipeline components, if available, keyed by component name. ~~Dict[str, List[str]]~~ |
| `pipe_factories` <Tag variant="new">2.2</Tag> | Dictionary of pipeline component names, mapped to their factory names. ~~Dict[str, str]~~ |
| `factories` | All available factory functions, keyed by name. ~~Dict[str, Callable[[...], Callable[[Doc], Doc]]]~~ |
| `factory_names` <Tag variant="new">3</Tag> | List of all available factory names. ~~List[str]~~ |
| `path` <Tag variant="new">2</Tag> | Path to the model data directory, if a model is loaded. Otherwise `None`. ~~Optional[Path]~~ |
## Class attributes {#class-attributes}
| Name | Type | Description |
| ---------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `Defaults` | class | Settings, data and factory methods for creating the `nlp` object and processing pipeline. |
| `lang` | str | Two-letter language ID, i.e. [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). |
| `default_config` | dict | Base [config](/usage/training#config) to use for [Language.config](/api/language#config). Defaults to [`default_config.cfg`](https://github.com/explosion/spaCy/tree/develop/spacy/default_config.cfg). |
| Name | Description |
| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `Defaults` | Settings, data and factory methods for creating the `nlp` object and processing pipeline. ~~Defaults~~ |
| `lang` | Two-letter language ID, i.e. [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). ~~str~~ |
| `default_config` | Base [config](/usage/training#config) to use for [Language.config](/api/language#config). Defaults to [`default_config.cfg`](https://github.com/explosion/spaCy/tree/develop/spacy/default_config.cfg). ~~Config~~ |
## Defaults {#defaults}
@ -906,17 +912,17 @@ customize the default language data:
> config = Config().from_str(DEFAULT_CONFIG)
> ```
| Name | Description |
| --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `stop_words` | List of stop words, used for `Token.is_stop`.<br />**Example:** [`stop_words.py`][stop_words.py] |
| `tokenizer_exceptions` | Tokenizer exception rules, string mapped to list of token attributes.<br />**Example:** [`de/tokenizer_exceptions.py`][de/tokenizer_exceptions.py] |
| `prefixes`, `suffixes`, `infixes` | Prefix, suffix and infix rules for the default tokenizer.<br />**Example:** [`puncutation.py`][punctuation.py] |
| `token_match` | Optional regex for matching strings that should never be split, overriding the infix rules.<br />**Example:** [`fr/tokenizer_exceptions.py`][fr/tokenizer_exceptions.py] |
| `url_match` | Regular expression for matching URLs. Prefixes and suffixes are removed before applying the match.<br />**Example:** [`tokenizer_exceptions.py`][tokenizer_exceptions.py] |
| `lex_attr_getters` | Custom functions for setting lexical attributes on tokens, e.g. `like_num`.<br />**Example:** [`lex_attrs.py`][lex_attrs.py] |
| `syntax_iterators` | Functions that compute views of a `Doc` object based on its syntax. At the moment, only used for [noun chunks](/usage/linguistic-features#noun-chunks).<br />**Example:** [`syntax_iterators.py`][syntax_iterators.py]. |
| `writing_system` | Information about the language's writing system, available via `Vocab.writing_system`. Defaults to: `{"direction": "ltr", "has_case": True, "has_letters": True}.`.<br />**Example:** [`zh/__init__.py`][zh/__init__.py] |
| `config` | Default [config](/usage/training#config) added to `nlp.config`. This can include references to custom tokenizers or lemmatizers.<br />**Example:** [`zh/__init__.py`][zh/__init__.py] |
| Name | Description |
| --------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `stop_words` | List of stop words, used for `Token.is_stop`.<br />**Example:** [`stop_words.py`][stop_words.py] ~~Set[str]~~ |
| `tokenizer_exceptions` | Tokenizer exception rules, string mapped to list of token attributes.<br />**Example:** [`de/tokenizer_exceptions.py`][de/tokenizer_exceptions.py] ~~Dict[str, List[dict]]~~ |
| `prefixes`, `suffixes`, `infixes` | Prefix, suffix and infix rules for the default tokenizer.<br />**Example:** [`puncutation.py`][punctuation.py] ~~Optional[List[Union[str, Pattern]]]~~ |
| `token_match` | Optional regex for matching strings that should never be split, overriding the infix rules.<br />**Example:** [`fr/tokenizer_exceptions.py`][fr/tokenizer_exceptions.py] ~~Optional[Pattern]~~ |
| `url_match` | Regular expression for matching URLs. Prefixes and suffixes are removed before applying the match.<br />**Example:** [`tokenizer_exceptions.py`][tokenizer_exceptions.py] ~~Optional[Pattern]~~ |
| `lex_attr_getters` | Custom functions for setting lexical attributes on tokens, e.g. `like_num`.<br />**Example:** [`lex_attrs.py`][lex_attrs.py] ~~Dict[int, Callable[[str], Any]]~~ |
| `syntax_iterators` | Functions that compute views of a `Doc` object based on its syntax. At the moment, only used for [noun chunks](/usage/linguistic-features#noun-chunks).<br />**Example:** [`syntax_iterators.py`][syntax_iterators.py]. ~~Dict[str, Callable[[Union[Doc, Span]], Iterator[Span]]]~~ |
| `writing_system` | Information about the language's writing system, available via `Vocab.writing_system`. Defaults to: `{"direction": "ltr", "has_case": True, "has_letters": True}.`.<br />**Example:** [`zh/__init__.py`][zh/__init__.py] ~~Dict[str, Any]~~ |
| `config` | Default [config](/usage/training#config) added to `nlp.config`. This can include references to custom tokenizers or lemmatizers.<br />**Example:** [`zh/__init__.py`][zh/__init__.py] ~~Config~~ |
[stop_words.py]:
https://github.com/explosion/spaCy/tree/master/spacy/lang/en/stop_words.py
@ -948,12 +954,12 @@ serialization by passing in the string names via the `exclude` argument.
> nlp.from_disk("./model-data", exclude=["ner"])
> ```
| Name | Description |
| ----------- | -------------------------------------------------- |
| `vocab` | The shared [`Vocab`](/api/vocab). |
| `tokenizer` | Tokenization rules and exceptions. |
| `meta` | The meta data, available as `Language.meta`. |
| ... | String names of pipeline components, e.g. `"ner"`. |
| Name | Description |
| ----------- | ------------------------------------------------------------------ |
| `vocab` | The shared [`Vocab`](/api/vocab). |
| `tokenizer` | Tokenization rules and exceptions. |
| `meta` | The meta data, available as [`Language.meta`](/api/language#meta). |
| ... | String names of pipeline components, e.g. `"ner"`. |
## FactoryMeta {#factorymeta new="3" tag="dataclass"}
@ -963,12 +969,12 @@ provided by the [`@Language.component`](/api/language#component) or
component is defined and stored on the `Language` class for each component
instance and factory instance.
| Name | Type | Description |
| ----------------------- | ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory` | str | The name of the registered component factory. |
| `default_config` | `Dict[str, Any]` | The default config, describing the default values of the factory arguments. |
| `assigns` | `Iterable[str]` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). |
| `requires` | `Iterable[str]` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).  |
| `retokenizes` | bool | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis).  |
| `scores` | `Iterable[str]` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). |
| `default_score_weights` | `Dict[str, float]` | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. |
| Name | Description |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `factory` | The name of the registered component factory. ~~str~~ |
| `default_config` | The default config, describing the default values of the factory arguments. ~~Dict[str, Any]~~ |
| `assigns` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
| `requires` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~  |
| `retokenizes` | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~bool~~  |
| `scores` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
| `default_score_weights` | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. ~~Dict[str, float]~~ |

165
website/docs/api/lemmatizer.md
View File

@ -36,11 +36,9 @@ tags is available in the pipeline and runs _before_ the lemmatizer.
The default config is defined by the pipeline component factory and describes
how the component should be configured. You can override its settings via the
`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
[`config.cfg` for training](/usage/training#config).
For examples of the lookups data formats used by the lookup and rule-based
lemmatizers, see the
[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) repo.
[`config.cfg` for training](/usage/training#config). For examples of the lookups
data formats used by the lookup and rule-based lemmatizers, see
[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data).
> #### Example
>
@ -49,12 +47,12 @@ lemmatizers, see the
> nlp.add_pipe("lemmatizer", config=config)
> ```
| Setting | Type | Description | Default |
| ----------- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- |
| `mode` | str | The lemmatizer mode, e.g. `"lookup"` or `"rule"`. | `"lookup"` |
| `lookups` | [`Lookups`](/api/lookups) | The lookups object containing the tables such as `"lemma_rules"`, `"lemma_index"`, `"lemma_exc"` and `"lemma_lookup"`. If `None`, default tables are loaded from `spacy-lookups-data`. | `None` |
| `overwrite` | bool | Whether to overwrite existing lemmas. | `False` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | **Not yet implemented:** the model to use. | `None` |
| Setting | Description |
| ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `mode` | The lemmatizer mode, e.g. `"lookup"` or `"rule"`. Defaults to `"lookup"`. ~~str~~ |
| `lookups` | The lookups object containing the tables such as `"lemma_rules"`, `"lemma_index"`, `"lemma_exc"` and `"lemma_lookup"`. If `None`, default tables are loaded from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data). Defaults to `None`. ~~Optional[Lookups]~~ |
| `overwrite` | Whether to overwrite existing lemmas. Defaults to `False`. ~~bool~~ |
| `model` | **Not yet implemented:** the model to use. ~~Model~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/lemmatizer.py
@ -77,15 +75,15 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| -------------- | ------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | [`Vocab`](/api/vocab) | The vocab. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | A model (not yet implemented). |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
| mode | str | The lemmatizer mode, e.g. `"lookup"` or `"rule"`. Defaults to `"lookup"`. |
| lookups | [`Lookups`](/api/lookups) | A lookups object containing the tables such as `"lemma_rules"`, `"lemma_index"`, `"lemma_exc"` and `"lemma_lookup"`. Defaults to `None`. |
| overwrite | bool | Whether to overwrite existing lemmas. |
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | **Not yet implemented:** The model to use. ~~Model~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | | |
| mode | The lemmatizer mode, e.g. `"lookup"` or `"rule"`. Defaults to `"lookup"`. ~~str~~ |
| lookups | A lookups object containing the tables such as `"lemma_rules"`, `"lemma_index"`, `"lemma_exc"` and `"lemma_lookup"`. Defaults to `None`. ~~Optional[Lookups]~~ |
| overwrite | Whether to overwrite existing lemmas. ~~bool~ |
## Lemmatizer.\_\_call\_\_ {#call tag="method"}
@ -102,10 +100,10 @@ and all pipeline components are applied to the `Doc` in order.
> processed = lemmatizer(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## Lemmatizer.pipe {#pipe tag="method"}
@ -121,12 +119,12 @@ applied to the `Doc` in order.
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## Lemmatizer.lookup_lemmatize {#lookup_lemmatize tag="method"}
@ -134,39 +132,39 @@ Lemmatize a token using a lookup-based approach. If no lemma is found, the
original string is returned. Languages can provide a
[lookup table](/usage/adding-languages#lemmatizer) via the `Lookups`.
| Name | Type | Description |
| ----------- | --------------------- | ------------------------------------- |
| `token` | [`Token`](/api/token) | The token to lemmatize. |
| **RETURNS** | `List[str]` | A list containing one or more lemmas. |
| Name | Description |
| ----------- | --------------------------------------------------- |
| `token` | The token to lemmatize. ~~Token~~ |
| **RETURNS** | A list containing one or more lemmas. ~~List[str]~~ |
## Lemmatizer.rule_lemmatize {#rule_lemmatize tag="method"}
Lemmatize a token using a rule-based approach. Typically relies on POS tags.
| Name | Type | Description |
| ----------- | --------------------- | ------------------------------------- |
| `token` | [`Token`](/api/token) | The token to lemmatize. |
| **RETURNS** | `List[str]` | A list containing one or more lemmas. |
| Name | Description |
| ----------- | --------------------------------------------------- |
| `token` | The token to lemmatize. ~~Token~~ |
| **RETURNS** | A list containing one or more lemmas. ~~List[str]~~ |
## Lemmatizer.is_base_form {#is_base_form tag="method"}
Check whether we're dealing with an uninflected paradigm, so we can avoid
lemmatization entirely.
| Name | Type | Description |
| ----------- | --------------------- | ------------------------------------------------------------------------------------------------------- |
| `token` | [`Token`](/api/token) | The token to analyze. |
| **RETURNS** | bool | Whether the token's attributes (e.g., part-of-speech tag, morphological features) describe a base form. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------- |
| `token` | The token to analyze. ~~Token~~ |
| **RETURNS** | Whether the token's attributes (e.g., part-of-speech tag, morphological features) describe a base form. ~~bool~~ |
## Lemmatizer.get_lookups_config {#get_lookups_config tag="classmethod"}
Returns the lookups configuration settings for a given mode for use in
[`Lemmatizer.load_lookups`](#load_lookups).
[`Lemmatizer.load_lookups`](/api/lemmatizer#load_lookups).
| Name | Type | Description |
| ----------- | ---- | ------------------------------------------------- |
| `mode` | str | The lemmatizer mode. |
| **RETURNS** | dict | The lookups configuration settings for this mode. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `mode` | The lemmatizer mode. ~~str~~ |
| **RETURNS** | The lookups configuration settings for this mode. Includes the keys `"required_tables"` and `"optional_tables"`, mapped to a list of table string names. ~~Dict[str, List[str]]~~ |
## Lemmatizer.load_lookups {#load_lookups tag="classmethod"}
@ -174,12 +172,12 @@ Load and validate lookups tables. If the provided lookups is `None`, load the
default lookups tables according to the language and mode settings. Confirm that
all required tables for the language and mode are present.
| Name | Type | Description |
| ----------- | ------------------------- | ---------------------------------------------------------------------------- |
| `lang` | str | The language. |
| `mode` | str | The lemmatizer mode. |
| `lookups` | [`Lookups`](/api/lookups) | The provided lookups, may be `None` if the default lookups should be loaded. |
| **RETURNS** | [`Lookups`](/api/lookups) | The lookups object. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------- |
| `lang` | The language. ~~str~~ |
| `mode` | The lemmatizer mode. ~~str~~ |
| `lookups` | The provided lookups, may be `None` if the default lookups should be loaded. ~~Optional[Lookups]~~ |
| **RETURNS** | The lookups. ~~Lookups~~ |
## Lemmatizer.to_disk {#to_disk tag="method"}
@ -192,11 +190,11 @@ Serialize the pipe to disk.
> lemmatizer.to_disk("/path/to/lemmatizer")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Lemmatizer.from_disk {#from_disk tag="method"}
@ -209,12 +207,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> lemmatizer.from_disk("/path/to/lemmatizer")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Lemmatizer` | The modified `Lemmatizer` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Lemmatizer` object. ~~Lemmatizer~~ |
## Lemmatizer.to_bytes {#to_bytes tag="method"}
@ -227,11 +225,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Lemmatizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `Lemmatizer` object. ~~bytes~~ |
## Lemmatizer.from_bytes {#from_bytes tag="method"}
@ -245,27 +243,20 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> lemmatizer.from_bytes(lemmatizer_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Lemmatizer` | The `Lemmatizer` object. |
## Lemmatizer.mode {#mode tag="property"}
The lemmatizer mode.
| Name | Type | Description |
| ----------- | ----- | -------------------- |
| **RETURNS** | `str` | The lemmatizer mode. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Lemmatizer` object. ~~Lemmatizer~~ |
## Attributes {#attributes}
| Name | Type | Description |
| --------- | --------------------------------- | ------------------- |
| `vocab` | The shared [`Vocab`](/api/vocab). |
| `lookups` | [`Lookups`](/api/lookups) | The lookups object. |
| Name | Description |
| --------- | ------------------------------------------- |
| `vocab` | The shared [`Vocab`](/api/vocab). ~~Vocab~~ |
| `lookups` | The lookups object. ~~Lookups~~ |
| `mode` | The lemmatizer mode. ~~str~~ |
## Serialization fields {#serialization-fields}

132
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@ -13,10 +13,10 @@ lemmatization depends on the part-of-speech tag).
Create a `Lexeme` object.
| Name | Type | Description |
| ------- | ------- | -------------------------- |
| `vocab` | `Vocab` | The parent vocabulary. |
| `orth` | int | The orth id of the lexeme. |
| Name | Description |
| ------- | ---------------------------------- |
| `vocab` | The parent vocabulary. ~~Vocab~~ |
| `orth` | The orth id of the lexeme. ~~int~~ |
## Lexeme.set_flag {#set_flag tag="method"}
@ -29,10 +29,10 @@ Change the value of a boolean flag.
> nlp.vocab["spaCy"].set_flag(COOL_FLAG, True)
> ```
| Name | Type | Description |
| --------- | ---- | ------------------------------------ |
| `flag_id` | int | The attribute ID of the flag to set. |
| `value` | bool | The new value of the flag. |
| Name | Description |
| --------- | -------------------------------------------- |
| `flag_id` | The attribute ID of the flag to set. ~~int~~ |
| `value` | The new value of the flag. ~~bool~~ |
## Lexeme.check_flag {#check_flag tag="method"}
@ -46,10 +46,10 @@ Check the value of a boolean flag.
> assert nlp.vocab["spaCy"].check_flag(MY_LIBRARY) == True
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------- |
| `flag_id` | int | The attribute ID of the flag to query. |
| **RETURNS** | bool | The value of the flag. |
| Name | Description |
| ----------- | ---------------------------------------------- |
| `flag_id` | The attribute ID of the flag to query. ~~int~~ |
| **RETURNS** | The value of the flag. ~~bool~~ |
## Lexeme.similarity {#similarity tag="method" model="vectors"}
@ -65,10 +65,10 @@ Compute a semantic similarity estimate. Defaults to cosine over vectors.
> assert apple_orange == orange_apple
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------------------------------------------------------- |
| other | - | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
| **RETURNS** | float | A scalar similarity score. Higher is more similar. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------- |
| other | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. ~~Union[Doc, Span, Token, Lexeme]~~ |
| **RETURNS** | A scalar similarity score. Higher is more similar. ~~float~~ |
## Lexeme.has_vector {#has_vector tag="property" model="vectors"}
@ -81,9 +81,9 @@ A boolean value indicating whether a word vector is associated with the lexeme.
> assert apple.has_vector
> ```
| Name | Type | Description |
| ----------- | ---- | ---------------------------------------------- |
| **RETURNS** | bool | Whether the lexeme has a vector data attached. |
| Name | Description |
| ----------- | ------------------------------------------------------- |
| **RETURNS** | Whether the lexeme has a vector data attached. ~~bool~~ |
## Lexeme.vector {#vector tag="property" model="vectors"}
@ -97,9 +97,9 @@ A real-valued meaning representation.
> assert apple.vector.shape == (300,)
> ```
| Name | Type | Description |
| ----------- | ---------------------------------------- | ----------------------------------------------------- |
| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A 1D numpy array representing the lexeme's semantics. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------ |
| **RETURNS** | A 1-dimensional array representing the lexeme's vector. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Lexeme.vector_norm {#vector_norm tag="property" model="vectors"}
@ -115,50 +115,50 @@ The L2 norm of the lexeme's vector representation.
> assert apple.vector_norm != pasta.vector_norm
> ```
| Name | Type | Description |
| ----------- | ----- | ----------------------------------------- |
| **RETURNS** | float | The L2 norm of the vector representation. |
| Name | Description |
| ----------- | --------------------------------------------------- |
| **RETURNS** | The L2 norm of the vector representation. ~~float~~ |
## Attributes {#attributes}
| Name | Type | Description |
| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | The lexeme's vocabulary. |
| `text` | str | Verbatim text content. |
| `orth` | int | ID of the verbatim text content. |
| `orth_` | str | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. |
| `rank` | int | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. |
| `flags` | int | Container of the lexeme's binary flags. |
| `norm` | int | The lexemes's norm, i.e. a normalized form of the lexeme text. |
| `norm_` | str | The lexemes's norm, i.e. a normalized form of the lexeme text. |
| `lower` | int | Lowercase form of the word. |
| `lower_` | str | Lowercase form of the word. |
| `shape` | int | Transform of the words's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `shape_` | str | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `prefix` | int | Length-N substring from the start of the word. Defaults to `N=1`. |
| `prefix_` | str | Length-N substring from the start of the word. Defaults to `N=1`. |
| `suffix` | int | Length-N substring from the end of the word. Defaults to `N=3`. |
| `suffix_` | str | Length-N substring from the start of the word. Defaults to `N=3`. |
| `is_alpha` | bool | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. |
| `is_ascii` | bool | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. |
| `is_digit` | bool | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. |
| `is_lower` | bool | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. |
| `is_upper` | bool | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. |
| `is_title` | bool | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. |
| `is_punct` | bool | Is the lexeme punctuation? |
| `is_left_punct` | bool | Is the lexeme a left punctuation mark, e.g. `(`? |
| `is_right_punct` | bool | Is the lexeme a right punctuation mark, e.g. `)`? |
| `is_space` | bool | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. |
| `is_bracket` | bool | Is the lexeme a bracket? |
| `is_quote` | bool | Is the lexeme a quotation mark? |
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the lexeme a currency symbol? |
| `like_url` | bool | Does the lexeme resemble a URL? |
| `like_num` | bool | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. |
| `like_email` | bool | Does the lexeme resemble an email address? |
| `is_oov` | bool | Does the lexeme have a word vector? |
| `is_stop` | bool | Is the lexeme part of a "stop list"? |
| `lang` | int | Language of the parent vocabulary. |
| `lang_` | str | Language of the parent vocabulary. |
| `prob` | float | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). |
| `cluster` | int | Brown cluster ID. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the lexeme. |
| Name | Description |
| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The lexeme's vocabulary. ~~Vocab~~ |
| `text` | Verbatim text content. ~~str~~ |
| `orth` | ID of the verbatim text content. ~~int~~ |
| `orth_` | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. ~~str~~ |
| `rank` | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ |
| `flags` | Container of the lexeme's binary flags. ~~int~~ |
| `norm` | The lexemes's norm, i.e. a normalized form of the lexeme text. ~~int~~ |
| `norm_` | The lexemes's norm, i.e. a normalized form of the lexeme text. ~~str~~ |
| `lower` | Lowercase form of the word. ~~int~~ |
| `lower_` | Lowercase form of the word. ~~str~~ |
| `shape` | Transform of the words's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~int~~ |
| `shape_` | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~str~~ |
| `prefix` | Length-N substring from the start of the word. Defaults to `N=1`. ~~int~~ |
| `prefix_` | Length-N substring from the start of the word. Defaults to `N=1`. ~~str~~ |
| `suffix` | Length-N substring from the end of the word. Defaults to `N=3`. ~~int~~ |
| `suffix_` | Length-N substring from the start of the word. Defaults to `N=3`. ~~str~~ |
| `is_alpha` | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. ~~bool~~ |
| `is_ascii` | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. ~~bool~~ |
| `is_digit` | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. ~~bool~~ |
| `is_lower` | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. ~~bool~~ |
| `is_upper` | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. ~~bool~~ |
| `is_title` | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. ~~bool~~ |
| `is_punct` | Is the lexeme punctuation? ~~bool~~ |
| `is_left_punct` | Is the lexeme a left punctuation mark, e.g. `(`? ~~bool~~ |
| `is_right_punct` | Is the lexeme a right punctuation mark, e.g. `)`? ~~bool~~ |
| `is_space` | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. ~~bool~~ |
| `is_bracket` | Is the lexeme a bracket? ~~bool~~ |
| `is_quote` | Is the lexeme a quotation mark? ~~bool~~ |
| `is_currency` <Tag variant="new">2.0.8</Tag> | Is the lexeme a currency symbol? ~~bool~~ |
| `like_url` | Does the lexeme resemble a URL? ~~bool~~ |
| `like_num` | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~ |
| `like_email` | Does the lexeme resemble an email address? ~~bool~~ |
| `is_oov` | Does the lexeme have a word vector? ~~bool~~ |
| `is_stop` | Is the lexeme part of a "stop list"? ~~bool~~ |
| `lang` | Language of the parent vocabulary. ~~int~~ |
| `lang_` | Language of the parent vocabulary. ~~str~~ |
| `prob` | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). ~~float~~ |
| `cluster` | Brown cluster ID. ~~int~~ |
| `sentiment` | A scalar value indicating the positivity or negativity of the lexeme. ~~float~~ |

134
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@ -24,10 +24,6 @@ Create a `Lookups` object.
> lookups = Lookups()
> ```
| Name | Type | Description |
| ----------- | --------- | ----------------------------- |
| **RETURNS** | `Lookups` | The newly constructed object. |
## Lookups.\_\_len\_\_ {#len tag="method"}
Get the current number of tables in the lookups.
@ -39,9 +35,9 @@ Get the current number of tables in the lookups.
> assert len(lookups) == 0
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------ |
| **RETURNS** | int | The number of tables in the lookups. |
| Name | Description |
| ----------- | -------------------------------------------- |
| **RETURNS** | The number of tables in the lookups. ~~int~~ |
## Lookups.\_\contains\_\_ {#contains tag="method"}
@ -56,10 +52,10 @@ Check if the lookups contain a table of a given name. Delegates to
> assert "some_table" in lookups
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------------------- |
| `name` | str | Name of the table. |
| **RETURNS** | bool | Whether a table of that name is in the lookups. |
| Name | Description |
| ----------- | -------------------------------------------------------- |
| `name` | Name of the table. ~~str~~ |
| **RETURNS** | Whether a table of that name is in the lookups. ~~bool~~ |
## Lookups.tables {#tables tag="property"}
@ -73,9 +69,9 @@ Get the names of all tables in the lookups.
> assert lookups.tables == ["some_table"]
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------- |
| **RETURNS** | list | Names of the tables in the lookups. |
| Name | Description |
| ----------- | ------------------------------------------------- |
| **RETURNS** | Names of the tables in the lookups. ~~List[str]~~ |
## Lookups.add_table {#add_table tag="method"}
@ -89,11 +85,11 @@ exists.
> lookups.add_table("some_table", {"foo": "bar"})
> ```
| Name | Type | Description |
| ----------- | ----------------------------- | ---------------------------------- |
| `name` | str | Unique name of the table. |
| `data` | dict | Optional data to add to the table. |
| **RETURNS** | [`Table`](/api/lookups#table) | The newly added table. |
| Name | Description |
| ----------- | ------------------------------------------- |
| `name` | Unique name of the table. ~~str~~ |
| `data` | Optional data to add to the table. ~~dict~~ |
| **RETURNS** | The newly added table. ~~Table~~ |
## Lookups.get_table {#get_table tag="method"}
@ -108,10 +104,10 @@ Get a table from the lookups. Raises an error if the table doesn't exist.
> assert table["foo"] == "bar"
> ```
| Name | Type | Description |
| ----------- | ----------------------------- | ------------------ |
| `name` | str | Name of the table. |
| **RETURNS** | [`Table`](/api/lookups#table) | The table. |
| Name | Description |
| ----------- | -------------------------- |
| `name` | Name of the table. ~~str~~ |
| **RETURNS** | The table. ~~Table~~ |
## Lookups.remove_table {#remove_table tag="method"}
@ -126,10 +122,10 @@ Remove a table from the lookups. Raises an error if the table doesn't exist.
> assert "some_table" not in lookups
> ```
| Name | Type | Description |
| ----------- | ----------------------------- | ---------------------------- |
| `name` | str | Name of the table to remove. |
| **RETURNS** | [`Table`](/api/lookups#table) | The removed table. |
| Name | Description |
| ----------- | ------------------------------------ |
| `name` | Name of the table to remove. ~~str~~ |
| **RETURNS** | The removed table. ~~Table~~ |
## Lookups.has_table {#has_table tag="method"}
@ -144,10 +140,10 @@ Check if the lookups contain a table of a given name. Equivalent to
> assert lookups.has_table("some_table")
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------------------- |
| `name` | str | Name of the table. |
| **RETURNS** | bool | Whether a table of that name is in the lookups. |
| Name | Description |
| ----------- | -------------------------------------------------------- |
| `name` | Name of the table. ~~str~~ |
| **RETURNS** | Whether a table of that name is in the lookups. ~~bool~~ |
## Lookups.to_bytes {#to_bytes tag="method"}
@ -159,9 +155,9 @@ Serialize the lookups to a bytestring.
> lookup_bytes = lookups.to_bytes()
> ```
| Name | Type | Description |
| ----------- | ----- | ----------------------- |
| **RETURNS** | bytes | The serialized lookups. |
| Name | Description |
| ----------- | --------------------------------- |
| **RETURNS** | The serialized lookups. ~~bytes~~ |
## Lookups.from_bytes {#from_bytes tag="method"}
@ -175,10 +171,10 @@ Load the lookups from a bytestring.
> lookups.from_bytes(lookup_bytes)
> ```
| Name | Type | Description |
| ------------ | --------- | ---------------------- |
| `bytes_data` | bytes | The data to load from. |
| **RETURNS** | `Lookups` | The loaded lookups. |
| Name | Description |
| ------------ | -------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| **RETURNS** | The loaded lookups. ~~Lookups~~ |
## Lookups.to_disk {#to_disk tag="method"}
@ -191,9 +187,9 @@ which will be created if it doesn't exist.
> lookups.to_disk("/path/to/lookups")
> ```
| Name | Type | Description |
| ------ | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| Name | Description |
| ------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
## Lookups.from_disk {#from_disk tag="method"}
@ -208,10 +204,10 @@ the file doesn't exist.
> lookups.from_disk("/path/to/lookups")
> ```
| Name | Type | Description |
| ----------- | ------------ | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| **RETURNS** | `Lookups` | The loaded lookups. |
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| **RETURNS** | The loaded lookups. ~~Lookups~~ |
## Table {#table tag="class, ordererddict"}
@ -236,9 +232,9 @@ Initialize a new table.
> assert table["foo"] == "bar"
> ```
| Name | Type | Description |
| ------ | ---- | ---------------------------------- |
| `name` | str | Optional table name for reference. |
| Name | Description |
| ------ | ------------------------------------------ |
| `name` | Optional table name for reference. ~~str~~ |
### Table.from_dict {#table.from_dict tag="classmethod"}
@ -252,11 +248,11 @@ Initialize a new table from a dict.
> table = Table.from_dict(data, name="some_table")
> ```
| Name | Type | Description |
| ----------- | ------- | ---------------------------------- |
| `data` | dict | The dictionary. |
| `name` | str | Optional table name for reference. |
| **RETURNS** | `Table` | The newly constructed object. |
| Name | Description |
| ----------- | ------------------------------------------ |
| `data` | The dictionary. ~~dict~~ |
| `name` | Optional table name for reference. ~~str~~ |
| **RETURNS** | The newly constructed object. ~~Table~~ |
### Table.set {#table.set tag="method"}
@ -272,10 +268,10 @@ Set a new key / value pair. String keys will be hashed. Same as
> assert table["foo"] == "bar"
> ```
| Name | Type | Description |
| ------- | --------- | ----------- |
| `key` | str / int | The key. |
| `value` | - | The value. |
| Name | Description |
| ------- | ---------------------------- |
| `key` | The key. ~~Union[str, int]~~ |
| `value` | The value. |
### Table.to_bytes {#table.to_bytes tag="method"}
@ -287,9 +283,9 @@ Serialize the table to a bytestring.
> table_bytes = table.to_bytes()
> ```
| Name | Type | Description |
| ----------- | ----- | --------------------- |
| **RETURNS** | bytes | The serialized table. |
| Name | Description |
| ----------- | ------------------------------- |
| **RETURNS** | The serialized table. ~~bytes~~ |
### Table.from_bytes {#table.from_bytes tag="method"}
@ -303,15 +299,15 @@ Load a table from a bytestring.
> table.from_bytes(table_bytes)
> ```
| Name | Type | Description |
| ------------ | ------- | ----------------- |
| `bytes_data` | bytes | The data to load. |
| **RETURNS** | `Table` | The loaded table. |
| Name | Description |
| ------------ | --------------------------- |
| `bytes_data` | The data to load. ~~bytes~~ |
| **RETURNS** | The loaded table. ~~Table~~ |
### Attributes {#table-attributes}
| Name | Type | Description |
| -------------- | --------------------------- | ----------------------------------------------------- |
| `name` | str | Table name. |
| `default_size` | int | Default size of bloom filters if no data is provided. |
| `bloom` | `preshed.bloom.BloomFilter` | The bloom filters. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `name` | Table name. ~~str~~ |
| `default_size` | Default size of bloom filters if no data is provided. ~~int~~ |
| `bloom` | The bloom filters. ~~preshed.BloomFilter~~ |

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@ -30,20 +30,20 @@ pattern keys correspond to a number of
[`Token` attributes](/api/token#attributes). The supported attributes for
rule-based matching are:
| Attribute | Type |  Description |
| -------------------------------------- | ---- | ------------------------------------------------------------------------------------------------------ |
| `ORTH` | str | The exact verbatim text of a token. |
| `TEXT` <Tag variant="new">2.1</Tag> | str | The exact verbatim text of a token. |
| `LOWER` | str | The lowercase form of the token text. |
|  `LENGTH` | int | The length of the token text. |
|  `IS_ALPHA`, `IS_ASCII`, `IS_DIGIT` | bool | Token text consists of alphabetic characters, ASCII characters, digits. |
|  `IS_LOWER`, `IS_UPPER`, `IS_TITLE` | bool | Token text is in lowercase, uppercase, titlecase. |
|  `IS_PUNCT`, `IS_SPACE`, `IS_STOP` | bool | Token is punctuation, whitespace, stop word. |
|  `LIKE_NUM`, `LIKE_URL`, `LIKE_EMAIL` | bool | Token text resembles a number, URL, email. |
|  `POS`, `TAG`, `DEP`, `LEMMA`, `SHAPE` | str | The token's simple and extended part-of-speech tag, dependency label, lemma, shape. |
| `ENT_TYPE` | str | The token's entity label. |
| `_` <Tag variant="new">2.1</Tag> | dict | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). |
| `OP` | str | Operator or quantifier to determine how often to match a token pattern. |
| Attribute |  Description |
| -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
| `ORTH` | The exact verbatim text of a token. ~~str~~ |
| `TEXT` <Tag variant="new">2.1</Tag> | The exact verbatim text of a token. ~~str~~ |
| `LOWER` | The lowercase form of the token text. ~~str~~ |
|  `LENGTH` | The length of the token text. ~~int~~ |
|  `IS_ALPHA`, `IS_ASCII`, `IS_DIGIT` | Token text consists of alphabetic characters, ASCII characters, digits. ~~bool~~ |
|  `IS_LOWER`, `IS_UPPER`, `IS_TITLE` | Token text is in lowercase, uppercase, titlecase. ~~bool~~ |
|  `IS_PUNCT`, `IS_SPACE`, `IS_STOP` | Token is punctuation, whitespace, stop word. ~~bool~~ |
|  `LIKE_NUM`, `LIKE_URL`, `LIKE_EMAIL` | Token text resembles a number, URL, email. ~~bool~~ |
|  `POS`, `TAG`, `DEP`, `LEMMA`, `SHAPE` | The token's simple and extended part-of-speech tag, dependency label, lemma, shape. ~~str~~ |
| `ENT_TYPE` | The token's entity label. ~~str~~ |
| `_` <Tag variant="new">2.1</Tag> | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). ~~Dict[str, Any]~~ |
| `OP` | Operator or quantifier to determine how often to match a token pattern. ~~str~~ |
Operators and quantifiers define **how often** a token pattern should be
matched:
@ -75,11 +75,11 @@ it compares to another value.
> ]
> ```
| Attribute | Type | Description |
| -------------------------- | ---------- | --------------------------------------------------------------------------------- |
| `IN` | any | Attribute value is member of a list. |
| `NOT_IN` | any | Attribute value is _not_ member of a list. |
| `==`, `>=`, `<=`, `>`, `<` | int, float | Attribute value is equal, greater or equal, smaller or equal, greater or smaller. |
| Attribute | Description |
| -------------------------- | ------------------------------------------------------------------------------------------------------- |
| `IN` | Attribute value is member of a list. ~~Any~~ |
| `NOT_IN` | Attribute value is _not_ member of a list. ~~Any~~ |
| `==`, `>=`, `<=`, `>`, `<` | Attribute value is equal, greater or equal, smaller or equal, greater or smaller. ~~Union[int, float]~~ |
## Matcher.\_\_init\_\_ {#init tag="method"}
@ -95,10 +95,10 @@ string where an integer is expected) or unexpected property names.
> matcher = Matcher(nlp.vocab)
> ```
| Name | Type | Description |
| --------------------------------------- | ------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. |
| `validate` <Tag variant="new">2.1</Tag> | bool | Validate all patterns added to this matcher. |
| Name | Description |
| --------------------------------------- | ----------------------------------------------------------------------------------------------------- |
| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ |
| `validate` <Tag variant="new">2.1</Tag> | Validate all patterns added to this matcher. ~~bool~~ |
## Matcher.\_\_call\_\_ {#call tag="method"}
@ -116,10 +116,10 @@ Find all token sequences matching the supplied patterns on the `Doc` or `Span`.
> matches = matcher(doc)
> ```
| Name | Type | Description |
| ----------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `doclike` | `Doc`/`Span` | The `Doc` or `Span` to match over. |
| **RETURNS** | list | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doclike` | The `Doc` or `Span` to match over. ~~Union[Doc, Span]~~ |
| **RETURNS** | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. ~~List[Tuple[int, int, int]]~~ |
## Matcher.pipe {#pipe tag="method"}
@ -134,13 +134,13 @@ Match a stream of documents, yielding them in turn.
> pass
> ```
| Name | Type | Description |
| --------------------------------------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `docs` | iterable | A stream of documents or spans. |
| `batch_size` | int | The number of documents to accumulate into a working set. |
| `return_matches` <Tag variant="new">2.1</Tag> | bool | Yield the match lists along with the docs, making results `(doc, matches)` tuples. |
| `as_tuples` | bool | Interpret the input stream as `(doc, context)` tuples, and yield `(result, context)` tuples out. If both `return_matches` and `as_tuples` are `True`, the output will be a sequence of `((doc, matches), context)` tuples. |
| **YIELDS** | `Doc` | Documents, in order. |
| Name | Description |
| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `docs` | A stream of documents or spans. ~~Iterable[Union[Doc, Span]]~~ |
| `batch_size` | The number of documents to accumulate into a working set. ~~int~~ |
| `return_matches` <Tag variant="new">2.1</Tag> | Yield the match lists along with the docs, making results `(doc, matches)` tuples. ~~bool~~ |
| `as_tuples` | Interpret the input stream as `(doc, context)` tuples, and yield `(result, context)` tuples out. If both `return_matches` and `as_tuples` are `True`, the output will be a sequence of `((doc, matches), context)` tuples. ~~bool~~ |
| **YIELDS** | Documents, in order. ~~Union[Doc, Tuple[Doc, Any], Tuple[Tuple[Doc, Any], Any]]~~ |
## Matcher.\_\_len\_\_ {#len tag="method" new="2"}
@ -157,9 +157,9 @@ patterns.
> assert len(matcher) == 1
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------- |
| **RETURNS** | int | The number of rules. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The number of rules. ~~int~~ |
## Matcher.\_\_contains\_\_ {#contains tag="method" new="2"}
@ -174,10 +174,10 @@ Check whether the matcher contains rules for a match ID.
> assert "Rule" in matcher
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------------------------- |
| `key` | str | The match ID. |
| **RETURNS** | bool | Whether the matcher contains rules for this match ID. |
| Name | Description |
| ----------- | -------------------------------------------------------------- |
| `key` | The match ID. ~~str~~ |
| **RETURNS** | Whether the matcher contains rules for this match ID. ~~bool~~ |
## Matcher.add {#add tag="method" new="2"}
@ -217,13 +217,13 @@ patterns = [[{"TEXT": "Google"}, {"TEXT": "Now"}], [{"TEXT": "GoogleNow"}]]
</Infobox>
| Name | Type | Description |
| ----------------------------------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `patterns` | `List[List[dict]]` | Match pattern. A pattern consists of a list of dicts, where each dict describes a token. |
| _keyword-only_ | | |
| `on_match` | callable / `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
| `greedy` <Tag variant="new">3</Tag> | str | Optional filter for greedy matches. Can either be `"FIRST"` or `"LONGEST"`. |
| Name | Description |
| ----------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `match_id` | An ID for the thing you're matching. ~~str~~ |
| `patterns` | Match pattern. A pattern consists of a list of dicts, where each dict describes a token. ~~List[List[Dict[str, Any]]]~~ |
| _keyword-only_ | |
| `on_match` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. ~~Optional[Callable[[Matcher, Doc, int, List[tuple], Any]]~~ |
| `greedy` <Tag variant="new">3</Tag> | Optional filter for greedy matches. Can either be `"FIRST"` or `"LONGEST"`. ~~Optional[str]~~ |
## Matcher.remove {#remove tag="method" new="2"}
@ -239,9 +239,9 @@ exist.
> assert "Rule" not in matcher
> ```
| Name | Type | Description |
| ----- | ---- | ------------------------- |
| `key` | str | The ID of the match rule. |
| Name | Description |
| ----- | --------------------------------- |
| `key` | The ID of the match rule. ~~str~~ |
## Matcher.get {#get tag="method" new="2"}
@ -255,7 +255,7 @@ Retrieve the pattern stored for a key. Returns the rule as an
> on_match, patterns = matcher.get("Rule")
> ```
| Name | Type | Description |
| ----------- | ----- | --------------------------------------------- |
| `key` | str | The ID of the match rule. |
| **RETURNS** | tuple | The rule, as an `(on_match, patterns)` tuple. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------- |
| `key` | The ID of the match rule. ~~str~~ |
| **RETURNS** | The rule, as an `(on_match, patterns)` tuple. ~~Tuple[Optional[Callable], List[List[dict]]]~~ |

142
website/docs/api/morphanalysis.md
View File

@ -1,142 +0,0 @@
---
title: MorphAnalysis
tag: class
source: spacy/tokens/morphanalysis.pyx
---
Stores a single morphological analysis.
## MorphAnalysis.\_\_init\_\_ {#init tag="method"}
Initialize a MorphAnalysis object from a UD FEATS string or a dictionary of
morphological features.
> #### Example
>
> ```python
> from spacy.tokens import MorphAnalysis
>
> feats = "Feat1=Val1|Feat2=Val2"
> m = MorphAnalysis(nlp.vocab, feats)
> ```
| Name | Type | Description |
| ---------- | ------------------ | --------------------------- |
| `vocab` | `Vocab` | The vocab. |
| `features` | `Union[Dict, str]` | The morphological features. |
## MorphAnalysis.\_\_contains\_\_ {#contains tag="method"}
Whether a feature/value pair is in the analysis.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert "Feat1=Val1" in morph
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------- |
| **RETURNS** | `str` | A feature/value pair in the analysis. |
## MorphAnalysis.\_\_iter\_\_ {#iter tag="method"}
Iterate over the feature/value pairs in the analysis.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val3|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert list(morph) == ["Feat1=Va1", "Feat1=Val3", "Feat2=Val2"]
> ```
| Name | Type | Description |
| ---------- | ----- | ------------------------------------- |
| **YIELDS** | `str` | A feature/value pair in the analysis. |
## MorphAnalysis.\_\_len\_\_ {#len tag="method"}
Returns the number of features in the analysis.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert len(morph) == 3
> ```
| Name | Type | Description |
| ----------- | ----- | --------------------------------------- |
| **RETURNS** | `int` | The number of features in the analysis. |
## MorphAnalysis.\_\_str\_\_ {#str tag="method"}
Returns the morphological analysis in the UD FEATS string format.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert str(morph) == feats
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------- |
| **RETURNS** | `str` | The analysis in UD FEATS format. |
## MorphAnalysis.get {#get tag="method"}
Retrieve values for a feature by field.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert morph.get("Feat1") == ["Val1", "Val2"]
> ```
| Name | Type | Description |
| ----------- | ------ | ---------------------------------- |
| `field` | `str` | The field to retrieve. |
| **RETURNS** | `list` | A list of the individual features. |
## MorphAnalysis.to_dict {#to_dict tag="method"}
Produce a dict representation of the analysis, in the same format as the tag
map.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert morph.to_dict() == {"Feat1": "Val1,Val2", "Feat2": "Val2"}
> ```
| Name | Type | Description |
| ----------- | ------ | ---------------------------------------- |
| **RETURNS** | `dict` | The dict representation of the analysis. |
## MorphAnalysis.from_id {#from_id tag="classmethod"}
Create a morphological analysis from a given hash ID.
> #### Example
>
> ```python
> feats = "Feat1=Val1|Feat2=Val2"
> hash = nlp.vocab.strings[feats]
> morph = MorphAnalysis.from_id(nlp.vocab, hash)
> assert str(morph) == feats
> ```
| Name | Type | Description |
| ------- | ------- | -------------------------------- |
| `vocab` | `Vocab` | The vocab. |
| `key` | `int` | The hash of the features string. |

186
website/docs/api/morphologizer.md
View File

@ -32,9 +32,9 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("morphologizer", config=config)
> ```
| Setting | Type | Description | Default |
| ------- | ------------------------------------------ | ----------------- | ----------------------------------- |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [Tagger](/api/architectures#Tagger) |
| Setting | Description |
| ------- | ------------------------------------------------------------------------------------------------------- |
| `model` | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/morphologizer.pyx
@ -42,7 +42,9 @@ https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/morphologizer.pyx
## Morphologizer.\_\_init\_\_ {#init tag="method"}
Initialize the morphologizer.
Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
> #### Example
>
@ -59,18 +61,14 @@ Initialize the morphologizer.
> morphologizer = Morphologizer(nlp.vocab, model)
> ```
Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| -------------- | ------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
| `labels_morph` | dict | Mapping of morph + POS tags to morph labels. |
| `labels_pos` | dict | Mapping of morph + POS tags to POS tags. |
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | |
| `labels_morph` | Mapping of morph + POS tags to morph labels. ~~Dict[str, str]~~ |
| `labels_pos` | Mapping of morph + POS tags to POS tags. ~~Dict[str, str]~~ |
## Morphologizer.\_\_call\_\_ {#call tag="method"}
@ -90,10 +88,10 @@ delegate to the [`predict`](/api/morphologizer#predict) and
> processed = morphologizer(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## Morphologizer.pipe {#pipe tag="method"}
@ -112,12 +110,12 @@ applied to the `Doc` in order. Both [`__call__`](/api/morphologizer#call) and
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## Morphologizer.begin_training {#begin_training tag="method"}
@ -138,13 +136,13 @@ setting up the label scheme based on the data.
> optimizer = morphologizer.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/sentencerecognizer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Morphologizer.predict {#predict tag="method"}
@ -158,10 +156,10 @@ modifying them.
> scores = morphologizer.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ----------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | - | The model's prediction for each document. |
| Name | Description |
| ----------- | ------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The model's prediction for each document. |
## Morphologizer.set_annotations {#set_annotations tag="method"}
@ -175,10 +173,10 @@ Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
> morphologizer.set_annotations([doc1, doc2], scores)
> ```
| Name | Type | Description |
| -------- | --------------- | ------------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `Morphologizer.predict`. |
| Name | Description |
| -------- | ------------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `Morphologizer.predict`. |
## Morphologizer.update {#update tag="method"}
@ -195,15 +193,15 @@ Delegates to [`predict`](/api/morphologizer#predict) and
> losses = morphologizer.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/sentencerecognizer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Morphologizer.get_loss {#get_loss tag="method"}
@ -218,11 +216,11 @@ predicted scores.
> loss, d_loss = morphologizer.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | - | Scores representing the model's predictions. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------- |
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
| `scores` | Scores representing the model's predictions. |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
## Morphologizer.create_optimizer {#create_optimizer tag="method"}
@ -235,9 +233,9 @@ Create an optimizer for the pipeline component.
> optimizer = morphologizer.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Morphologizer.use_params {#use_params tag="method, contextmanager"}
@ -252,9 +250,9 @@ context, the original parameters are restored.
> morphologizer.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## Morphologizer.add_label {#add_label tag="method"}
@ -268,10 +266,10 @@ both `pos` and `morph`, the label should include the UPOS as the feature `POS`.
> morphologizer.add_label("Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin")
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------------------------- |
| `label` | str | The label to add. |
| **RETURNS** | int | `0` if the label is already present, otherwise `1`. |
| Name | Description |
| ----------- | ----------------------------------------------------------- |
| `label` | The label to add. ~~str~~ |
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
## Morphologizer.to_disk {#to_disk tag="method"}
@ -284,11 +282,11 @@ Serialize the pipe to disk.
> morphologizer.to_disk("/path/to/morphologizer")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Morphologizer.from_disk {#from_disk tag="method"}
@ -301,12 +299,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> morphologizer.from_disk("/path/to/morphologizer")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Morphologizer` | The modified `Morphologizer` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Morphologizer` object. ~~Morphologizer~~ |
## Morphologizer.to_bytes {#to_bytes tag="method"}
@ -319,11 +317,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Morphologizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `Morphologizer` object. ~~bytes~~ |
## Morphologizer.from_bytes {#from_bytes tag="method"}
@ -337,19 +335,19 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> morphologizer.from_bytes(morphologizer_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Morphologizer` | The `Morphologizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Morphologizer` object. ~~Morphologizer~~ |
## Morphologizer.labels {#labels tag="property"}
The labels currently added to the component in Universal Dependencies
[FEATS format](https://universaldependencies.org/format.html#morphological-annotation).
Note that even for a blank component, this will always include the internal
empty label `_`. If POS features are used, the labels will include the
The labels currently added to the component in the Universal Dependencies
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
format. Note that even for a blank component, this will always include the
internal empty label `_`. If POS features are used, the labels will include the
coarse-grained POS as the feature `POS`.
> #### Example
@ -359,9 +357,9 @@ coarse-grained POS as the feature `POS`.
> assert "Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin" in morphologizer.labels
> ```
| Name | Type | Description |
| ----------- | ----- | ---------------------------------- |
| **RETURNS** | tuple | The labels added to the component. |
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
## Serialization fields {#serialization-fields}

209
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@ -7,7 +7,8 @@ source: spacy/morphology.pyx
Store the possible morphological analyses for a language, and index them by
hash. To save space on each token, tokens only know the hash of their
morphological analysis, so queries of morphological attributes are delegated to
this class.
this class. See [`MorphAnalysis`](/api/morphology#morphanalysis) for the
container storing a single morphological analysis.
## Morphology.\_\_init\_\_ {#init tag="method"}
@ -21,15 +22,17 @@ Create a Morphology object.
> morphology = Morphology(strings)
> ```
| Name | Type | Description |
| --------- | ------------- | ----------------- |
| `strings` | `StringStore` | The string store. |
| Name | Description |
| --------- | --------------------------------- |
| `strings` | The string store. ~~StringStore~~ |
## Morphology.add {#add tag="method"}
Insert a morphological analysis in the morphology table, if not already present.
The morphological analysis may be provided in the UD FEATS format as a string or
in the tag map dictionary format. Returns the hash of the new analysis.
The morphological analysis may be provided in the Universal Dependencies
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
format as a string or in the tag map dictionary format. Returns the hash of the
new analysis.
> #### Example
>
@ -39,9 +42,9 @@ in the tag map dictionary format. Returns the hash of the new analysis.
> assert hash == nlp.vocab.strings[feats]
> ```
| Name | Type | Description |
| ---------- | ------------------ | --------------------------- |
| `features` | `Union[Dict, str]` | The morphological features. |
| Name | Description |
| ---------- | ------------------------------------------------ |
| `features` | The morphological features. ~~Union[Dict, str]~~ |
## Morphology.get {#get tag="method"}
@ -53,16 +56,20 @@ in the tag map dictionary format. Returns the hash of the new analysis.
> assert nlp.vocab.morphology.get(hash) == feats
> ```
Get the FEATS string for the hash of the morphological analysis.
Get the
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
string for the hash of the morphological analysis.
| Name | Type | Description |
| ------- | ---- | --------------------------------------- |
| `morph` | int | The hash of the morphological analysis. |
| Name | Description |
| ------- | ----------------------------------------------- |
| `morph` | The hash of the morphological analysis. ~~int~~ |
## Morphology.feats_to_dict {#feats_to_dict tag="staticmethod"}
Convert a string FEATS representation to a dictionary of features and values in
the same format as the tag map.
Convert a string
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
representation to a dictionary of features and values in the same format as the
tag map.
> #### Example
>
@ -72,14 +79,16 @@ the same format as the tag map.
> assert d == {"Feat1": "Val1", "Feat2": "Val2"}
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------------------------------------ |
| `feats` | str | The morphological features in Universal Dependencies FEATS format. |
| **RETURNS** | dict | The morphological features as a dictionary. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| `feats` | The morphological features in Universal Dependencies [FEATS](https://universaldependencies.org/format.html#morphological-annotation) format. ~~str~~ |
| **RETURNS** | The morphological features as a dictionary. ~~Dict[str, str]~~ |
## Morphology.dict_to_feats {#dict_to_feats tag="staticmethod"}
Convert a dictionary of features and values to a string FEATS representation.
Convert a dictionary of features and values to a string
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
representation.
> #### Example
>
@ -89,15 +98,157 @@ Convert a dictionary of features and values to a string FEATS representation.
> assert f == "Feat1=Val1|Feat2=Val2"
> ```
| Name | Type | Description |
| ------------ | ----------------- | --------------------------------------------------------------------- |
| `feats_dict` | `Dict[str, Dict]` | The morphological features as a dictionary. |
| **RETURNS** | str | The morphological features as in Universal Dependencies FEATS format. |
| Name | Description |
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `feats_dict` | The morphological features as a dictionary. ~~Dict[str, str]~~ |
| **RETURNS** | The morphological features as in Universal Dependencies [FEATS](https://universaldependencies.org/format.html#morphological-annotation) format. ~~str~~ |
## Attributes {#attributes}
| Name | Type | Description |
| ------------- | ----- | -------------------------------------------- |
| `FEATURE_SEP` | `str` | The FEATS feature separator. Default is `|`. |
| `FIELD_SEP` | `str` | The FEATS field separator. Default is `=`. |
| `VALUE_SEP` | `str` | The FEATS value separator. Default is `,`. |
| Name | Description |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------ |
| `FEATURE_SEP` | The [FEATS](https://universaldependencies.org/format.html#morphological-annotation) feature separator. Default is `|`. ~~str~~ |
| `FIELD_SEP` | The [FEATS](https://universaldependencies.org/format.html#morphological-annotation) field separator. Default is `=`. ~~str~~ |
| `VALUE_SEP` | The [FEATS](https://universaldependencies.org/format.html#morphological-annotation) value separator. Default is `,`. ~~str~~ |
## MorphAnalysis {#morphanalysis tag="class" source="spacy/tokens/morphanalysis.pyx"}
Stores a single morphological analysis.
### MorphAnalysis.\_\_init\_\_ {#morphanalysis-init tag="method"}
Initialize a MorphAnalysis object from a Universal Dependencies
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
string or a dictionary of morphological features.
> #### Example
>
> ```python
> from spacy.tokens import MorphAnalysis
>
> feats = "Feat1=Val1|Feat2=Val2"
> m = MorphAnalysis(nlp.vocab, feats)
> ```
| Name | Description |
| ---------- | ---------------------------------------------------------- |
| `vocab` | The vocab. ~~Vocab~~ |
| `features` | The morphological features. ~~Union[Dict[str, str], str]~~ |
### MorphAnalysis.\_\_contains\_\_ {#morphanalysis-contains tag="method"}
Whether a feature/value pair is in the analysis.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert "Feat1=Val1" in morph
> ```
| Name | Description |
| ----------- | --------------------------------------------- |
| **RETURNS** | A feature/value pair in the analysis. ~~str~~ |
### MorphAnalysis.\_\_iter\_\_ {#morphanalysis-iter tag="method"}
Iterate over the feature/value pairs in the analysis.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val3|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert list(morph) == ["Feat1=Va1", "Feat1=Val3", "Feat2=Val2"]
> ```
| Name | Description |
| ---------- | --------------------------------------------- |
| **YIELDS** | A feature/value pair in the analysis. ~~str~~ |
### MorphAnalysis.\_\_len\_\_ {#morphanalysis-len tag="method"}
Returns the number of features in the analysis.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert len(morph) == 3
> ```
| Name | Description |
| ----------- | ----------------------------------------------- |
| **RETURNS** | The number of features in the analysis. ~~int~~ |
### MorphAnalysis.\_\_str\_\_ {#morphanalysis-str tag="method"}
Returns the morphological analysis in the Universal Dependencies
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
string format.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert str(morph) == feats
> ```
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| **RETURNS** | The analysis in the Universal Dependencies [FEATS](https://universaldependencies.org/format.html#morphological-annotation) format. ~~str~~ |
### MorphAnalysis.get {#morphanalysis-get tag="method"}
Retrieve values for a feature by field.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert morph.get("Feat1") == ["Val1", "Val2"]
> ```
| Name | Description |
| ----------- | ------------------------------------------------ |
| `field` | The field to retrieve. ~~str~~ |
| **RETURNS** | A list of the individual features. ~~List[str]~~ |
### MorphAnalysis.to_dict {#morphanalysis-to_dict tag="method"}
Produce a dict representation of the analysis, in the same format as the tag
map.
> #### Example
>
> ```python
> feats = "Feat1=Val1,Val2|Feat2=Val2"
> morph = MorphAnalysis(nlp.vocab, feats)
> assert morph.to_dict() == {"Feat1": "Val1,Val2", "Feat2": "Val2"}
> ```
| Name | Description |
| ----------- | ----------------------------------------------------------- |
| **RETURNS** | The dict representation of the analysis. ~~Dict[str, str]~~ |
### MorphAnalysis.from_id {#morphanalysis-from_id tag="classmethod"}
Create a morphological analysis from a given hash ID.
> #### Example
>
> ```python
> feats = "Feat1=Val1|Feat2=Val2"
> hash = nlp.vocab.strings[feats]
> morph = MorphAnalysis.from_id(nlp.vocab, hash)
> assert str(morph) == feats
> ```
| Name | Description |
| ------- | ---------------------------------------- |
| `vocab` | The vocab. ~~Vocab~~ |
| `key` | The hash of the features string. ~~int~~ |

62
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@ -36,11 +36,11 @@ be shown.
> matcher = PhraseMatcher(nlp.vocab)
> ```
| Name | Type | Description |
| --------------------------------------- | --------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. |
| `attr` <Tag variant="new">2.1</Tag> | int / str | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. |
| `validate` <Tag variant="new">2.1</Tag> | bool | Validate patterns added to the matcher. |
| Name | Description |
| --------------------------------------- | ------------------------------------------------------------------------------------------------------ |
| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ |
| `attr` <Tag variant="new">2.1</Tag> | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. ~~Union[int, str]~~ |
| `validate` <Tag variant="new">2.1</Tag> | Validate patterns added to the matcher. ~~bool~~ |
## PhraseMatcher.\_\_call\_\_ {#call tag="method"}
@ -57,10 +57,10 @@ Find all token sequences matching the supplied patterns on the `Doc`.
> matches = matcher(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `doc` | `Doc` | The document to match over. |
| **RETURNS** | list | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `match_id` is the ID of the added match pattern. |
| Name | Description |
| ----------- | ----------------------------------- |
| `doc` | The document to match over. ~~Doc~~ |
| **RETURNS** | list | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `match_id` is the ID of the added match pattern. ~~List[Tuple[int, int, int]]~~ |
<Infobox title="Note on retrieving the string representation of the match_id" variant="warning">
@ -87,11 +87,13 @@ Match a stream of documents, yielding them in turn.
> pass
> ```
| Name | Type | Description |
| ------------ | -------- | --------------------------------------------------------- |
| `docs` | iterable | A stream of documents. |
| `batch_size` | int | The number of documents to accumulate into a working set. |
| **YIELDS** | `Doc` | Documents, in order. |
| Name | Description |
| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `docs` | A stream of documents. ~~Iterable[Doc]~~ |
| `batch_size` | The number of documents to accumulate into a working set. ~~int~~ |
| `return_matches` <Tag variant="new">2.1</Tag> | Yield the match lists along with the docs, making results `(doc, matches)` tuples. ~~bool~~ |
| `as_tuples` | Interpret the input stream as `(doc, context)` tuples, and yield `(result, context)` tuples out. If both `return_matches` and `as_tuples` are `True`, the output will be a sequence of `((doc, matches), context)` tuples. ~~bool~~ |
| **YIELDS** | Documents and optional matches or context in order. ~~Union[Doc, Tuple[Doc, Any], Tuple[Tuple[Doc, Any], Any]]~~ |
## PhraseMatcher.\_\_len\_\_ {#len tag="method"}
@ -108,9 +110,9 @@ patterns.
> assert len(matcher) == 1
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------- |
| **RETURNS** | int | The number of rules. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The number of rules. ~~int~~ |
## PhraseMatcher.\_\_contains\_\_ {#contains tag="method"}
@ -125,10 +127,10 @@ Check whether the matcher contains rules for a match ID.
> assert "OBAMA" in matcher
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------------------------- |
| `key` | str | The match ID. |
| **RETURNS** | bool | Whether the matcher contains rules for this match ID. |
| Name | Description |
| ----------- | -------------------------------------------------------------- |
| `key` | The match ID. ~~str~~ |
| **RETURNS** | Whether the matcher contains rules for this match ID. ~~bool~~ |
## PhraseMatcher.add {#add tag="method"}
@ -165,12 +167,12 @@ patterns = [nlp("health care reform"), nlp("healthcare reform")]
</Infobox>
| Name | Type | Description |
| -------------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `docs` | list | `Doc` objects of the phrases to match. |
| _keyword-only_ | | |
| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
| Name | Description |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. ~~str~~ |
| `docs` | `Doc` objects of the phrases to match. ~~List[Doc]~~ |
| _keyword-only_ | | |
| `on_match` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. ~~Optional[Callable[[Matcher, Doc, int, List[tuple], Any]]~~ |
## PhraseMatcher.remove {#remove tag="method" new="2.2"}
@ -187,6 +189,6 @@ does not exist.
> assert "OBAMA" not in matcher
> ```
| Name | Type | Description |
| ----- | ---- | ------------------------- |
| `key` | str | The ID of the match rule. |
| Name | Description |
| ----- | --------------------------------- |
| `key` | The ID of the match rule. ~~str~~ |

216
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@ -45,12 +45,12 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#create_pipe).
| Name | Type | Description |
| ------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| `**cfg` | | Additional config parameters and settings. Will be available as the dictionary `Pipe.cfg` and is serialized with the component. |
| Name | Description |
| ------- | ------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], Any]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| `**cfg` | Additional config parameters and settings. Will be available as the dictionary `Pipe.cfg` and is serialized with the component. |
## Pipe.\_\_call\_\_ {#call tag="method"}
@ -70,10 +70,10 @@ and all pipeline components are applied to the `Doc` in order. Both
> processed = pipe(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## Pipe.pipe {#pipe tag="method"}
@ -91,12 +91,12 @@ applied to the `Doc` in order. Both [`__call__`](/api/pipe#call) and
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------- |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | The processed documents in order. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## Pipe.begin_training {#begin_training tag="method"}
@ -116,13 +116,13 @@ setting up the label scheme based on the data.
> optimizer = pipe.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/pipe#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Pipe.predict {#predict tag="method"}
@ -142,10 +142,10 @@ This method needs to be overwritten with your own custom `predict` method.
> scores = pipe.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ----------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | - | The model's prediction for each document. |
| Name | Description |
| ----------- | ------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The model's prediction for each document. |
## Pipe.set_annotations {#set_annotations tag="method"}
@ -166,10 +166,10 @@ method.
> pipe.set_annotations(docs, scores)
> ```
| Name | Type | Description |
| -------- | --------------- | ---------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `Pipe.predict`. |
| Name | Description |
| -------- | ------------------------------------------------ |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `Tagger.predict`. |
## Pipe.update {#update tag="method"}
@ -184,15 +184,15 @@ predictions and gold-standard annotations, and update the component's model.
> losses = pipe.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/pipe#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Pipe.rehearse {#rehearse tag="method,experimental" new="3"}
@ -208,14 +208,14 @@ the "catastrophic forgetting" problem. This feature is experimental.
> losses = pipe.rehearse(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Pipe.get_loss {#get_loss tag="method"}
@ -230,11 +230,11 @@ predicted scores.
> loss, d_loss = ner.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | | Scores representing the model's predictions. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------- |
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
| `scores` | Scores representing the model's predictions. |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
## Pipe.score {#score tag="method" new="3"}
@ -246,10 +246,10 @@ Score a batch of examples.
> scores = pipe.score(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | --------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, e.g. produced by the [`Scorer`](/api/scorer). |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------- |
| `examples` | The examples to score. ~~Iterable[Example]~~ |
| **RETURNS** | The scores, e.g. produced by the [`Scorer`](/api/scorer). ~~Dict[str, Union[float, Dict[str, float]]]~~ |
## Pipe.create_optimizer {#create_optimizer tag="method"}
@ -263,26 +263,9 @@ Create an optimizer for the pipeline component. Defaults to
> optimizer = pipe.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## Pipe.add_label {#add_label tag="method"}
Add a new label to the pipe. It's possible to extend pretrained models with new
labels, but care should be taken to avoid the "catastrophic forgetting" problem.
> #### Example
>
> ```python
> pipe = nlp.add_pipe("your_custom_pipe")
> pipe.add_label("MY_LABEL")
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------------------------- |
| `label` | str | The label to add. |
| **RETURNS** | int | `0` if the label is already present, otherwise `1`. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Pipe.use_params {#use_params tag="method, contextmanager"}
@ -297,9 +280,26 @@ context, the original parameters are restored.
> pipe.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## Pipe.add_label {#add_label tag="method"}
Add a new label to the pipe. It's possible to extend pretrained models with new
labels, but care should be taken to avoid the "catastrophic forgetting" problem.
> #### Example
>
> ```python
> pipe = nlp.add_pipe("your_custom_pipe")
> pipe.add_label("MY_LABEL")
> ```
| Name | Description |
| ----------- | ----------------------------------------------------------- |
| `label` | The label to add. ~~str~~ |
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
## Pipe.to_disk {#to_disk tag="method"}
@ -312,11 +312,11 @@ Serialize the pipe to disk.
> pipe.to_disk("/path/to/pipe")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Pipe.from_disk {#from_disk tag="method"}
@ -329,12 +329,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> pipe.from_disk("/path/to/pipe")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Pipe` | The modified pipe. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified pipe. ~~Pipe~~ |
## Pipe.to_bytes {#to_bytes tag="method"}
@ -347,11 +347,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the pipe. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the pipe. ~~bytes~~ |
## Pipe.from_bytes {#from_bytes tag="method"}
@ -365,21 +365,21 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> pipe.from_bytes(pipe_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Pipe` | The pipe. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The pipe. ~~Pipe~~ |
## Attributes {#attributes}
| Name | Type | Description |
| ------- | ------------------------------------------ | ----------------------------------------------------------------------------------------------------- |
| `vocab` | [`Vocab`](/api/vocab) | The shared vocabulary that's passed in on initialization. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model powering the component. |
| `name` | str | The name of the component instance in the pipeline. Can be used in the losses. |
| `cfg` | dict | Keyword arguments passed to [`Pipe.__init__`](/api/pipe#init). Will be serialized with the component. |
| Name | Description |
| ------- | ------------------------------------------------------------------------------------------------------------------------ |
| `vocab` | The shared vocabulary that's passed in on initialization. ~~Vocab~~ |
| `model` | The model powering the component. ~~Model[List[Doc], Any]~~ |
| `name` | The name of the component instance in the pipeline. Can be used in the losses. ~~str~~ |
| `cfg` | Keyword arguments passed to [`Pipe.__init__`](/api/pipe#init). Will be serialized with the component. ~~Dict[str, Any]~~ |
## Serialization fields {#serialization-fields}

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@ -33,10 +33,10 @@ all other components.
</Infobox>
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------ |
| `doc` | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
| **RETURNS** | `Doc` | The modified `Doc` with merged noun chunks. |
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
| **RETURNS** | The modified `Doc` with merged noun chunks. ~~Doc~~ |
## merge_entities {#merge_entities tag="function"}
@ -63,10 +63,10 @@ components to the end of the pipeline and after all other components.
</Infobox>
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------ |
| `doc` | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
| **RETURNS** | `Doc` | The modified `Doc` with merged entities. |
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
| **RETURNS** | The modified `Doc` with merged entities. ~~Doc~~ |
## merge_subtokens {#merge_subtokens tag="function" new="2.1"}
@ -102,8 +102,8 @@ end of the pipeline and after all other components.
</Infobox>
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------ |
| `doc` | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
| `label` | str | The subtoken dependency label. Defaults to `"subtok"`. |
| **RETURNS** | `Doc` | The modified `Doc` with merged subtokens. |
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
| `label` | The subtoken dependency label. Defaults to `"subtok"`. ~~str~~ |
| **RETURNS** | The modified `Doc` with merged subtokens. ~~Doc~~ |

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@ -27,9 +27,9 @@ Create a new `Scorer`.
> scorer = Scorer(nlp)
> ```
| Name | Type | Description |
| ----- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nlp` | Language | The pipeline to use for scoring, where each pipeline component may provide a scoring method. If none is provided, then a default pipeline for the multi-language code `xx` is constructed containing: `senter`, `tagger`, `morphologizer`, `parser`, `ner`, `textcat`. |
| Name | Description |
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nlp` | The pipeline to use for scoring, where each pipeline component may provide a scoring method. If none is provided, then a default pipeline for the multi-language code `xx` is constructed containing: `senter`, `tagger`, `morphologizer`, `parser`, `ner`, `textcat`. ~~Language~~ |
## Scorer.score {#score tag="method"}
@ -55,10 +55,10 @@ attribute being scored:
> scores = scorer.score(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | --------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
| **RETURNS** | `Dict` | A dictionary of scores. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------- |
| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
| **RETURNS** | A dictionary of scores. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
## Scorer.score_tokenization {#score_tokenization tag="staticmethod" new="3"}
@ -74,10 +74,10 @@ Scores the tokenization:
> scores = Scorer.score_tokenization(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | --------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
| **RETURNS** | `Dict` | A dictionary containing the scores `token_acc`, `token_p`, `token_r`, `token_f`. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------- |
| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
| **RETURNS** | `Dict` | A dictionary containing the scores `token_acc`, `token_p`, `token_r`, `token_f`. ~~Dict[str, float]]~~ |
## Scorer.score_token_attr {#score_token_attr tag="staticmethod" new="3"}
@ -90,18 +90,19 @@ Scores a single token attribute.
> print(scores["pos_acc"])
> ```
| Name | Type | Description |
| -------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
| `attr` | `str` | The attribute to score. |
| _keyword-only_ | | |
| `getter` | `Callable` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
| **RETURNS** | `Dict[str, float]` | A dictionary containing the score `{attr}_acc`. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
| `attr` | The attribute to score. ~~str~~ |
| _keyword-only_ | |
| `getter` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. ~~Callable[[Token, str], Any]~~ |
| **RETURNS** | A dictionary containing the score `{attr}_acc`. ~~Dict[str, float]~~ |
## Scorer.score_token_attr_per_feat {#score_token_attr_per_feat tag="staticmethod" new="3"}
Scores a single token attribute per feature for a token attribute in
[UFEATS](https://universaldependencies.org/format.html#morphological-annotation)
Scores a single token attribute per feature for a token attribute in the
Universal Dependencies
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
format.
> #### Example
@ -111,13 +112,13 @@ format.
> print(scores["morph_per_feat"])
> ```
| Name | Type | Description |
| -------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
| `attr` | `str` | The attribute to score. |
| _keyword-only_ | | |
| `getter` | `Callable` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
| **RETURNS** | `Dict` | A dictionary containing the per-feature PRF scores under the key `{attr}_per_feat`. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
| `attr` | The attribute to score. ~~str~~ |
| _keyword-only_ | |
| `getter` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. ~~Callable[[Token, str], Any]~~ |
| **RETURNS** | A dictionary containing the per-feature PRF scores under the key `{attr}_per_feat`. ~~Dict[str, Dict[str, float]]~~ |
## Scorer.score_spans {#score_spans tag="staticmethod" new="3"}
@ -130,13 +131,13 @@ Returns PRF scores for labeled or unlabeled spans.
> print(scores["ents_f"])
> ```
| Name | Type | Description |
| -------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
| `attr` | `str` | The attribute to score. |
| _keyword-only_ | | |
| `getter` | `Callable` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`. |
| **RETURNS** | `Dict` | A dictionary containing the PRF scores under the keys `{attr}_p`, `{attr}_r`, `{attr}_f` and the per-type PRF scores under `{attr}_per_type`. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
| `attr` | The attribute to score. ~~str~~ |
| _keyword-only_ | |
| `getter` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`. ~~Callable[[Doc, str], Iterable[Span]]~~ |
| **RETURNS** | A dictionary containing the PRF scores under the keys `{attr}_p`, `{attr}_r`, `{attr}_f` and the per-type PRF scores under `{attr}_per_type`. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
## Scorer.score_deps {#score_deps tag="staticmethod" new="3"}
@ -159,16 +160,16 @@ Calculate the UAS, LAS, and LAS per type scores for dependency parses.
> print(scores["dep_uas"], scores["dep_las"])
> ```
| Name | Type | Description |
| --------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
| `attr` | `str` | The attribute containing the dependency label. |
| _keyword-only_ | | |
| `getter` | `Callable` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
| `head_attr` | `str` | The attribute containing the head token. |
| `head_getter` | `callable` | Defaults to `getattr`. If provided, `head_getter(token, attr)` should return the head for an individual `Token`. |
| `ignore_labels` | `Tuple` | Labels to ignore while scoring (e.g., `punct`). |
| **RETURNS** | `Dict` | A dictionary containing the scores: `{attr}_uas`, `{attr}_las`, and `{attr}_las_per_type`. |
| Name | Description |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
| `attr` | The attribute to score. ~~str~~ |
| _keyword-only_ | |
| `getter` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. ~~Callable[[Token, str], Any]~~ |
| `head_attr` | The attribute containing the head token. ~~str~~ |
| `head_getter` | Defaults to `getattr`. If provided, `head_getter(token, attr)` should return the head for an individual `Token`. ~~Callable[[Doc, str], Token]~~ |
| `ignore_labels` | Labels to ignore while scoring (e.g. `"punct"`). ~~Iterable[str]~~ |
| **RETURNS** | A dictionary containing the scores: `{attr}_uas`, `{attr}_las`, and `{attr}_las_per_type`. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
## Scorer.score_cats {#score_cats tag="staticmethod" new="3"}
@ -195,13 +196,13 @@ depends on the scorer settings:
> print(scores["cats_macro_auc"])
> ```
| Name | Type | Description |
| ---------------- | ------------------- | ------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
| `attr` | `str` | The attribute to score. |
| _keyword-only_ | | |
| `getter` | `Callable` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. |
| labels | `Iterable[str]` | The set of possible labels. Defaults to `[]`. |
| `multi_label` | `bool` | Whether the attribute allows multiple labels. Defaults to `True`. |
| `positive_label` | `str` | The positive label for a binary task with exclusive classes. Defaults to `None`. |
| **RETURNS** | `Dict` | A dictionary containing the scores, with inapplicable scores as `None`. |
| Name | Description |
| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
| `attr` | The attribute to score. ~~str~~ |
| _keyword-only_ | |
| `getter` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. ~~Callable[[Doc, str], Dict[str, float]]~~ |
| labels | The set of possible labels. Defaults to `[]`. ~~Iterable[str]~~ |
| `multi_label` | Whether the attribute allows multiple labels. Defaults to `True`. ~~bool~~ |
| `positive_label` | The positive label for a binary task with exclusive classes. Defaults to `None`. ~~Optional[str]~~ |
| **RETURNS** | A dictionary containing the scores, with inapplicable scores as `None`. ~~Dict[str, Optional[float]]~~ |

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@ -29,9 +29,9 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("senter", config=config)
> ```
| Setting | Type | Description | Default |
| ------- | ------------------------------------------ | ----------------- | ----------------------------------- |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [Tagger](/api/architectures#Tagger) |
| Setting | Description |
| ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/senter.pyx
@ -60,11 +60,11 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| ------- | ------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| Name | Description |
| ------- | -------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
## SentenceRecognizer.\_\_call\_\_ {#call tag="method"}
@ -85,10 +85,10 @@ and all pipeline components are applied to the `Doc` in order. Both
> processed = senter(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## SentenceRecognizer.pipe {#pipe tag="method"}
@ -107,12 +107,12 @@ and [`pipe`](/api/sentencerecognizer#pipe) delegate to the
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## SentenceRecognizer.begin_training {#begin_training tag="method"}
@ -132,13 +132,13 @@ setting up the label scheme based on the data.
> optimizer = senter.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/sentencerecognizer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## SentenceRecognizer.predict {#predict tag="method"}
@ -152,10 +152,10 @@ modifying them.
> scores = senter.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ----------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | - | The model's prediction for each document. |
| Name | Description |
| ----------- | ------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The model's prediction for each document. |
## SentenceRecognizer.set_annotations {#set_annotations tag="method"}
@ -169,10 +169,10 @@ Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
> senter.set_annotations([doc1, doc2], scores)
> ```
| Name | Type | Description |
| -------- | --------------- | ------------------------------------------------------------ |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `SentenceRecognizer.predict`. |
| Name | Description |
| -------- | ------------------------------------------------------------ |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `SentenceRecognizer.predict`. |
## SentenceRecognizer.update {#update tag="method"}
@ -189,15 +189,15 @@ Delegates to [`predict`](/api/sentencerecognizer#predict) and
> losses = senter.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/sentencerecognizer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## SentenceRecognizer.rehearse {#rehearse tag="method,experimental" new="3"}
@ -213,14 +213,14 @@ the "catastrophic forgetting" problem. This feature is experimental.
> losses = senter.rehearse(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## SentenceRecognizer.get_loss {#get_loss tag="method"}
@ -235,11 +235,11 @@ predicted scores.
> loss, d_loss = senter.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | - | Scores representing the model's predictions. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------- |
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
| `scores` | Scores representing the model's predictions. |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
## SentenceRecognizer.score {#score tag="method" new="3"}
@ -251,10 +251,10 @@ Score a batch of examples.
> scores = senter.score(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | ------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | The examples to score. ~~Iterable[Example]~~ |
| **RETURNS** | The scores, produced by [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"`, `"tag"` and `"lemma"`. ~~Dict[str, float]~~ |
## SentenceRecognizer.create_optimizer {#create_optimizer tag="method"}
@ -267,9 +267,9 @@ Create an optimizer for the pipeline component.
> optimizer = senter.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## SentenceRecognizer.use_params {#use_params tag="method, contextmanager"}
@ -284,9 +284,9 @@ context, the original parameters are restored.
> senter.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## SentenceRecognizer.to_disk {#to_disk tag="method"}
@ -299,11 +299,11 @@ Serialize the pipe to disk.
> senter.to_disk("/path/to/senter")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## SentenceRecognizer.from_disk {#from_disk tag="method"}
@ -316,12 +316,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> senter.from_disk("/path/to/senter")
> ```
| Name | Type | Description |
| -------------- | -------------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `SentenceRecognizer` | The modified `SentenceRecognizer` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `SentenceRecognizer` object. ~~SentenceRecognizer~~ |
## SentenceRecognizer.to_bytes {#to_bytes tag="method"}
@ -334,11 +334,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `SentenceRecognizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `SentenceRecognizer` object. ~~bytes~~ |
## SentenceRecognizer.from_bytes {#from_bytes tag="method"}
@ -352,12 +352,12 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> senter.from_bytes(senter_bytes)
> ```
| Name | Type | Description |
| -------------- | -------------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `SentenceRecognizer` | The `SentenceRecognizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `SentenceRecognizer` object. ~~SentenceRecognizer~~ |
## Serialization fields {#serialization-fields}

70
website/docs/api/sentencizer.md
View File

@ -28,9 +28,9 @@ how the component should be configured. You can override its settings via the
> nlp.add_pipe("entity_ruler", config=config)
> ```
| Setting | Type | Description | Default |
| ------------- | ----------- | ---------------------------------------------------------------------------------------------------------- | ------- |
| `punct_chars` | `List[str]` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults if not set. | `None` |
| Setting | Description |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `punct_chars` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults if not set. Defaults to `None`. ~~Optional[List[str]]~~ | `None` |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/sentencizer.pyx
@ -51,10 +51,10 @@ Initialize the sentencizer.
> sentencizer = Sentencizer()
> ```
| Name | Type | Description |
| -------------- | ----------- | ----------------------------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `punct_chars` | `List[str]` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `punct_chars` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults. ~~Optional[List[str]]~~ |
```python
### punct_chars defaults
@ -87,10 +87,10 @@ the component has been added to the pipeline using
> assert len(list(doc.sents)) == 2
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------ |
| `doc` | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
| **RETURNS** | `Doc` | The modified `Doc` with added sentence boundaries. |
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
| **RETURNS** | The modified `Doc` with added sentence boundaries. ~~Doc~~ |
## Sentencizer.pipe {#pipe tag="method"}
@ -106,12 +106,12 @@ applied to the `Doc` in order.
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------- |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | The processed documents in order. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## Sentencizer.score {#score tag="method" new="3"}
@ -123,10 +123,10 @@ Score a batch of examples.
> scores = sentencizer.score(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | ------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------------------------------- |
| `examples` | The examples to score. ~~Iterable[Example]~~ |
| **RETURNS** | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). ~~Dict[str, Union[float, Dict[str, float]]~~ |
## Sentencizer.to_disk {#to_disk tag="method"}
@ -142,9 +142,9 @@ a file `sentencizer.json`. This also happens automatically when you save an
> sentencizer.to_disk("/path/to/sentencizer.json")
> ```
| Name | Type | Description |
| ------ | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a JSON file, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| Name | Description |
| ------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a JSON file, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
## Sentencizer.from_disk {#from_disk tag="method"}
@ -159,10 +159,10 @@ added to its pipeline.
> sentencizer.from_disk("/path/to/sentencizer.json")
> ```
| Name | Type | Description |
| ----------- | ------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a JSON file. Paths may be either strings or `Path`-like objects. |
| **RETURNS** | `Sentencizer` | The modified `Sentencizer` object. |
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a JSON file. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| **RETURNS** | The modified `Sentencizer` object. ~~Sentencizer~~ |
## Sentencizer.to_bytes {#to_bytes tag="method"}
@ -176,9 +176,9 @@ Serialize the sentencizer settings to a bytestring.
> sentencizer_bytes = sentencizer.to_bytes()
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------- |
| **RETURNS** | bytes | The serialized data. |
| Name | Description |
| ----------- | ------------------------------ |
| **RETURNS** | The serialized data. ~~bytes~~ |
## Sentencizer.from_bytes {#from_bytes tag="method"}
@ -192,7 +192,7 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> sentencizer.from_bytes(sentencizer_bytes)
> ```
| Name | Type | Description |
| ------------ | ------------- | ---------------------------------- |
| `bytes_data` | bytes | The bytestring to load. |
| **RETURNS** | `Sentencizer` | The modified `Sentencizer` object. |
| Name | Description |
| ------------ | -------------------------------------------------- |
| `bytes_data` | The bytestring to load. ~~bytes~~ |
| **RETURNS** | The modified `Sentencizer` object. ~~Sentencizer~~ |

240
website/docs/api/span.md
View File

@ -18,14 +18,14 @@ Create a Span object from the slice `doc[start : end]`.
> assert [t.text for t in span] == ["it", "back", "!"]
> ```
| Name | Type | Description |
| -------- | ---------------------------------------- | --------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `start` | int | The index of the first token of the span. |
| `end` | int | The index of the first token after the span. |
| `label` | int / str | A label to attach to the span, e.g. for named entities. As of v2.1, the label can also be a string. |
| `kb_id` | int / str | A knowledge base ID to attach to the span, e.g. for named entities. The ID can be an integer or a string. |
| `vector` | `numpy.ndarray[ndim=1, dtype="float32"]` | A meaning representation of the span. |
| Name | Description |
| -------- | --------------------------------------------------------------------------------------- |
| `doc` | The parent document. ~~Doc~~ |
| `start` | The index of the first token of the span. ~~int~~ |
| `end` | The index of the first token after the span. ~~int~~ |
| `label` | A label to attach to the span, e.g. for named entities. ~~Union[str, int]~~ |
| `kb_id` | A knowledge base ID to attach to the span, e.g. for named entities. ~~Union[str, int]~~ |
| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Span.\_\_getitem\_\_ {#getitem tag="method"}
@ -39,10 +39,10 @@ Get a `Token` object.
> assert span[1].text == "back"
> ```
| Name | Type | Description |
| ----------- | ------- | --------------------------------------- |
| `i` | int | The index of the token within the span. |
| **RETURNS** | `Token` | The token at `span[i]`. |
| Name | Description |
| ----------- | ----------------------------------------------- |
| `i` | The index of the token within the span. ~~int~~ |
| **RETURNS** | The token at `span[i]`. ~~Token~~ |
Get a `Span` object.
@ -54,10 +54,10 @@ Get a `Span` object.
> assert span[1:3].text == "back!"
> ```
| Name | Type | Description |
| ----------- | ------ | -------------------------------- |
| `start_end` | tuple | The slice of the span to get. |
| **RETURNS** | `Span` | The span at `span[start : end]`. |
| Name | Description |
| ----------- | ------------------------------------------------- |
| `start_end` | The slice of the span to get. ~~Tuple[int, int]~~ |
| **RETURNS** | The span at `span[start : end]`. ~~Span~~ |
## Span.\_\_iter\_\_ {#iter tag="method"}
@ -71,9 +71,9 @@ Iterate over `Token` objects.
> assert [t.text for t in span] == ["it", "back", "!"]
> ```
| Name | Type | Description |
| ---------- | ------- | ----------------- |
| **YIELDS** | `Token` | A `Token` object. |
| Name | Description |
| ---------- | --------------------------- |
| **YIELDS** | A `Token` object. ~~Token~~ |
## Span.\_\_len\_\_ {#len tag="method"}
@ -87,9 +87,9 @@ Get the number of tokens in the span.
> assert len(span) == 3
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------- |
| **RETURNS** | int | The number of tokens in the span. |
| Name | Description |
| ----------- | ----------------------------------------- |
| **RETURNS** | The number of tokens in the span. ~~int~~ |
## Span.set_extension {#set_extension tag="classmethod" new="2"}
@ -107,14 +107,14 @@ For details, see the documentation on
> assert doc[1:4]._.has_city
> ```
| Name | Type | Description |
| --------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | Name of the attribute to set by the extension. For example, `"my_attr"` will be available as `span._.my_attr`. |
| `default` | - | Optional default value of the attribute if no getter or method is defined. |
| `method` | callable | Set a custom method on the object, for example `span._.compare(other_span)`. |
| `getter` | callable | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute. |
| `setter` | callable | Setter function that takes the `Span` and a value, and modifies the object. Is called when the user writes to the `Span._` attribute. |
| `force` | bool | Force overwriting existing attribute. |
| Name | Description |
| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the attribute to set by the extension. For example, `"my_attr"` will be available as `span._.my_attr`. ~~str~~ |
| `default` | Optional default value of the attribute if no getter or method is defined. ~~Optional[Any]~~ |
| `method` | Set a custom method on the object, for example `span._.compare(other_span)`. ~~Optional[Callable[[Span, ...], Any]]~~ |
| `getter` | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute. ~~Optional[Callable[[Span], Any]]~~ |
| `setter` | Setter function that takes the `Span` and a value, and modifies the object. Is called when the user writes to the `Span._` attribute. ~~Optional[Callable[[Span, Any], None]]~~ |
| `force` | Force overwriting existing attribute. ~~bool~~ |
## Span.get_extension {#get_extension tag="classmethod" new="2"}
@ -131,10 +131,10 @@ Look up a previously registered extension by name. Returns a 4-tuple
> assert extension == (False, None, None, None)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------- |
| `name` | str | Name of the extension. |
| **RETURNS** | tuple | A `(default, method, getter, setter)` tuple of the extension. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Span.has_extension {#has_extension tag="classmethod" new="2"}
@ -148,10 +148,10 @@ Check whether an extension has been registered on the `Span` class.
> assert Span.has_extension("is_city")
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------------ |
| `name` | str | Name of the extension to check. |
| **RETURNS** | bool | Whether the extension has been registered. |
| Name | Description |
| ----------- | --------------------------------------------------- |
| `name` | Name of the extension to check. ~~str~~ |
| **RETURNS** | Whether the extension has been registered. ~~bool~~ |
## Span.remove_extension {#remove_extension tag="classmethod" new="2.0.12"}
@ -166,10 +166,10 @@ Remove a previously registered extension.
> assert not Span.has_extension("is_city")
> ```
| Name | Type | Description |
| ----------- | ----- | --------------------------------------------------------------------- |
| `name` | str | Name of the extension. |
| **RETURNS** | tuple | A `(default, method, getter, setter)` tuple of the removed extension. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the removed extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Span.char_span {#char_span tag="method" new="2.2.4"}
@ -184,14 +184,14 @@ the character indices don't map to a valid span.
> assert span.text == "New York"
> ```
| Name | Type | Description |
| ----------- | ---------------------------------------- | --------------------------------------------------------------------- |
| `start` | int | The index of the first character of the span. |
| `end` | int | The index of the last character after the span. |
| `label` | uint64 / str | A label to attach to the span, e.g. for named entities. |
| `kb_id` | uint64 / str | An ID from a knowledge base to capture the meaning of a named entity. |
| `vector` | `numpy.ndarray[ndim=1, dtype="float32"]` | A meaning representation of the span. |
| **RETURNS** | `Span` | The newly constructed object or `None`. |
| Name | Description |
| ------------------------------------ | ----------------------------------------------------------------------------------------- |
| `start` | The index of the first character of the span. ~~int~~ |
| `end` | The index of the last character after the span. ~int~~ |
| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ |
| `kb_id` <Tag variant="new">2.2</Tag> | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ |
## Span.similarity {#similarity tag="method" model="vectors"}
@ -209,10 +209,10 @@ using an average of word vectors.
> assert apples_oranges == oranges_apples
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------------------------------------------------------- |
| `other` | - | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
| **RETURNS** | float | A scalar similarity score. Higher is more similar. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------- |
| `other` | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. ~~Union[Doc, Span, Token, Lexeme]~~ |
| **RETURNS** | A scalar similarity score. Higher is more similar. ~~float~~ |
## Span.get_lca_matrix {#get_lca_matrix tag="method"}
@ -229,9 +229,9 @@ ancestor is found, e.g. if span excludes a necessary ancestor.
> # array([[0, 0, 0], [0, 1, 2], [0, 2, 2]], dtype=int32)
> ```
| Name | Type | Description |
| ----------- | -------------------------------------- | ------------------------------------------------ |
| **RETURNS** | `numpy.ndarray[ndim=2, dtype="int32"]` | The lowest common ancestor matrix of the `Span`. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------- |
| **RETURNS** | The lowest common ancestor matrix of the `Span`. ~~numpy.ndarray[ndim=2, dtype=int32]~~ |
## Span.to_array {#to_array tag="method" new="2"}
@ -249,10 +249,10 @@ shape `(N, M)`, where `N` is the length of the document. The values will be
> np_array = span.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
> ```
| Name | Type | Description |
| ----------- | ----------------------------- | -------------------------------------------------------------------------------------------------------- |
| `attr_ids` | list | A list of attribute ID ints. |
| **RETURNS** | `numpy.ndarray[long, ndim=2]` | A feature matrix, with one row per word, and one column per attribute indicated in the input `attr_ids`. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| `attr_ids` | A list of attributes (int IDs or string names) or a single attribute (int ID or string name). ~~Union[int, str, List[Union[int, str]]]~~ |
| **RETURNS** | The exported attributes as a numpy array. ~~Union[numpy.ndarray[ndim=2, dtype=uint64], numpy.ndarray[ndim=1, dtype=uint64]]~~ |
## Span.ents {#ents tag="property" new="2.0.13" model="ner"}
@ -270,9 +270,9 @@ if the entity recognizer has been applied.
> assert ents[0].text == "Mr. Best"
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------- |
| **RETURNS** | tuple | Entities in the span, one `Span` per entity. |
| Name | Description |
| ----------- | ----------------------------------------------------------------- |
| **RETURNS** | Entities in the span, one `Span` per entity. ~~Tuple[Span, ...]~~ |
## Span.as_doc {#as_doc tag="method"}
@ -287,10 +287,10 @@ Create a new `Doc` object corresponding to the `Span`, with a copy of the data.
> assert doc2.text == "New York"
> ```
| Name | Type | Description |
| ---------------- | ----- | ---------------------------------------------------- |
| `copy_user_data` | bool | Whether or not to copy the original doc's user data. |
| **RETURNS** | `Doc` | A `Doc` object of the `Span`'s content. |
| Name | Description |
| ---------------- | ------------------------------------------------------------- |
| `copy_user_data` | Whether or not to copy the original doc's user data. ~~bool~~ |
| **RETURNS** | A `Doc` object of the `Span`'s content. ~~Doc~~ |
## Span.root {#root tag="property" model="parser"}
@ -309,9 +309,9 @@ taken.
> assert new_york.root.text == "York"
> ```
| Name | Type | Description |
| ----------- | ------- | --------------- |
| **RETURNS** | `Token` | The root token. |
| Name | Description |
| ----------- | ------------------------- |
| **RETURNS** | The root token. ~~Token~~ |
## Span.conjuncts {#conjuncts tag="property" model="parser"}
@ -325,9 +325,9 @@ A tuple of tokens coordinated to `span.root`.
> assert [t.text for t in apples_conjuncts] == ["oranges"]
> ```
| Name | Type | Description |
| ----------- | ------- | ----------------------- |
| **RETURNS** | `tuple` | The coordinated tokens. |
| Name | Description |
| ----------- | --------------------------------------------- |
| **RETURNS** | The coordinated tokens. ~~Tuple[Token, ...]~~ |
## Span.lefts {#lefts tag="property" model="parser"}
@ -341,9 +341,9 @@ Tokens that are to the left of the span, whose heads are within the span.
> assert lefts == ["New"]
> ```
| Name | Type | Description |
| ---------- | ------- | ------------------------------------ |
| **YIELDS** | `Token` | A left-child of a token of the span. |
| Name | Description |
| ---------- | ---------------------------------------------- |
| **YIELDS** | A left-child of a token of the span. ~~Token~~ |
## Span.rights {#rights tag="property" model="parser"}
@ -357,9 +357,9 @@ Tokens that are to the right of the span, whose heads are within the span.
> assert rights == ["in"]
> ```
| Name | Type | Description |
| ---------- | ------- | ------------------------------------- |
| **YIELDS** | `Token` | A right-child of a token of the span. |
| Name | Description |
| ---------- | ----------------------------------------------- |
| **YIELDS** | A right-child of a token of the span. ~~Token~~ |
## Span.n_lefts {#n_lefts tag="property" model="parser"}
@ -373,9 +373,9 @@ the span.
> assert doc[3:7].n_lefts == 1
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------- |
| **RETURNS** | int | The number of left-child tokens. |
| Name | Description |
| ----------- | ---------------------------------------- |
| **RETURNS** | The number of left-child tokens. ~~int~~ |
## Span.n_rights {#n_rights tag="property" model="parser"}
@ -389,9 +389,9 @@ the span.
> assert doc[2:4].n_rights == 1
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------- |
| **RETURNS** | int | The number of right-child tokens. |
| Name | Description |
| ----------- | ----------------------------------------- |
| **RETURNS** | The number of right-child tokens. ~~int~~ |
## Span.subtree {#subtree tag="property" model="parser"}
@ -405,9 +405,9 @@ Tokens within the span and tokens which descend from them.
> assert subtree == ["Give", "it", "back", "!"]
> ```
| Name | Type | Description |
| ---------- | ------- | ------------------------------------------------- |
| **YIELDS** | `Token` | A token within the span, or a descendant from it. |
| Name | Description |
| ---------- | ----------------------------------------------------------- |
| **YIELDS** | A token within the span, or a descendant from it. ~~Token~~ |
## Span.has_vector {#has_vector tag="property" model="vectors"}
@ -420,9 +420,9 @@ A boolean value indicating whether a word vector is associated with the object.
> assert doc[1:].has_vector
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------------- |
| **RETURNS** | bool | Whether the span has a vector data attached. |
| Name | Description |
| ----------- | ----------------------------------------------------- |
| **RETURNS** | Whether the span has a vector data attached. ~~bool~~ |
## Span.vector {#vector tag="property" model="vectors"}
@ -437,9 +437,9 @@ vectors.
> assert doc[1:].vector.shape == (300,)
> ```
| Name | Type | Description |
| ----------- | ---------------------------------------- | --------------------------------------------------- |
| **RETURNS** | `numpy.ndarray[ndim=1, dtype="float32"]` | A 1D numpy array representing the span's semantics. |
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| **RETURNS** | A 1-dimensional array representing the span's vector. ~~`numpy.ndarray[ndim=1, dtype=float32]~~ |
## Span.vector_norm {#vector_norm tag="property" model="vectors"}
@ -454,31 +454,31 @@ The L2 norm of the span's vector representation.
> assert doc[1:].vector_norm != doc[2:].vector_norm
> ```
| Name | Type | Description |
| ----------- | ----- | ----------------------------------------- |
| **RETURNS** | float | The L2 norm of the vector representation. |
| Name | Description |
| ----------- | --------------------------------------------------- |
| **RETURNS** | The L2 norm of the vector representation. ~~float~~ |
## Attributes {#attributes}
| Name | Type | Description |
| --------------------------------------- | ------------ | -------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `tensor` <Tag variant="new">2.1.7</Tag> | `ndarray` | The span's slice of the parent `Doc`'s tensor. |
| `sent` | `Span` | The sentence span that this span is a part of. |
| `start` | int | The token offset for the start of the span. |
| `end` | int | The token offset for the end of the span. |
| `start_char` | int | The character offset for the start of the span. |
| `end_char` | int | The character offset for the end of the span. |
| `text` | str | A string representation of the span text. |
| `text_with_ws` | str | The text content of the span with a trailing whitespace character if the last token has one. |
| `orth` | int | ID of the verbatim text content. |
| `orth_` | str | Verbatim text content (identical to `Span.text`). Exists mostly for consistency with the other attributes. |
| `label` | int | The hash value of the span's label. |
| `label_` | str | The span's label. |
| `lemma_` | str | The span's lemma. |
| `kb_id` | int | The hash value of the knowledge base ID referred to by the span. |
| `kb_id_` | str | The knowledge base ID referred to by the span. |
| `ent_id` | int | The hash value of the named entity the token is an instance of. |
| `ent_id_` | str | The string ID of the named entity the token is an instance of. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the span. |
| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |
| Name | Description |
| --------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `doc` | The parent document. ~~Doc~~ |
| `tensor` <Tag variant="new">2.1.7</Tag> | The span's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~ |
| `sent` | The sentence span that this span is a part of. ~~Span~~ |
| `start` | The token offset for the start of the span. ~~int~~ |
| `end` | The token offset for the end of the span. ~~int~~ |
| `start_char` | The character offset for the start of the span. ~~int~~ |
| `end_char` | The character offset for the end of the span. ~~int~~ |
| `text` | A string representation of the span text. ~~str~~ |
| `text_with_ws` | The text content of the span with a trailing whitespace character if the last token has one. ~~str~~ |
| `orth` | ID of the verbatim text content. ~~int~~ |
| `orth_` | Verbatim text content (identical to `Span.text`). Exists mostly for consistency with the other attributes. ~~str~~ |
| `label` | The hash value of the span's label. ~~int~~ |
| `label_` | The span's label. ~~str~~ |
| `lemma_` | The span's lemma. Equivalent to `"".join(token.text_with_ws for token in span)`. ~~str~~ |
| `kb_id` | The hash value of the knowledge base ID referred to by the span. ~~int~~ |
| `kb_id_` | The knowledge base ID referred to by the span. ~~str~~ |
| `ent_id` | The hash value of the named entity the token is an instance of. ~~int~~ |
| `ent_id_` | The string ID of the named entity the token is an instance of. ~~str~~ |
| `sentiment` | A scalar value indicating the positivity or negativity of the span. ~~float~~ |
| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ |

80
website/docs/api/stringstore.md
View File

@ -19,9 +19,9 @@ Create the `StringStore`.
> stringstore = StringStore(["apple", "orange"])
> ```
| Name | Type | Description |
| --------- | -------- | ------------------------------------------ |
| `strings` | iterable | A sequence of strings to add to the store. |
| Name | Description |
| --------- | ---------------------------------------------------------------------- |
| `strings` | A sequence of strings to add to the store. ~~Optional[Iterable[str]]~~ |
## StringStore.\_\_len\_\_ {#len tag="method"}
@ -34,9 +34,9 @@ Get the number of strings in the store.
> assert len(stringstore) == 2
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------- |
| **RETURNS** | int | The number of strings in the store. |
| Name | Description |
| ----------- | ------------------------------------------- |
| **RETURNS** | The number of strings in the store. ~~int~~ |
## StringStore.\_\_getitem\_\_ {#getitem tag="method"}
@ -51,10 +51,10 @@ Retrieve a string from a given hash, or vice versa.
> assert stringstore[apple_hash] == "apple"
> ```
| Name | Type | Description |
| -------------- | -------------------- | -------------------------- |
| `string_or_id` | bytes, str or uint64 | The value to encode. |
| **RETURNS** | str or int | The value to be retrieved. |
| Name | Description |
| -------------- | ----------------------------------------------- |
| `string_or_id` | The value to encode. ~~Union[bytes, str, int]~~ |
| **RETURNS** | The value to be retrieved. ~~Union[str, int]~~ |
## StringStore.\_\_contains\_\_ {#contains tag="method"}
@ -68,15 +68,15 @@ Check whether a string is in the store.
> assert not "cherry" in stringstore
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------- |
| `string` | str | The string to check. |
| **RETURNS** | bool | Whether the store contains the string. |
| Name | Description |
| ----------- | ----------------------------------------------- |
| `string` | The string to check. ~~str~~ |
| **RETURNS** | Whether the store contains the string. ~~bool~~ |
## StringStore.\_\_iter\_\_ {#iter tag="method"}
Iterate over the strings in the store, in order. Note that a newly initialized
store will always include an empty string `''` at position `0`.
store will always include an empty string `""` at position `0`.
> #### Example
>
@ -86,9 +86,9 @@ store will always include an empty string `''` at position `0`.
> assert all_strings == ["apple", "orange"]
> ```
| Name | Type | Description |
| ---------- | ---- | ---------------------- |
| **YIELDS** | str | A string in the store. |
| Name | Description |
| ---------- | ------------------------------ |
| **YIELDS** | A string in the store. ~~str~~ |
## StringStore.add {#add tag="method" new="2"}
@ -105,10 +105,10 @@ Add a string to the `StringStore`.
> assert stringstore["banana"] == banana_hash
> ```
| Name | Type | Description |
| ----------- | ------ | ------------------------ |
| `string` | str | The string to add. |
| **RETURNS** | uint64 | The string's hash value. |
| Name | Description |
| ----------- | -------------------------------- |
| `string` | The string to add. ~~str~~ |
| **RETURNS** | The string's hash value. ~~int~~ |
## StringStore.to_disk {#to_disk tag="method" new="2"}
@ -120,9 +120,9 @@ Save the current state to a directory.
> stringstore.to_disk("/path/to/strings")
> ```
| Name | Type | Description |
| ------ | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| Name | Description |
| ------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
## StringStore.from_disk {#from_disk tag="method" new="2"}
@ -135,10 +135,10 @@ Loads state from a directory. Modifies the object in place and returns it.
> stringstore = StringStore().from_disk("/path/to/strings")
> ```
| Name | Type | Description |
| ----------- | ------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| **RETURNS** | `StringStore` | The modified `StringStore` object. |
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| **RETURNS** | The modified `StringStore` object. ~~StringStore~~ |
## StringStore.to_bytes {#to_bytes tag="method"}
@ -150,9 +150,9 @@ Serialize the current state to a binary string.
> store_bytes = stringstore.to_bytes()
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------ |
| **RETURNS** | bytes | The serialized form of the `StringStore` object. |
| Name | Description |
| ----------- | ---------------------------------------------------------- |
| **RETURNS** | The serialized form of the `StringStore` object. ~~bytes~~ |
## StringStore.from_bytes {#from_bytes tag="method"}
@ -166,10 +166,10 @@ Load state from a binary string.
> new_store = StringStore().from_bytes(store_bytes)
> ```
| Name | Type | Description |
| ------------ | ------------- | ------------------------- |
| `bytes_data` | bytes | The data to load from. |
| **RETURNS** | `StringStore` | The `StringStore` object. |
| Name | Description |
| ------------ | ----------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| **RETURNS** | The `StringStore` object. ~~StringStore~~ |
## Utilities {#util}
@ -184,7 +184,7 @@ Get a 64-bit hash for a given string.
> assert hash_string("apple") == 8566208034543834098
> ```
| Name | Type | Description |
| ----------- | ------ | ------------------- |
| `string` | str | The string to hash. |
| **RETURNS** | uint64 | The hash. |
| Name | Description |
| ----------- | --------------------------- |
| `string` | The string to hash. ~~str~~ |
| **RETURNS** | The hash. ~~int~~ |

194
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@ -28,10 +28,10 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("tagger", config=config)
> ```
| Setting | Type | Description | Default |
| ---------------- | ------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------- |
| `set_morphology` | bool | Whether to set morphological features. | `False` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). | [Tagger](/api/architectures#Tagger) |
| Setting | Description |
| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `set_morphology` | Whether to set morphological features. Defaults to `False`. ~~bool~~ |
| `model` | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/tagger.pyx
@ -58,13 +58,13 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| ---------------- | ------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
| `set_morphology` | bool | Whether to set morphological features. |
| Name | Description |
| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). ~~Model[List[Doc], List[Floats2d]]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | |
| `set_morphology` | Whether to set morphological features. ~~bool~~ |
## Tagger.\_\_call\_\_ {#call tag="method"}
@ -84,10 +84,10 @@ and all pipeline components are applied to the `Doc` in order. Both
> processed = tagger(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## Tagger.pipe {#pipe tag="method"}
@ -105,12 +105,12 @@ applied to the `Doc` in order. Both [`__call__`](/api/tagger#call) and
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## Tagger.begin_training {#begin_training tag="method"}
@ -130,13 +130,13 @@ setting up the label scheme based on the data.
> optimizer = tagger.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/tagger#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Tagger.predict {#predict tag="method"}
@ -150,10 +150,10 @@ modifying them.
> scores = tagger.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ----------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | - | The model's prediction for each document. |
| Name | Description |
| ----------- | ------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The model's prediction for each document. |
## Tagger.set_annotations {#set_annotations tag="method"}
@ -167,10 +167,10 @@ Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
> tagger.set_annotations([doc1, doc2], scores)
> ```
| Name | Type | Description |
| -------- | --------------- | ------------------------------------------------ |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `Tagger.predict`. |
| Name | Description |
| -------- | ------------------------------------------------ |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `Tagger.predict`. |
## Tagger.update {#update tag="method"}
@ -187,15 +187,15 @@ Delegates to [`predict`](/api/tagger#predict) and
> losses = tagger.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/tagger#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Tagger.rehearse {#rehearse tag="method,experimental" new="3"}
@ -211,14 +211,14 @@ the "catastrophic forgetting" problem. This feature is experimental.
> losses = tagger.rehearse(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Tagger.get_loss {#get_loss tag="method"}
@ -233,11 +233,11 @@ predicted scores.
> loss, d_loss = tagger.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | - | Scores representing the model's predictions. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------- |
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
| `scores` | Scores representing the model's predictions. |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
## Tagger.score {#score tag="method" new="3"}
@ -249,10 +249,10 @@ Score a batch of examples.
> scores = tagger.score(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"`, `"tag"` and `"lemma"`. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | The examples to score. ~~Iterable[Example]~~ |
| **RETURNS** | The scores, produced by [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"`, `"tag"` and `"lemma"`. ~~Dict[str, float]~~ |
## Tagger.create_optimizer {#create_optimizer tag="method"}
@ -265,9 +265,9 @@ Create an optimizer for the pipeline component.
> optimizer = tagger.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Tagger.use_params {#use_params tag="method, contextmanager"}
@ -282,9 +282,9 @@ context, the original parameters are restored.
> tagger.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## Tagger.add_label {#add_label tag="method"}
@ -297,10 +297,10 @@ Add a new label to the pipe.
> tagger.add_label("MY_LABEL")
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------------------------- |
| `label` | str | The label to add. |
| **RETURNS** | int | `0` if the label is already present, otherwise `1`. |
| Name | Description |
| ----------- | ----------------------------------------------------------- |
| `label` | The label to add. ~~str~~ |
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
## Tagger.to_disk {#to_disk tag="method"}
@ -313,11 +313,11 @@ Serialize the pipe to disk.
> tagger.to_disk("/path/to/tagger")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Tagger.from_disk {#from_disk tag="method"}
@ -330,12 +330,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> tagger.from_disk("/path/to/tagger")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tagger` | The modified `Tagger` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Tagger` object. ~~Tagger~~ |
## Tagger.to_bytes {#to_bytes tag="method"}
@ -348,11 +348,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Tagger` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `Tagger` object. ~~bytes~~ |
## Tagger.from_bytes {#from_bytes tag="method"}
@ -366,12 +366,12 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> tagger.from_bytes(tagger_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tagger` | The `Tagger` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Tagger` object. ~~Tagger~~ |
## Tagger.labels {#labels tag="property"}
@ -384,9 +384,9 @@ The labels currently added to the component.
> assert "MY_LABEL" in tagger.labels
> ```
| Name | Type | Description |
| ----------- | ------------ | ---------------------------------- |
| **RETURNS** | `Tuple[str]` | The labels added to the component. |
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
## Serialization fields {#serialization-fields}

222
website/docs/api/textcategorizer.md
View File

@ -35,10 +35,10 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("textcat", config=config)
> ```
| Setting | Type | Description | Default |
| -------- | ------------------------------------------ | --------------------------------------------------------------------------------------- | ----------------------------------------------------- |
| `labels` | `List[str]` | A list of categories to learn. If empty, the model infers the categories from the data. | `[]` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | A model instance that predicts scores for each category. | [TextCatEnsemble](/api/architectures#TextCatEnsemble) |
| Setting | Description |
| -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `labels` | A list of categories to learn. If empty, the model infers the categories from the data. Defaults to `[]`. ~~Iterable[str]~~ |
| `model` | A model instance that predicts scores for each category. Defaults to [TextCatEnsemble](/api/architectures#TextCatEnsemble). ~~Model[List[Doc], List[Floats2d]]~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/textcat.py
@ -65,13 +65,13 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#create_pipe).
| Name | Type | Description |
| -------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
| `labels` | `Iterable[str]` | The labels to use. |
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | |
| `labels` | The labels to use. ~~Iterable[str]~~ |
## TextCategorizer.\_\_call\_\_ {#call tag="method"}
@ -91,10 +91,10 @@ delegate to the [`predict`](/api/textcategorizer#predict) and
> processed = textcat(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## TextCategorizer.pipe {#pipe tag="method"}
@ -113,12 +113,12 @@ applied to the `Doc` in order. Both [`__call__`](/api/textcategorizer#call) and
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------- |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | The processed documents in order. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## TextCategorizer.begin_training {#begin_training tag="method"}
@ -138,13 +138,13 @@ setting up the label scheme based on the data.
> optimizer = textcat.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/textcategorizer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## TextCategorizer.predict {#predict tag="method"}
@ -158,10 +158,10 @@ modifying them.
> scores = textcat.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ----------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | - | The model's prediction for each document. |
| Name | Description |
| ----------- | ------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The model's prediction for each document. |
## TextCategorizer.set_annotations {#set_annotations tag="method"}
@ -175,10 +175,10 @@ Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
> textcat.set_annotations(docs, scores)
> ```
| Name | Type | Description |
| -------- | --------------- | --------------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `TextCategorizer.predict`. |
| Name | Description |
| -------- | --------------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `TextCategorizer.predict`. |
## TextCategorizer.update {#update tag="method"}
@ -195,15 +195,15 @@ Delegates to [`predict`](/api/textcategorizer#predict) and
> losses = textcat.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/textcategorizer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## TextCategorizer.rehearse {#rehearse tag="method,experimental" new="3"}
@ -219,14 +219,14 @@ the "catastrophic forgetting" problem. This feature is experimental.
> losses = textcat.rehearse(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## TextCategorizer.get_loss {#get_loss tag="method"}
@ -241,11 +241,11 @@ predicted scores.
> loss, d_loss = textcat.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | - | Scores representing the model's predictions. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
| Name | Description |
| ----------- | --------------------------------------------------------------------------- |
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
| `scores` | Scores representing the model's predictions. |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
## TextCategorizer.score {#score tag="method" new="3"}
@ -257,12 +257,12 @@ Score a batch of examples.
> scores = textcat.score(examples)
> ```
| Name | Type | Description |
| ---------------- | ------------------- | ---------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The examples to score. |
| _keyword-only_ | | |
| `positive_label` | str | Optional positive label. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_cats`](/api/scorer#score_cats). |
| Name | Description |
| ---------------- | -------------------------------------------------------------------------------------------------------------------- |
| `examples` | The examples to score. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `positive_label` | Optional positive label. ~~Optional[str]~~ |
| **RETURNS** | The scores, produced by [`Scorer.score_cats`](/api/scorer#score_cats). ~~Dict[str, Union[float, Dict[str, float]]]~~ |
## TextCategorizer.create_optimizer {#create_optimizer tag="method"}
@ -275,25 +275,9 @@ Create an optimizer for the pipeline component.
> optimizer = textcat.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## TextCategorizer.add_label {#add_label tag="method"}
Add a new label to the pipe.
> #### Example
>
> ```python
> textcat = nlp.add_pipe("textcat")
> textcat.add_label("MY_LABEL")
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------------------------- |
| `label` | str | The label to add. |
| **RETURNS** | int | `0` if the label is already present, otherwise `1`. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## TextCategorizer.use_params {#use_params tag="method, contextmanager"}
@ -307,9 +291,25 @@ Modify the pipe's model, to use the given parameter values.
> textcat.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## TextCategorizer.add_label {#add_label tag="method"}
Add a new label to the pipe.
> #### Example
>
> ```python
> textcat = nlp.add_pipe("textcat")
> textcat.add_label("MY_LABEL")
> ```
| Name | Description |
| ----------- | ----------------------------------------------------------- |
| `label` | The label to add. ~~str~~ |
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
## TextCategorizer.to_disk {#to_disk tag="method"}
@ -322,11 +322,11 @@ Serialize the pipe to disk.
> textcat.to_disk("/path/to/textcat")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## TextCategorizer.from_disk {#from_disk tag="method"}
@ -339,12 +339,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> textcat.from_disk("/path/to/textcat")
> ```
| Name | Type | Description |
| -------------- | ----------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `TextCategorizer` | The modified `TextCategorizer` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `TextCategorizer` object. ~~TextCategorizer~~ |
## TextCategorizer.to_bytes {#to_bytes tag="method"}
@ -357,11 +357,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `TextCategorizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `TextCategorizer` object. ~~bytes~~ |
## TextCategorizer.from_bytes {#from_bytes tag="method"}
@ -375,12 +375,12 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> textcat.from_bytes(textcat_bytes)
> ```
| Name | Type | Description |
| -------------- | ----------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `TextCategorizer` | The `TextCategorizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `TextCategorizer` object. ~~TextCategorizer~~ |
## TextCategorizer.labels {#labels tag="property"}
@ -393,9 +393,9 @@ The labels currently added to the component.
> assert "MY_LABEL" in textcat.labels
> ```
| Name | Type | Description |
| ----------- | ----- | ---------------------------------- |
| **RETURNS** | tuple | The labels added to the component. |
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
## Serialization fields {#serialization-fields}

142
website/docs/api/tok2vec.md
View File

@ -15,7 +15,7 @@ multiple components, e.g. to have one embedding and CNN network shared between a
[`EntityRecognizer`](/api/entityrecognizer).
In order to use the `Tok2Vec` predictions, subsequent components should use the
[Tok2VecListener](/api/architectures#Tok2VecListener) layer as the tok2vec
[Tok2VecListener](/api/architectures#Tok2VecListener) layer as the `tok2vec`
subnetwork of their model. This layer will read data from the `doc.tensor`
attribute during prediction. During training, the `Tok2Vec` component will save
its prediction and backprop callback for each batch, so that the subsequent
@ -40,9 +40,9 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("tok2vec", config=config)
> ```
| Setting | Type | Description | Default |
| ------- | ------------------------------------------ | ----------------------------------------------------------------------- | ----------------------------------------------- |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** `List[Doc]`. **Output:** `List[Floats2d]`. The model to use. | [HashEmbedCNN](/api/architectures#HashEmbedCNN) |
| Setting | Description |
| ------- | ------------------------------------------------------------------------------------------------------------------ |
| `model` | The model to use. Defaults to [HashEmbedCNN](/api/architectures#HashEmbedCNN). ~~Model[List[Doc], List[Floats2d]~~ |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/tok2vec.py
@ -69,11 +69,11 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#create_pipe).
| Name | Type | Description |
| ------- | ------------------------------------------ | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| Name | Description |
| ------- | ------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
## Tok2Vec.\_\_call\_\_ {#call tag="method"}
@ -95,10 +95,10 @@ pipeline components are applied to the `Doc` in order. Both
> processed = tok2vec(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## Tok2Vec.pipe {#pipe tag="method"}
@ -116,12 +116,12 @@ and [`set_annotations`](/api/tok2vec#set_annotations) methods.
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------- |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | The processed documents in order. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## Tok2Vec.begin_training {#begin_training tag="method"}
@ -141,13 +141,13 @@ setting up the label scheme based on the data.
> optimizer = tok2vec.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/tok2vec#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Tok2Vec.predict {#predict tag="method"}
@ -161,10 +161,10 @@ modifying them.
> scores = tok2vec.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ----------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | - | The model's prediction for each document. |
| Name | Description |
| ----------- | ------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The model's prediction for each document. |
## Tok2Vec.set_annotations {#set_annotations tag="method"}
@ -178,10 +178,10 @@ Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
> tok2vec.set_annotations(docs, scores)
> ```
| Name | Type | Description |
| -------- | --------------- | ------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `Tok2Vec.predict`. |
| Name | Description |
| -------- | ------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `Tok2Vec.predict`. |
## Tok2Vec.update {#update tag="method"}
@ -197,15 +197,15 @@ Delegates to [`predict`](/api/tok2vec#predict).
> losses = tok2vec.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/tok2vec#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Tok2Vec.create_optimizer {#create_optimizer tag="method"}
@ -218,9 +218,9 @@ Create an optimizer for the pipeline component.
> optimizer = tok2vec.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Tok2Vec.use_params {#use_params tag="method, contextmanager"}
@ -235,9 +235,9 @@ context, the original parameters are restored.
> tok2vec.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## Tok2Vec.to_disk {#to_disk tag="method"}
@ -250,11 +250,11 @@ Serialize the pipe to disk.
> tok2vec.to_disk("/path/to/tok2vec")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Tok2Vec.from_disk {#from_disk tag="method"}
@ -267,12 +267,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> tok2vec.from_disk("/path/to/tok2vec")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tok2Vec` | The modified `Tok2Vec` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Tok2Vec` object. ~~Tok2Vec~~ |
## Tok2Vec.to_bytes {#to_bytes tag="method"}
@ -285,11 +285,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Tok2Vec` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `Tok2Vec` object. ~~bytes~~ |
## Tok2Vec.from_bytes {#from_bytes tag="method"}
@ -303,12 +303,12 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> tok2vec.from_bytes(tok2vec_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tok2Vec` | The `Tok2Vec` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Tok2Vec` object. ~~Tok2Vec~~ |
## Serialization fields {#serialization-fields}

302
website/docs/api/token.md
View File

@ -17,11 +17,11 @@ Construct a `Token` object.
> assert token.text == "Give"
> ```
| Name | Type | Description |
| -------- | ------- | ------------------------------------------- |
| `vocab` | `Vocab` | A storage container for lexical types. |
| `doc` | `Doc` | The parent document. |
| `offset` | int | The index of the token within the document. |
| Name | Description |
| -------- | --------------------------------------------------- |
| `vocab` | A storage container for lexical types. ~~Vocab~~ |
| `doc` | The parent document. ~~Doc~~ |
| `offset` | The index of the token within the document. ~~int~~ |
## Token.\_\_len\_\_ {#len tag="method"}
@ -35,9 +35,9 @@ The number of unicode characters in the token, i.e. `token.text`.
> assert len(token) == 4
> ```
| Name | Type | Description |
| ----------- | ---- | ---------------------------------------------- |
| **RETURNS** | int | The number of unicode characters in the token. |
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | The number of unicode characters in the token. ~~int~~ |
## Token.set_extension {#set_extension tag="classmethod" new="2"}
@ -55,14 +55,14 @@ For details, see the documentation on
> assert doc[3]._.is_fruit
> ```
| Name | Type | Description |
| --------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | Name of the attribute to set by the extension. For example, `"my_attr"` will be available as `token._.my_attr`. |
| `default` | - | Optional default value of the attribute if no getter or method is defined. |
| `method` | callable | Set a custom method on the object, for example `token._.compare(other_token)`. |
| `getter` | callable | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute. |
| `setter` | callable | Setter function that takes the `Token` and a value, and modifies the object. Is called when the user writes to the `Token._` attribute. |
| `force` | bool | Force overwriting existing attribute. |
| Name | Description |
| --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the attribute to set by the extension. For example, `"my_attr"` will be available as `token._.my_attr`. ~~str~~ |
| `default` | Optional default value of the attribute if no getter or method is defined. ~~Optional[Any]~~ |
| `method` | Set a custom method on the object, for example `token._.compare(other_token)`. ~~Optional[Callable[[Token, ...], Any]]~~ |
| `getter` | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute. ~~Optional[Callable[[Token], Any]]~~ |
| `setter` | Setter function that takes the `Token` and a value, and modifies the object. Is called when the user writes to the `Token._` attribute. ~~Optional[Callable[[Token, Any], None]]~~ |
| `force` | Force overwriting existing attribute. ~~bool~~ |
## Token.get_extension {#get_extension tag="classmethod" new="2"}
@ -79,10 +79,10 @@ Look up a previously registered extension by name. Returns a 4-tuple
> assert extension == (False, None, None, None)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------- |
| `name` | str | Name of the extension. |
| **RETURNS** | tuple | A `(default, method, getter, setter)` tuple of the extension. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Token.has_extension {#has_extension tag="classmethod" new="2"}
@ -96,10 +96,10 @@ Check whether an extension has been registered on the `Token` class.
> assert Token.has_extension("is_fruit")
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------------ |
| `name` | str | Name of the extension to check. |
| **RETURNS** | bool | Whether the extension has been registered. |
| Name | Description |
| ----------- | --------------------------------------------------- |
| `name` | Name of the extension to check. ~~str~~ |
| **RETURNS** | Whether the extension has been registered. ~~bool~~ |
## Token.remove_extension {#remove_extension tag="classmethod" new=""2.0.11""}
@ -114,10 +114,10 @@ Remove a previously registered extension.
> assert not Token.has_extension("is_fruit")
> ```
| Name | Type | Description |
| ----------- | ----- | --------------------------------------------------------------------- |
| `name` | str | Name of the extension. |
| **RETURNS** | tuple | A `(default, method, getter, setter)` tuple of the removed extension. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the removed extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Token.check_flag {#check_flag tag="method"}
@ -132,10 +132,10 @@ Check the value of a boolean flag.
> assert token.check_flag(IS_TITLE) == True
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------- |
| `flag_id` | int | The attribute ID of the flag to check. |
| **RETURNS** | bool | Whether the flag is set. |
| Name | Description |
| ----------- | ---------------------------------------------- |
| `flag_id` | The attribute ID of the flag to check. ~~int~~ |
| **RETURNS** | Whether the flag is set. ~~bool~~ |
## Token.similarity {#similarity tag="method" model="vectors"}
@ -150,10 +150,10 @@ Compute a semantic similarity estimate. Defaults to cosine over vectors.
> assert apples_oranges == oranges_apples
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------------------------------------------------------- |
| other | - | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
| **RETURNS** | float | A scalar similarity score. Higher is more similar. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------- |
| other | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. ~~Union[Doc, Span, Token, Lexeme]~~ |
| **RETURNS** | A scalar similarity score. Higher is more similar. ~~float~~ |
## Token.nbor {#nbor tag="method"}
@ -167,10 +167,10 @@ Get a neighboring token.
> assert give_nbor.text == "it"
> ```
| Name | Type | Description |
| ----------- | ------- | ----------------------------------------------------------- |
| `i` | int | The relative position of the token to get. Defaults to `1`. |
| **RETURNS** | `Token` | The token at position `self.doc[self.i+i]`. |
| Name | Description |
| ----------- | ------------------------------------------------------------------- |
| `i` | The relative position of the token to get. Defaults to `1`. ~~int~~ |
| **RETURNS** | The token at position `self.doc[self.i+i]`. ~~Token~~ |
## Token.is_ancestor {#is_ancestor tag="method" model="parser"}
@ -186,10 +186,10 @@ dependency tree.
> assert give.is_ancestor(it)
> ```
| Name | Type | Description |
| ----------- | ------- | ----------------------------------------------------- |
| descendant | `Token` | Another token. |
| **RETURNS** | bool | Whether this token is the ancestor of the descendant. |
| Name | Description |
| ----------- | -------------------------------------------------------------- |
| descendant | Another token. ~~Token~~ |
| **RETURNS** | Whether this token is the ancestor of the descendant. ~~bool~~ |
## Token.ancestors {#ancestors tag="property" model="parser"}
@ -205,9 +205,9 @@ The rightmost token of this token's syntactic descendants.
> assert [t.text for t in he_ancestors] == ["pleaded"]
> ```
| Name | Type | Description |
| ---------- | ------- | --------------------------------------------------------------------- |
| **YIELDS** | `Token` | A sequence of ancestor tokens such that `ancestor.is_ancestor(self)`. |
| Name | Description |
| ---------- | ------------------------------------------------------------------------------- |
| **YIELDS** | A sequence of ancestor tokens such that `ancestor.is_ancestor(self)`. ~~Token~~ |
## Token.conjuncts {#conjuncts tag="property" model="parser"}
@ -221,9 +221,9 @@ A tuple of coordinated tokens, not including the token itself.
> assert [t.text for t in apples_conjuncts] == ["oranges"]
> ```
| Name | Type | Description |
| ----------- | ------- | ----------------------- |
| **RETURNS** | `tuple` | The coordinated tokens. |
| Name | Description |
| ----------- | --------------------------------------------- |
| **RETURNS** | The coordinated tokens. ~~Tuple[Token, ...]~~ |
## Token.children {#children tag="property" model="parser"}
@ -237,9 +237,9 @@ A sequence of the token's immediate syntactic children.
> assert [t.text for t in give_children] == ["it", "back", "!"]
> ```
| Name | Type | Description |
| ---------- | ------- | ------------------------------------------- |
| **YIELDS** | `Token` | A child token such that `child.head==self`. |
| Name | Description |
| ---------- | ------------------------------------------------------- |
| **YIELDS** | A child token such that `child.head == self`. ~~Token~~ |
## Token.lefts {#lefts tag="property" model="parser"}
@ -253,9 +253,9 @@ The leftward immediate children of the word, in the syntactic dependency parse.
> assert lefts == ["New"]
> ```
| Name | Type | Description |
| ---------- | ------- | -------------------------- |
| **YIELDS** | `Token` | A left-child of the token. |
| Name | Description |
| ---------- | ------------------------------------ |
| **YIELDS** | A left-child of the token. ~~Token~~ |
## Token.rights {#rights tag="property" model="parser"}
@ -269,9 +269,9 @@ The rightward immediate children of the word, in the syntactic dependency parse.
> assert rights == ["in"]
> ```
| Name | Type | Description |
| ---------- | ------- | --------------------------- |
| **YIELDS** | `Token` | A right-child of the token. |
| Name | Description |
| ---------- | ------------------------------------- |
| **YIELDS** | A right-child of the token. ~~Token~~ |
## Token.n_lefts {#n_lefts tag="property" model="parser"}
@ -285,9 +285,9 @@ dependency parse.
> assert doc[3].n_lefts == 1
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------- |
| **RETURNS** | int | The number of left-child tokens. |
| Name | Description |
| ----------- | ---------------------------------------- |
| **RETURNS** | The number of left-child tokens. ~~int~~ |
## Token.n_rights {#n_rights tag="property" model="parser"}
@ -301,9 +301,9 @@ dependency parse.
> assert doc[3].n_rights == 1
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------- |
| **RETURNS** | int | The number of right-child tokens. |
| Name | Description |
| ----------- | ----------------------------------------- |
| **RETURNS** | The number of right-child tokens. ~~int~~ |
## Token.subtree {#subtree tag="property" model="parser"}
@ -317,9 +317,9 @@ A sequence containing the token and all the token's syntactic descendants.
> assert [t.text for t in give_subtree] == ["Give", "it", "back", "!"]
> ```
| Name | Type | Description |
| ---------- | ------- | -------------------------------------------------------------------------- |
| **YIELDS** | `Token` | A descendant token such that `self.is_ancestor(token)` or `token == self`. |
| Name | Description |
| ---------- | ------------------------------------------------------------------------------------ |
| **YIELDS** | A descendant token such that `self.is_ancestor(token)` or `token == self`. ~~Token~~ |
## Token.is_sent_start {#is_sent_start tag="property" new="2"}
@ -334,9 +334,9 @@ unknown. Defaults to `True` for the first token in the `Doc`.
> assert not doc[5].is_sent_start
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------ |
| **RETURNS** | bool | Whether the token starts a sentence. |
| Name | Description |
| ----------- | --------------------------------------------- |
| **RETURNS** | Whether the token starts a sentence. ~~bool~~ |
## Token.has_vector {#has_vector tag="property" model="vectors"}
@ -350,9 +350,9 @@ A boolean value indicating whether a word vector is associated with the token.
> assert apples.has_vector
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------------------- |
| **RETURNS** | bool | Whether the token has a vector data attached. |
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | Whether the token has a vector data attached. ~~bool~~ |
## Token.vector {#vector tag="property" model="vectors"}
@ -367,9 +367,9 @@ A real-valued meaning representation.
> assert apples.vector.shape == (300,)
> ```
| Name | Type | Description |
| ----------- | ---------------------------------------- | ---------------------------------------------------- |
| **RETURNS** | `numpy.ndarray[ndim=1, dtype="float32"]` | A 1D numpy array representing the token's semantics. |
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| **RETURNS** | A 1-dimensional array representing the token's vector. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Token.vector_norm {#vector_norm tag="property" model="vectors"}
@ -386,80 +386,80 @@ The L2 norm of the token's vector representation.
> assert apples.vector_norm != pasta.vector_norm
> ```
| Name | Type | Description |
| ----------- | ----- | ----------------------------------------- |
| **RETURNS** | float | The L2 norm of the vector representation. |
| Name | Description |
| ----------- | --------------------------------------------------- |
| **RETURNS** | The L2 norm of the vector representation. ~~float~~ |
## Attributes {#attributes}
| Name | Type | Description |
| -------------------------------------------- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `lex` <Tag variant="new">3</Tag> | [`Lexeme`](/api/lexeme) | The underlying lexeme. |
| `sent` <Tag variant="new">2.0.12</Tag> | [`Span`](/api/span) | The sentence span that this token is a part of. |
| `text` | str | Verbatim text content. |
| `text_with_ws` | str | Text content, with trailing space character if present. |
| `whitespace_` | str | Trailing space character if present. |
| `orth` | int | ID of the verbatim text content. |
| `orth_` | str | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. |
| `vocab` | `Vocab` | The vocab object of the parent `Doc`. |
| `tensor` <Tag variant="new">2.1.7</Tag> | `ndarray` | The tokens's slice of the parent `Doc`'s tensor. |
| `head` | `Token` | The syntactic parent, or "governor", of this token. |
| `left_edge` | `Token` | The leftmost token of this token's syntactic descendants. |
| `right_edge` | `Token` | The rightmost token of this token's syntactic descendants. |
| `i` | int | The index of the token within the parent document. |
| `ent_type` | int | Named entity type. |
| `ent_type_` | str | Named entity type. |
| `ent_iob` | int | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. |
| `ent_iob_` | str | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. |
| `ent_kb_id` <Tag variant="new">2.2</Tag> | int | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_kb_id_` <Tag variant="new">2.2</Tag> | str | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_id` | int | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `ent_id_` | str | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `lemma` | int | Base form of the token, with no inflectional suffixes. |
| `lemma_` | str | Base form of the token, with no inflectional suffixes. |
| `norm` | int | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `norm_` | str | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `lower` | int | Lowercase form of the token. |
| `lower_` | str | Lowercase form of the token text. Equivalent to `Token.text.lower()`. |
| `shape` | int | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `shape_` | str | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `prefix` | int | Hash value of a length-N substring from the start of the token. Defaults to `N=1`. |
| `prefix_` | str | A length-N substring from the start of the token. Defaults to `N=1`. |
| `suffix` | int | Hash value of a length-N substring from the end of the token. Defaults to `N=3`. |
| `suffix_` | str | Length-N substring from the end of the token. Defaults to `N=3`. |
| `is_alpha` | bool | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. |
| `is_ascii` | bool | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. |
| `is_digit` | bool | Does the token consist of digits? Equivalent to `token.text.isdigit()`. |
| `is_lower` | bool | Is the token in lowercase? Equivalent to `token.text.islower()`. |
| `is_upper` | bool | Is the token in uppercase? Equivalent to `token.text.isupper()`. |
| `is_title` | bool | Is the token in titlecase? Equivalent to `token.text.istitle()`. |
| `is_punct` | bool | Is the token punctuation? |
| `is_left_punct` | bool | Is the token a left punctuation mark, e.g. `"("` ? |
| `is_right_punct` | bool | Is the token a right punctuation mark, e.g. `")"` ? |
| `is_space` | bool | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. |
| `is_bracket` | bool | Is the token a bracket? |
| `is_quote` | bool | Is the token a quotation mark? |
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the token a currency symbol? |
| `like_url` | bool | Does the token resemble a URL? |
| `like_num` | bool | Does the token represent a number? e.g. "10.9", "10", "ten", etc. |
| `like_email` | bool | Does the token resemble an email address? |
| `is_oov` | bool | Does the token have a word vector? |
| `is_stop` | bool | Is the token part of a "stop list"? |
| `pos` | int | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/docs/u/pos/). |
| `pos_` | str | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/docs/u/pos/). |
| `tag` | int | Fine-grained part-of-speech. |
| `tag_` | str | Fine-grained part-of-speech. |
| `morph` | `MorphAnalysis` | Morphological analysis. |
| `morph_` | str | Morphological analysis in UD FEATS format. |
| `dep` | int | Syntactic dependency relation. |
| `dep_` | str | Syntactic dependency relation. |
| `lang` | int | Language of the parent document's vocabulary. |
| `lang_` | str | Language of the parent document's vocabulary. |
| `prob` | float | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). |
| `idx` | int | The character offset of the token within the parent document. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the token. |
| `lex_id` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `rank` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `cluster` | int | Brown cluster ID. |
| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |
| Name | Description |
| -------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | The parent document. ~~Doc~~ |
| `lex` <Tag variant="new">3</Tag> | The underlying lexeme. ~~Lexeme~~ |
| `sent` <Tag variant="new">2.0.12</Tag> | The sentence span that this token is a part of. ~~Span~~ |
| `text` | Verbatim text content. ~~str~~ |
| `text_with_ws` | Text content, with trailing space character if present. ~~str~~ |
| `whitespace_` | Trailing space character if present. ~~str~~ |
| `orth` | ID of the verbatim text content. ~~int~~ |
| `orth_` | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. ~~str~~ |
| `vocab` | The vocab object of the parent `Doc`. ~~vocab~~ |
| `tensor` <Tag variant="new">2.1.7</Tag> | The tokens's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~ |
| `head` | The syntactic parent, or "governor", of this token. ~~Token~~ |
| `left_edge` | The leftmost token of this token's syntactic descendants. ~~Token~~ |
| `right_edge` | The rightmost token of this token's syntactic descendants. ~~Token~~ |
| `i` | The index of the token within the parent document. ~~int~~ |
| `ent_type` | Named entity type. ~~int~~ |
| `ent_type_` | Named entity type. ~~str~~ |
| `ent_iob` | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. ~~int~~ |
| `ent_iob_` | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. ~~str~~ |
| `ent_kb_id` <Tag variant="new">2.2</Tag> | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~int~~ |
| `ent_kb_id_` <Tag variant="new">2.2</Tag> | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~str~~ |
| `ent_id` | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. ~~int~~ |
| `ent_id_` | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. ~~str~~ |
| `lemma` | Base form of the token, with no inflectional suffixes. ~~int~~ |
| `lemma_` | Base form of the token, with no inflectional suffixes. ~~str~~ |
| `norm` | The token's norm, i.e. a normalized form of the token text. Can be set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions). ~~int~~ |
| `norm_` | The token's norm, i.e. a normalized form of the token text. Can be set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions). ~~str~~ |
| `lower` | Lowercase form of the token. ~~int~~ |
| `lower_` | Lowercase form of the token text. Equivalent to `Token.text.lower()`. ~~str~~ |
| `shape` | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~int~~ |
| `shape_` | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. ~~str~~ |
| `prefix` | Hash value of a length-N substring from the start of the token. Defaults to `N=1`. ~~int~~ |
| `prefix_` | A length-N substring from the start of the token. Defaults to `N=1`. ~~str~~ |
| `suffix` | Hash value of a length-N substring from the end of the token. Defaults to `N=3`. ~~int~~ |
| `suffix_` | Length-N substring from the end of the token. Defaults to `N=3`. ~~str~~ |
| `is_alpha` | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. ~~bool~~ |
| `is_ascii` | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. ~~bool~~ |
| `is_digit` | Does the token consist of digits? Equivalent to `token.text.isdigit()`. ~~bool~~ |
| `is_lower` | Is the token in lowercase? Equivalent to `token.text.islower()`. ~~bool~~ |
| `is_upper` | Is the token in uppercase? Equivalent to `token.text.isupper()`. ~~bool~~ |
| `is_title` | Is the token in titlecase? Equivalent to `token.text.istitle()`. ~~bool~~ |
| `is_punct` | Is the token punctuation? ~~bool~~ |
| `is_left_punct` | Is the token a left punctuation mark, e.g. `"("` ? ~~bool~~ |
| `is_right_punct` | Is the token a right punctuation mark, e.g. `")"` ? ~~bool~~ |
| `is_space` | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. ~~bool~~ |
| `is_bracket` | Is the token a bracket? ~~bool~~ |
| `is_quote` | Is the token a quotation mark? ~~bool~~ |
| `is_currency` <Tag variant="new">2.0.8</Tag> | Is the token a currency symbol? ~~bool~~ |
| `like_url` | Does the token resemble a URL? ~~bool~~ |
| `like_num` | Does the token represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~ |
| `like_email` | Does the token resemble an email address? ~~bool~~ |
| `is_oov` | Does the token have a word vector? ~~bool~~ |
| `is_stop` | Is the token part of a "stop list"? ~~bool~~ |
| `pos` | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/docs/u/pos/). ~~int~~ |
| `pos_` | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/docs/u/pos/). ~~str~~ |
| `tag` | Fine-grained part-of-speech. ~~int~~ |
| `tag_` | Fine-grained part-of-speech. ~~str~~ |
| `morph` <Tag variant="new">3</Tag> | Morphological analysis. ~~MorphAnalysis~~ |
| `morph_` <Tag variant="new">3</Tag> | Morphological analysis in the Universal Dependencies [FEATS]https://universaldependencies.org/format.html#morphological-annotation format. ~~str~~ |
| `dep` | Syntactic dependency relation. ~~int~~ |
| `dep_` | Syntactic dependency relation. ~~str~~ |
| `lang` | Language of the parent document's vocabulary. ~~int~~ |
| `lang_` | Language of the parent document's vocabulary. ~~str~~ |
| `prob` | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). ~~float~~ |
| `idx` | The character offset of the token within the parent document. ~~int~~ |
| `sentiment` | A scalar value indicating the positivity or negativity of the token. ~~float~~ |
| `lex_id` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ |
| `rank` | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ |
| `cluster` | Brown cluster ID. ~~int~~ |
| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ |

136
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@ -45,15 +45,15 @@ the
> tokenizer = nlp.tokenizer
> ```
| Name | Type | Description |
| ---------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | A storage container for lexical types. |
| `rules` | dict | Exceptions and special-cases for the tokenizer. |
| `prefix_search` | callable | A function matching the signature of `re.compile(string).search` to match prefixes. |
| `suffix_search` | callable | A function matching the signature of `re.compile(string).search` to match suffixes. |
| `infix_finditer` | callable | A function matching the signature of `re.compile(string).finditer` to find infixes. |
| `token_match` | callable | A function matching the signature of `re.compile(string).match` to find token matches. |
| `url_match` | callable | A function matching the signature of `re.compile(string).match` to find token matches after considering prefixes and suffixes. |
| Name | Description |
| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | A storage container for lexical types. ~~Vocab~~ |
| `rules` | Exceptions and special-cases for the tokenizer. ~~Optional[Dict[str, List[Dict[int, str]]]]~~ |
| `prefix_search` | A function matching the signature of `re.compile(string).search` to match prefixes. ~~Optional[Callable[[str], Optional[Match]]]~~ |
| `suffix_search` | A function matching the signature of `re.compile(string).search` to match suffixes. ~~Optional[Callable[[str], Optional[Match]]]~~ |
| `infix_finditer` | A function matching the signature of `re.compile(string).finditer` to find infixes. ~~Optional[Callable[[str], Iterator[Match]]]~~ |
| `token_match` | A function matching the signature of `re.compile(string).match` to find token matches. ~~Optional[Callable[[str], Optional[Match]]]~~ |
| `url_match` | A function matching the signature of `re.compile(string).match` to find token matches after considering prefixes and suffixes. ~~Optional[Callable[[str], Optional[Match]]]~~ |
## Tokenizer.\_\_call\_\_ {#call tag="method"}
@ -66,10 +66,10 @@ Tokenize a string.
> assert len(tokens) == 4
> ```
| Name | Type | Description |
| ----------- | ----- | --------------------------------------- |
| `string` | str | The string to tokenize. |
| **RETURNS** | `Doc` | A container for linguistic annotations. |
| Name | Description |
| ----------- | ----------------------------------------------- |
| `string` | The string to tokenize. ~~str~~ |
| **RETURNS** | A container for linguistic annotations. ~~Doc~~ |
## Tokenizer.pipe {#pipe tag="method"}
@ -83,40 +83,40 @@ Tokenize a stream of texts.
> pass
> ```
| Name | Type | Description |
| ------------ | ----- | ---------------------------------------------------------------------------- |
| `texts` | - | A sequence of unicode texts. |
| `batch_size` | int | The number of texts to accumulate in an internal buffer. Defaults to `1000`. |
| **YIELDS** | `Doc` | A sequence of Doc objects, in order. |
| Name | Description |
| ------------ | ------------------------------------------------------------------------------------ |
| `texts` | A sequence of unicode texts. ~~Iterable[str]~~ |
| `batch_size` | The number of texts to accumulate in an internal buffer. Defaults to `1000`. ~~int~~ |
| **YIELDS** | The tokenized Doc objects, in order. ~~Doc~~ |
## Tokenizer.find_infix {#find_infix tag="method"}
Find internal split points of the string.
| Name | Type | Description |
| ----------- | ---- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `string` | str | The string to split. |
| **RETURNS** | list | A list of `re.MatchObject` objects that have `.start()` and `.end()` methods, denoting the placement of internal segment separators, e.g. hyphens. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `string` | The string to split. ~~str~~ |
| **RETURNS** | A list of `re.MatchObject` objects that have `.start()` and `.end()` methods, denoting the placement of internal segment separators, e.g. hyphens. ~~List[Match]~~ |
## Tokenizer.find_prefix {#find_prefix tag="method"}
Find the length of a prefix that should be segmented from the string, or `None`
if no prefix rules match.
| Name | Type | Description |
| ----------- | ---- | ------------------------------------------------------ |
| `string` | str | The string to segment. |
| **RETURNS** | int | The length of the prefix if present, otherwise `None`. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------ |
| `string` | The string to segment. ~~str~~ |
| **RETURNS** | The length of the prefix if present, otherwise `None`. ~~Optional[int]~~ |
## Tokenizer.find_suffix {#find_suffix tag="method"}
Find the length of a suffix that should be segmented from the string, or `None`
if no suffix rules match.
| Name | Type | Description |
| ----------- | ------------ | ------------------------------------------------------ |
| `string` | str | The string to segment. |
| **RETURNS** | int / `None` | The length of the suffix if present, otherwise `None`. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------ |
| `string` | The string to segment. ~~str~~ |
| **RETURNS** | The length of the suffix if present, otherwise `None`. ~~Optional[int]~~ |
## Tokenizer.add_special_case {#add_special_case tag="method"}
@ -134,10 +134,10 @@ and examples.
> tokenizer.add_special_case("don't", case)
> ```
| Name | Type | Description |
| ------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `string` | str | The string to specially tokenize. |
| `token_attrs` | iterable | A sequence of dicts, where each dict describes a token and its attributes. The `ORTH` fields of the attributes must exactly match the string when they are concatenated. |
| Name | Description |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `string` | The string to specially tokenize. ~~str~~ |
| `token_attrs` | A sequence of dicts, where each dict describes a token and its attributes. The `ORTH` fields of the attributes must exactly match the string when they are concatenated. ~~Iterable[Dict[int, str]]~~ |
## Tokenizer.explain {#explain tag="method"}
@ -153,10 +153,10 @@ produced are identical to `Tokenizer.__call__` except for whitespace tokens.
> assert [t[1] for t in tok_exp] == ["(", "do", "n't", ")"]
> ```
| Name | Type | Description |
| ----------- | ---- | --------------------------------------------------- |
| `string` | str | The string to tokenize with the debugging tokenizer |
| **RETURNS** | list | A list of `(pattern_string, token_string)` tuples |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------- |
| `string` | The string to tokenize with the debugging tokenizer. ~~str~~ |
| **RETURNS** | A list of `(pattern_string, token_string)` tuples. ~~List[Tuple[str, str]]~~ |
## Tokenizer.to_disk {#to_disk tag="method"}
@ -169,11 +169,11 @@ Serialize the tokenizer to disk.
> tokenizer.to_disk("/path/to/tokenizer")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Tokenizer.from_disk {#from_disk tag="method"}
@ -186,12 +186,12 @@ Load the tokenizer from disk. Modifies the object in place and returns it.
> tokenizer.from_disk("/path/to/tokenizer")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tokenizer` | The modified `Tokenizer` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Tokenizer` object. ~~Tokenizer~~ |
## Tokenizer.to_bytes {#to_bytes tag="method"}
@ -204,11 +204,11 @@ Load the tokenizer from disk. Modifies the object in place and returns it.
Serialize the tokenizer to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Tokenizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `Tokenizer` object. ~~bytes~~ |
## Tokenizer.from_bytes {#from_bytes tag="method"}
@ -223,23 +223,23 @@ it.
> tokenizer.from_bytes(tokenizer_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tokenizer` | The `Tokenizer` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Tokenizer` object. ~~Tokenizer~~ |
## Attributes {#attributes}
| Name | Type | Description |
| ---------------- | ------- | -------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The vocab object of the parent `Doc`. |
| `prefix_search` | - | A function to find segment boundaries from the start of a string. Returns the length of the segment, or `None`. |
| `suffix_search` | - | A function to find segment boundaries from the end of a string. Returns the length of the segment, or `None`. |
| `infix_finditer` | - | A function to find internal segment separators, e.g. hyphens. Returns a (possibly empty) list of `re.MatchObject` objects. |
| `token_match` | - | A function matching the signature of `re.compile(string).match to find token matches. Returns an`re.MatchObject`or`None. |
| `rules` | dict | A dictionary of tokenizer exceptions and special cases. |
| Name | Description |
| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The vocab object of the parent `Doc`. ~~Vocab~~ |
| `prefix_search` | A function to find segment boundaries from the start of a string. Returns the length of the segment, or `None`. ~~Optional[Callable[[str], Optional[Match]]]~~ |
| `suffix_search` | A function to find segment boundaries from the end of a string. Returns the length of the segment, or `None`. ~~Optional[Callable[[str], Optional[Match]]]~~ |
| `infix_finditer` | A function to find internal segment separators, e.g. hyphens. Returns a (possibly empty) sequence of `re.MatchObject` objects. ~~Optional[Callable[[str], Iterator[Match]]]~~ |
| `token_match` | A function matching the signature of `re.compile(string).match` to find token matches. Returns an `re.MatchObject` or `None`. ~~Optional[Callable[[str], Optional[Match]]]~~ |
| `rules` | A dictionary of tokenizer exceptions and special cases. ~~Optional[Dict[str, List[Dict[int, str]]]]~~ |
## Serialization fields {#serialization-fields}

513
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View File

@ -18,9 +18,10 @@ Load a model using the name of an installed
`Path`-like object. spaCy will try resolving the load argument in this order. If
a model is loaded from a model name, spaCy will assume it's a Python package and
import it and call the model's own `load()` method. If a model is loaded from a
path, spaCy will assume it's a data directory, read the language and pipeline
settings off the meta.json and initialize the `Language` class. The data will be
loaded in via [`Language.from_disk`](/api/language#from_disk).
path, spaCy will assume it's a data directory, load its
[`config.cfg`](/api/data-formats#config) and use the language and pipeline
information to construct the `Language` class. The data will be loaded in via
[`Language.from_disk`](/api/language#from_disk).
> #### Example
>
@ -32,17 +33,18 @@ loaded in via [`Language.from_disk`](/api/language#from_disk).
> nlp = spacy.load("en_core_web_sm", disable=["parser", "tagger"])
> ```
| Name | Type | Description |
| ----------------------------------- | ---------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str / `Path` | Model to load, i.e. package name or path. |
| _keyword-only_ | | |
| `disable` | `List[str]` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
| `config` <Tag variant="new">3</Tag> | `Dict[str, Any]` / [`Config`](https://thinc.ai/docs/api-config#config) | Optional config overrides, either as nested dict or dict keyed by section value in dot notation, e.g. `"components.name.value"`. |
| **RETURNS** | `Language` | A `Language` object with the loaded model. |
| Name | Description |
| ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `name` | Model to load, i.e. package name or path. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~ |
| `config` <Tag variant="new">3</Tag> | Optional config overrides, either as nested dict or dict keyed by section value in dot notation, e.g. `"components.name.value"`. ~~Union[Dict[str, Any], Config]~~ |
| **RETURNS** | A `Language` object with the loaded model. ~~Language~~ |
Essentially, `spacy.load()` is a convenience wrapper that reads the language ID
and pipeline components from a model's `meta.json`, initializes the `Language`
class, loads in the model data and returns it.
Essentially, `spacy.load()` is a convenience wrapper that reads the model's
[`config.cfg`](/api/data-formats#config), uses the language and pipeline
information to construct a `Language` object, loads in the model data and
returns it.
```python
### Abstract example
@ -65,12 +67,12 @@ Create a blank model of a given language class. This function is the twin of
> nlp_de = spacy.blank("de") # equivalent to German()
> ```
| Name | Type | Description |
| ----------- | ---------- | ------------------------------------------------------------------------------------------------ |
| `name` | str | [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) of the language class to load. |
| **RETURNS** | `Language` | An empty `Language` object of the appropriate subclass. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------- |
| `name` | [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) of the language class to load. ~~str~~ |
| **RETURNS** | An empty `Language` object of the appropriate subclass. ~~Language~~ |
#### spacy.info {#spacy.info tag="function"}
### spacy.info {#spacy.info tag="function"}
The same as the [`info` command](/api/cli#info). Pretty-print information about
your installation, models and local setup from within spaCy. To get the model
@ -85,12 +87,12 @@ meta data as a dictionary instead, you can use the `meta` attribute on your
> markdown = spacy.info(markdown=True, silent=True)
> ```
| Name | Type | Description |
| -------------- | ---- | ------------------------------------------------ |
| `model` | str | A model, i.e. a package name or path (optional). |
| _keyword-only_ | | |
| `markdown` | bool | Print information as Markdown. |
| `silent` | bool | Don't print anything, just return. |
| Name | Description |
| -------------- | ------------------------------------------------------------------ |
| `model` | A model, i.e. a package name or path (optional). ~~Optional[str]~~ |
| _keyword-only_ | |
| `markdown` | Print information as Markdown. ~~bool~~ |
| `silent` | Don't print anything, just return. ~~bool~~ |
### spacy.explain {#spacy.explain tag="function"}
@ -111,10 +113,10 @@ list of available terms, see
> # world NN noun, singular or mass
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------------------------- |
| `term` | str | Term to explain. |
| **RETURNS** | str | The explanation, or `None` if not found in the glossary. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------- |
| `term` | Term to explain. ~~str~~ |
| **RETURNS** | The explanation, or `None` if not found in the glossary. ~~Optional[str]~~ |
### spacy.prefer_gpu {#spacy.prefer_gpu tag="function" new="2.0.14"}
@ -131,9 +133,9 @@ models.
> nlp = spacy.load("en_core_web_sm")
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------ |
| **RETURNS** | bool | Whether the GPU was activated. |
| Name | Description |
| ----------- | --------------------------------------- |
| **RETURNS** | Whether the GPU was activated. ~~bool~~ |
### spacy.require_gpu {#spacy.require_gpu tag="function" new="2.0.14"}
@ -150,9 +152,9 @@ and _before_ loading any models.
> nlp = spacy.load("en_core_web_sm")
> ```
| Name | Type | Description |
| ----------- | ---- | ----------- |
| **RETURNS** | bool | `True` |
| Name | Description |
| ----------- | --------------- |
| **RETURNS** | `True` ~~bool~~ |
## displaCy {#displacy source="spacy/displacy"}
@ -175,16 +177,16 @@ browser. Will run a simple web server.
> displacy.serve([doc1, doc2], style="dep")
> ```
| Name | Type | Description | Default |
| --------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ----------- |
| `docs` | list, `Doc`, `Span` | Document(s) to visualize. |
| `style` | str | Visualization style, `'dep'` or `'ent'`. | `'dep'` |
| `page` | bool | Render markup as full HTML page. | `True` |
| `minify` | bool | Minify HTML markup. | `False` |
| `options` | dict | [Visualizer-specific options](#displacy_options), e.g. colors. | `{}` |
| `manual` | bool | Don't parse `Doc` and instead, expect a dict or list of dicts. [See here](/usage/visualizers#manual-usage) for formats and examples. | `False` |
| `port` | int | Port to serve visualization. | `5000` |
| `host` | str | Host to serve visualization. | `'0.0.0.0'` |
| Name | Description |
| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `docs` | Document(s) or span(s) to visualize. ~~Union[Iterable[Union[Doc, Span]], Doc, Span]~~ |
| `style` | Visualization style, `"dep"` or `"ent"`. Defaults to `"dep"`. ~~str~~ |
| `page` | Render markup as full HTML page. Defaults to `True`. ~~bool~~ |
| `minify` | Minify HTML markup. Defaults to `False`. ~~bool~~ |
| `options` | [Visualizer-specific options](#displacy_options), e.g. colors. ~~Dict[str, Any]~~ |
| `manual` | Don't parse `Doc` and instead, expect a dict or list of dicts. [See here](/usage/visualizers#manual-usage) for formats and examples. Defaults to `False`. ~~bool~~ |
| `port` | Port to serve visualization. Defaults to `5000`. ~~int~~ |
| `host` | Host to serve visualization. Defaults to `"0.0.0.0"`. ~~str~~ |
### displacy.render {#displacy.render tag="method" new="2"}
@ -200,16 +202,16 @@ Render a dependency parse tree or named entity visualization.
> html = displacy.render(doc, style="dep")
> ```
| Name | Type | Description | Default |
| ----------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| `docs` | list, `Doc`, `Span` | Document(s) to visualize. |
| `style` | str | Visualization style, `'dep'` or `'ent'`. | `'dep'` |
| `page` | bool | Render markup as full HTML page. | `False` |
| `minify` | bool | Minify HTML markup. | `False` |
| `jupyter` | bool | Explicitly enable or disable "[Jupyter](http://jupyter.org/) mode" to return markup ready to be rendered in a notebook. Detected automatically if `None`. | `None` |
| `options` | dict | [Visualizer-specific options](#displacy_options), e.g. colors. | `{}` |
| `manual` | bool | Don't parse `Doc` and instead, expect a dict or list of dicts. [See here](/usage/visualizers#manual-usage) for formats and examples. | `False` |
| **RETURNS** | str | Rendered HTML markup. |
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `docs` | Document(s) or span(s) to visualize. ~~Union[Iterable[Union[Doc, Span]], Doc, Span]~~ |
| `style` | Visualization style, `"dep"` or `"ent"`. Defaults to `"dep"`. ~~str~~ |
| `page` | Render markup as full HTML page. Defaults to `True`. ~~bool~~ |
| `minify` | Minify HTML markup. Defaults to `False`. ~~bool~~ |
| `options` | [Visualizer-specific options](#displacy_options), e.g. colors. ~~Dict[str, Any]~~ |
| `manual` | Don't parse `Doc` and instead, expect a dict or list of dicts. [See here](/usage/visualizers#manual-usage) for formats and examples. Defaults to `False`. ~~bool~~ |
| `jupyter` | Explicitly enable or disable "[Jupyter](http://jupyter.org/) mode" to return markup ready to be rendered in a notebook. Detected automatically if `None` (default). ~~Optional[bool]~~ |
| **RETURNS** | The rendered HTML markup. ~~str~~ |
### Visualizer options {#displacy_options}
@ -225,22 +227,22 @@ If a setting is not present in the options, the default value will be used.
> displacy.serve(doc, style="dep", options=options)
> ```
| Name | Type | Description | Default |
| ------------------------------------------ | ---- | --------------------------------------------------------------------------------------------------------------- | ----------------------- |
| `fine_grained` | bool | Use fine-grained part-of-speech tags (`Token.tag_`) instead of coarse-grained tags (`Token.pos_`). | `False` |
| `add_lemma` <Tag variant="new">2.2.4</Tag> | bool | Print the lemma's in a separate row below the token texts. | `False` |
| `collapse_punct` | bool | Attach punctuation to tokens. Can make the parse more readable, as it prevents long arcs to attach punctuation. | `True` |
| `collapse_phrases` | bool | Merge noun phrases into one token. | `False` |
| `compact` | bool | "Compact mode" with square arrows that takes up less space. | `False` |
| `color` | str | Text color (HEX, RGB or color names). | `'#000000'` |
| `bg` | str | Background color (HEX, RGB or color names). | `'#ffffff'` |
| `font` | str | Font name or font family for all text. | `'Arial'` |
| `offset_x` | int | Spacing on left side of the SVG in px. | `50` |
| `arrow_stroke` | int | Width of arrow path in px. | `2` |
| `arrow_width` | int | Width of arrow head in px. | `10` / `8` (compact) |
| `arrow_spacing` | int | Spacing between arrows in px to avoid overlaps. | `20` / `12` (compact) |
| `word_spacing` | int | Vertical spacing between words and arcs in px. | `45` |
| `distance` | int | Distance between words in px. | `175` / `150` (compact) |
| Name | Description |
| ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------- |
| `fine_grained` | Use fine-grained part-of-speech tags (`Token.tag_`) instead of coarse-grained tags (`Token.pos_`). Defaults to `False`. ~~bool~~ |
| `add_lemma` <Tag variant="new">2.2.4</Tag> | Print the lemma's in a separate row below the token texts. Defaults to `False`. ~~bool~~ |
| `collapse_punct` | Attach punctuation to tokens. Can make the parse more readable, as it prevents long arcs to attach punctuation. Defaults to `True`. ~~bool~~ |
| `collapse_phrases` | Merge noun phrases into one token. Defaults to `False`. ~~bool~~ |
| `compact` | "Compact mode" with square arrows that takes up less space. Defaults to `False`. ~~bool~~ |
| `color` | Text color (HEX, RGB or color names). Defaults to `"#000000"`. ~~str~~ |
| `bg` | Background color (HEX, RGB or color names). Defaults to `"#ffffff"`. ~~str~~ |
| `font` | Font name or font family for all text. Defaults to `"Arial"`. ~~str~~ |
| `offset_x` | Spacing on left side of the SVG in px. Defaults to `50`. ~~int~~ |
| `arrow_stroke` | Width of arrow path in px. Defaults to `2`. ~~int~~ |
| `arrow_width` | Width of arrow head in px. Defaults to `10` in regular mode and `8` in compact mode. ~~int~~ |
| `arrow_spacing` | Spacing between arrows in px to avoid overlaps. Defaults to `20` in regular mode and `12` in compact mode. ~~int~~ |
| `word_spacing` | Vertical spacing between words and arcs in px. Defaults to `45`. ~~int~~ |
| `distance` | Distance between words in px. Defaults to `175` in regular mode and `150` in compact mode. ~~int~~ |
#### Named Entity Visualizer options {#displacy_options-ent}
@ -252,11 +254,11 @@ If a setting is not present in the options, the default value will be used.
> displacy.serve(doc, style="ent", options=options)
> ```
| Name | Type | Description | Default |
| --------------------------------------- | ---- | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------ |
| `ents` | list | Entity types to highlight (`None` for all types). | `None` |
| `colors` | dict | Color overrides. Entity types in uppercase should be mapped to color names or values. | `{}` |
| `template` <Tag variant="new">2.2</Tag> | str | Optional template to overwrite the HTML used to render entity spans. Should be a format string and can use `{bg}`, `{text}` and `{label}`. | see [`templates.py`](https://github.com/explosion/spaCy/blob/master/spacy/displacy/templates.py) |
| Name | Description |
| --------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `ents` | Entity types to highlight or `None` for all types (default). ~~Optional[List[str]]~~ |
| `colors` | Color overrides. Entity types should be mapped to color names or values. ~~Dict[str, str]~~ |
| `template` <Tag variant="new">2.2</Tag> | Optional template to overwrite the HTML used to render entity spans. Should be a format string and can use `{bg}`, `{text}` and `{label}`. See [`templates.py`](https://github.com/explosion/spaCy/blob/master/spacy/displacy/templates.py) for examples. ~~Optional[str]~~ |
By default, displaCy comes with colors for all entity types used by
[spaCy models](/models). If you're using custom entity types, you can use the
@ -280,43 +282,44 @@ concept of function registries. spaCy also uses the function registry for
language subclasses, model architecture, lookups and pipeline component
factories.
<!-- TODO: improve example? -->
> #### Example
>
> ```python
> from typing import Iterator
> import spacy
> from thinc.api import Model
>
> @spacy.registry.architectures("CustomNER.v1")
> def custom_ner(n0: int) -> Model:
> return Model("custom", forward, dims={"nO": nO})
> @spacy.registry.schedules("waltzing.v1")
> def waltzing() -> Iterator[float]:
> i = 0
> while True:
> yield i % 3 + 1
> i += 1
> ```
| Registry name | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `architectures` | Registry for functions that create [model architectures](/api/architectures). Can be used to register custom model architectures and reference them in the `config.cfg`. |
| `factories` | Registry for functions that create [pipeline components](/usage/processing-pipelines#custom-components). Added automatically when you use the `@spacy.component` decorator and also reads from [entry points](/usage/saving-loading#entry-points) |
| `tokenizers` | Registry for tokenizer factories. Registered functions should return a callback that receives the `nlp` object and returns a [`Tokenizer`](/api/tokenizer) or a custom callable. |
| `languages` | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points). |
| `lookups` | Registry for large lookup tables available via `vocab.lookups`. |
| `displacy_colors` | Registry for custom color scheme for the [`displacy` NER visualizer](/usage/visualizers). Automatically reads from [entry points](/usage/saving-loading#entry-points). |
| `assets` | Registry for data assets, knowledge bases etc. |
| `callbacks` | Registry for custom callbacks to [modify the `nlp` object](/usage/training#custom-code-nlp-callbacks) before training. |
| `readers` | Registry for training and evaluation data readers like [`Corpus`](/api/corpus). |
| `batchers` | Registry for training and evaluation [data batchers](#batchers). |
| `optimizers` | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers). |
| `schedules` | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules). |
| `layers` | Registry for functions that create [layers](https://thinc.ai/docs/api-layers). |
| `losses` | Registry for functions that create [losses](https://thinc.ai/docs/api-loss). |
| `initializers` | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers). |
| Registry name | Description |
| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `architectures` | Registry for functions that create [model architectures](/api/architectures). Can be used to register custom model architectures and reference them in the `config.cfg`. |
| `assets` | Registry for data assets, knowledge bases etc. |
| `batchers` | Registry for training and evaluation [data batchers](#batchers). |
| `callbacks` | Registry for custom callbacks to [modify the `nlp` object](/usage/training#custom-code-nlp-callbacks) before training. |
| `displacy_colors` | Registry for custom color scheme for the [`displacy` NER visualizer](/usage/visualizers). Automatically reads from [entry points](/usage/saving-loading#entry-points). |
| `factories` | Registry for functions that create [pipeline components](/usage/processing-pipelines#custom-components). Added automatically when you use the `@spacy.component` decorator and also reads from [entry points](/usage/saving-loading#entry-points). |
| `initializers` | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers). |
| `languages` | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points). |
| `layers` | Registry for functions that create [layers](https://thinc.ai/docs/api-layers). |
| `lookups` | Registry for large lookup tables available via `vocab.lookups`. |
| `losses` | Registry for functions that create [losses](https://thinc.ai/docs/api-loss). |
| `optimizers` | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers). |
| `readers` | Registry for training and evaluation data readers like [`Corpus`](/api/corpus). |
| `schedules` | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules). |
| `tokenizers` | Registry for tokenizer factories. Registered functions should return a callback that receives the `nlp` object and returns a [`Tokenizer`](/api/tokenizer) or a custom callable. |
### spacy-transformers registry {#registry-transformers}
The following registries are added by the
[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package.
See the [`Transformer`](/api/transformer) API reference and
[usage docs](/usage/transformers) for details.
[usage docs](/usage/embeddings-transformers) for details.
> #### Example
>
@ -338,7 +341,17 @@ See the [`Transformer`](/api/transformer) API reference and
## Batchers {#batchers source="spacy/gold/batchers.py" new="3"}
<!-- TODO: intro -->
A data batcher implements a batching strategy that essentially turns a stream of
items into a stream of batches, with each batch consisting of one item or a list
of items. During training, the models update their weights after processing one
batch at a time. Typical batching strategies include presenting the training
data as a stream of batches with similar sizes, or with increasing batch sizes.
See the Thinc documentation on
[`schedules`](https://thinc.ai/docs/api-schedules) for a few standard examples.
Instead of using one of the built-in batchers listed here, you can also
[implement your own](/usage/training#custom-code-readers-batchers), which may or
may not use a custom schedule.
#### batch_by_words.v1 {#batch_by_words tag="registered function"}
@ -359,13 +372,13 @@ themselves, or be discarded if `discard_oversize` is set to `True`. The argument
> get_length = null
> ```
| Name | Type | Description |
| ------------------ | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `seqs` | `Iterable[Any]` | The sequences to minibatch. |
| `size` | `Iterable[int]` / int | The target number of words per batch. Can also be a block referencing a schedule, e.g. [`compounding`](https://thinc.ai/docs/api-schedules/#compounding). |
| `tolerance` | float | What percentage of the size to allow batches to exceed. |
| `discard_oversize` | bool | Whether to discard sequences that by themselves exceed the tolerated size. |
| `get_length` | `Callable[[Any], int]` | Optional function that receives a sequence item and returns its length. Defaults to the built-in `len()` if not set. |
| Name | Description |
| ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `seqs` | The sequences to minibatch. ~~Iterable[Any]~~ |
| `size` | The target number of words per batch. Can also be a block referencing a schedule, e.g. [`compounding`](https://thinc.ai/docs/api-schedules/#compounding). ~~Union[int, Sequence[int]]~~ |
| `tolerance` | What percentage of the size to allow batches to exceed. ~~float~~ |
| `discard_oversize` | Whether to discard sequences that by themselves exceed the tolerated size. ~~bool~~ |
| `get_length` | Optional function that receives a sequence item and returns its length. Defaults to the built-in `len()` if not set. ~~Optional[Callable[[Any], int]]~~ |
#### batch_by_sequence.v1 {#batch_by_sequence tag="registered function"}
@ -380,10 +393,10 @@ themselves, or be discarded if `discard_oversize` is set to `True`. The argument
Create a batcher that creates batches of the specified size.
| Name | Type | Description |
| ------------ | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `size` | `Iterable[int]` / int | The target number of items per batch. Can also be a block referencing a schedule, e.g. [`compounding`](https://thinc.ai/docs/api-schedules/#compounding). |
| `get_length` | `Callable[[Any], int]` | Optional function that receives a sequence item and returns its length. Defaults to the built-in `len()` if not set. |
| Name | Description |
| ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `size` | The target number of items per batch. Can also be a block referencing a schedule, e.g. [`compounding`](https://thinc.ai/docs/api-schedules/#compounding). ~~Union[int, Sequence[int]]~~ |
| `get_length` | Optional function that receives a sequence item and returns its length. Defaults to the built-in `len()` if not set. ~~Optional[Callable[[Any], int]]~~ |
#### batch_by_padded.v1 {#batch_by_padded tag="registered function"}
@ -403,12 +416,12 @@ sequences binned by length within a window. The padded size is defined as the
maximum length of sequences within the batch multiplied by the number of
sequences in the batch.
| Name | Type | Description |
| ------------------ | ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `size` | `Iterable[int]` / int | The largest padded size to batch sequences into. Can also be a block referencing a schedule, e.g. [`compounding`](https://thinc.ai/docs/api-schedules/#compounding). |
| `buffer` | int | The number of sequences to accumulate before sorting by length. A larger buffer will result in more even sizing, but if the buffer is very large, the iteration order will be less random, which can result in suboptimal training. |
| `discard_oversize` | bool | Whether to discard sequences that are by themselves longer than the largest padded batch size. |
| `get_length` | `Callable[[Any], int]` | Optional function that receives a sequence item and returns its length. Defaults to the built-in `len()` if not set. |
| Name | Description |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `size` | The largest padded size to batch sequences into. Can also be a block referencing a schedule, e.g. [`compounding`](https://thinc.ai/docs/api-schedules/#compounding). ~~Union[int, Sequence[int]]~~ |
| `buffer` | The number of sequences to accumulate before sorting by length. A larger buffer will result in more even sizing, but if the buffer is very large, the iteration order will be less random, which can result in suboptimal training. ~~int~~ |
| `discard_oversize` | Whether to discard sequences that are by themselves longer than the largest padded batch size. ~~bool~~ |
| `get_length` | Optional function that receives a sequence item and returns its length. Defaults to the built-in `len()` if not set. ~~Optional[Callable[[Any], int]]~~ |
## Training data and alignment {#gold source="spacy/gold"}
@ -436,11 +449,11 @@ single-token entity.
> assert tags == ["O", "O", "U-LOC", "O"]
> ```
| Name | Type | Description |
| ----------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The document that the entity offsets refer to. The output tags will refer to the token boundaries within the document. |
| `entities` | iterable | A sequence of `(start, end, label)` triples. `start` and `end` should be character-offset integers denoting the slice into the original string. |
| **RETURNS** | list | str strings, describing the [BILUO](/usage/linguistic-features#accessing-ner) tags. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `doc` | The document that the entity offsets refer to. The output tags will refer to the token boundaries within the document. ~~Doc~~ |
| `entities` | A sequence of `(start, end, label)` triples. `start` and `end` should be character-offset integers denoting the slice into the original string. ~~List[Tuple[int, int, Union[str, int]]]~~ |
| **RETURNS** | A list of strings, describing the [BILUO](/usage/linguistic-features#accessing-ner) tags. ~~List[str]~~ |
### gold.offsets_from_biluo_tags {#offsets_from_biluo_tags tag="function"}
@ -458,11 +471,11 @@ Encode per-token tags following the
> assert entities == [(7, 13, "LOC")]
> ```
| Name | Type | Description |
| ----------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The document that the BILUO tags refer to. |
| `entities` | iterable | A sequence of [BILUO](/usage/linguistic-features#accessing-ner) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. |
| **RETURNS** | list | A sequence of `(start, end, label)` triples. `start` and `end` will be character-offset integers denoting the slice into the original string. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `doc` | The document that the BILUO tags refer to. ~~Doc~~ |
| `entities` | A sequence of [BILUO](/usage/linguistic-features#accessing-ner) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. ~~List[str]~~ |
| **RETURNS** | A sequence of `(start, end, label)` triples. `start` and `end` will be character-offset integers denoting the slice into the original string. ~~List[Tuple[int, int, str]]~~ |
### gold.spans_from_biluo_tags {#spans_from_biluo_tags tag="function" new="2.1"}
@ -481,11 +494,11 @@ token-based tags, e.g. to overwrite the `doc.ents`.
> doc.ents = spans_from_biluo_tags(doc, tags)
> ```
| Name | Type | Description |
| ----------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The document that the BILUO tags refer to. |
| `entities` | iterable | A sequence of [BILUO](/usage/linguistic-features#accessing-ner) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. |
| **RETURNS** | list | A sequence of `Span` objects with added entity labels. |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `doc` | The document that the BILUO tags refer to. ~~Doc~~ |
| `entities` | A sequence of [BILUO](/usage/linguistic-features#accessing-ner) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. ~~List[str]~~ |
| **RETURNS** | A sequence of `Span` objects with added entity labels. ~~List[Span]~~ |
## Utility functions {#util source="spacy/util.py"}
@ -497,14 +510,13 @@ page should be safe to use and we'll try to ensure backwards compatibility.
However, we recommend having additional tests in place if your application
depends on any of spaCy's utilities.
<!-- TODO: document new config-related util functions? -->
### util.get_lang_class {#util.get_lang_class tag="function"}
Import and load a `Language` class. Allows lazy-loading
[language data](/usage/adding-languages) and importing languages using the
two-letter language code. To add a language code for a custom language class,
you can use the [`set_lang_class`](/api/top-level#util.set_lang_class) helper.
you can register it using the [`@registry.languages`](/api/top-level#registry)
decorator.
> #### Example
>
@ -514,36 +526,14 @@ you can use the [`set_lang_class`](/api/top-level#util.set_lang_class) helper.
> lang = lang_class()
> ```
| Name | Type | Description |
| ----------- | ---------- | -------------------------------------- |
| `lang` | str | Two-letter language code, e.g. `'en'`. |
| **RETURNS** | `Language` | Language class. |
### util.set_lang_class {#util.set_lang_class tag="function"}
Set a custom `Language` class name that can be loaded via
[`get_lang_class`](/api/top-level#util.get_lang_class). If your model uses a
custom language, this is required so that spaCy can load the correct class from
the two-letter language code.
> #### Example
>
> ```python
> from spacy.lang.xy import CustomLanguage
>
> util.set_lang_class('xy', CustomLanguage)
> lang_class = util.get_lang_class('xy')
> nlp = lang_class()
> ```
| Name | Type | Description |
| ------ | ---------- | -------------------------------------- |
| `name` | str | Two-letter language code, e.g. `'en'`. |
| `cls` | `Language` | The language class, e.g. `English`. |
| Name | Description |
| ----------- | ---------------------------------------------- |
| `lang` | Two-letter language code, e.g. `"en"`. ~~str~~ |
| **RETURNS** | The respective subclass. ~~Language~~ |
### util.lang_class_is_loaded {#util.lang_class_is_loaded tag="function" new="2.1"}
Check whether a `Language` class is already loaded. `Language` classes are
Check whether a `Language` subclass is already loaded. `Language` subclasses are
loaded lazily, to avoid expensive setup code associated with the language data.
> #### Example
@ -554,19 +544,19 @@ loaded lazily, to avoid expensive setup code associated with the language data.
> assert util.lang_class_is_loaded("de") is False
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------- |
| `name` | str | Two-letter language code, e.g. `'en'`. |
| **RETURNS** | bool | Whether the class has been loaded. |
| Name | Description |
| ----------- | ---------------------------------------------- |
| `name` | Two-letter language code, e.g. `"en"`. ~~str~~ |
| **RETURNS** | Whether the class has been loaded. ~~bool~~ |
### util.load_model {#util.load_model tag="function" new="2"}
Load a model from a package or data path. If called with a package name, spaCy
will assume the model is a Python package and import and call its `load()`
method. If called with a path, spaCy will assume it's a data directory, read the
language and pipeline settings from the meta.json and initialize a `Language`
class. The model data will then be loaded in via
[`Language.from_disk()`](/api/language#from_disk).
language and pipeline settings from the [`config.cfg`](/api/data-formats#config)
and create a `Language` object. The model data will then be loaded in via
[`Language.from_disk`](/api/language#from_disk).
> #### Example
>
@ -576,31 +566,13 @@ class. The model data will then be loaded in via
> nlp = util.load_model("/path/to/data")
> ```
| Name | Type | Description |
| ------------- | ---------- | -------------------------------------------------------- |
| `name` | str | Package name or model path. |
| `**overrides` | - | Specific overrides, like pipeline components to disable. |
| **RETURNS** | `Language` | `Language` class with the loaded model. |
### util.load_model_from_path {#util.load_model_from_path tag="function" new="2"}
Load a model from a data directory path. Creates the [`Language`](/api/language)
class and pipeline based on the directory's meta.json and then calls
[`from_disk()`](/api/language#from_disk) with the path. This function also makes
it easy to test a new model that you haven't packaged yet.
> #### Example
>
> ```python
> nlp = load_model_from_path("/path/to/data")
> ```
| Name | Type | Description |
| ------------- | ---------- | ---------------------------------------------------------------------------------------------------- |
| `model_path` | str | Path to model data directory. |
| `meta` | dict | Model meta data. If `False`, spaCy will try to load the meta from a meta.json in the same directory. |
| `**overrides` | - | Specific overrides, like pipeline components to disable. |
| **RETURNS** | `Language` | `Language` class with the loaded model. |
| Name | Description |
| ----------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Package name or model path. ~~str~~ |
| `vocab` <Tag variant="new">3</Tag> | Optional shared vocab to pass in on initialization. If `True` (default), a new `Vocab` object will be created. ~~Union[Vocab, bool]~~. |
| `disable` | Names of pipeline components to disable. ~~Iterable[str]~~ |
| `config` <Tag variant="new">3</Tag> | Config overrides as nested dict or flat dict keyed by section values in dot notation, e.g. `"nlp.pipeline"`. ~~Union[Dict[str, Any], Config]~~ |
| **RETURNS** | `Language` class with the loaded model. ~~Language~~ |
### util.load_model_from_init_py {#util.load_model_from_init_py tag="function" new="2"}
@ -616,26 +588,66 @@ A helper function to use in the `load()` method of a model package's
> return load_model_from_init_py(__file__, **overrides)
> ```
| Name | Type | Description |
| ------------- | ---------- | -------------------------------------------------------- |
| `init_file` | str | Path to model's `__init__.py`, i.e. `__file__`. |
| `**overrides` | - | Specific overrides, like pipeline components to disable. |
| **RETURNS** | `Language` | `Language` class with the loaded model. |
| Name | Description |
| ----------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `init_file` | Path to model's `__init__.py`, i.e. `__file__`. ~~Union[str, Path]~~ |
| `vocab` <Tag variant="new">3</Tag> | Optional shared vocab to pass in on initialization. If `True` (default), a new `Vocab` object will be created. ~~Union[Vocab, bool]~~. |
| `disable` | Names of pipeline components to disable. ~~Iterable[str]~~ |
| `config` <Tag variant="new">3</Tag> | Config overrides as nested dict or flat dict keyed by section values in dot notation, e.g. `"nlp.pipeline"`. ~~Union[Dict[str, Any], Config]~~ |
| **RETURNS** | `Language` class with the loaded model. ~~Language~~ |
### util.get_model_meta {#util.get_model_meta tag="function" new="2"}
### util.load_config {#util.load_config tag="function" new="3"}
Get a model's meta.json from a directory path and validate its contents.
Load a model's [`config.cfg`](/api/data-formats#config) from a file path. The
config typically includes details about the model pipeline and how its
components are created, as well as all training settings and hyperparameters.
> #### Example
>
> ```python
> meta = util.get_model_meta("/path/to/model")
> config = util.load_config("/path/to/model/config.cfg")
> print(config.to_str())
> ```
| Name | Type | Description |
| ----------- | ------------ | ------------------------ |
| `path` | str / `Path` | Path to model directory. |
| **RETURNS** | dict | The model's meta data. |
| Name | Description |
| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `path` | Path to the model's `config.cfg`. ~~Union[str, Path]~~ |
| `overrides` | Optional config overrides to replace in loaded config. Can be provided as nested dict, or as flat dict with keys in dot notation, e.g. `"nlp.pipeline"`. ~~Dict[str, Any]~~ |
| `interpolate` | Whether to interpolate the config and replace variables like `${paths.train}` with their values. Defaults to `False`. ~~bool~~ |
| **RETURNS** | The model's config. ~~Config~~ |
### util.load_meta {#util.load_meta tag="function" new="3"}
Get a model's [`meta.json`](/api/data-formats#meta) from a file path and
validate its contents.
> #### Example
>
> ```python
> meta = util.load_meta("/path/to/model/meta.json")
> ```
| Name | Description |
| ----------- | ----------------------------------------------------- |
| `path` | Path to the model's `meta.json`. ~~Union[str, Path]~~ |
| **RETURNS** | The model's meta data. ~~Dict[str, Any]~~ |
### util.get_installed_models {#util.get_installed_models tag="function" new="3"}
List all model packages installed in the current environment. This will include
any spaCy model that was packaged with [`spacy package`](/api/cli#package).
Under the hood, model packages expose a Python entry point that spaCy can check,
without having to load the model.
> #### Example
>
> ```python
> model_names = util.get_installed_models()
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------- |
| **RETURNS** | The string names of the models installed in the current environment. ~~List[str]~~ |
### util.is_package {#util.is_package tag="function"}
@ -649,10 +661,10 @@ Check if string maps to a package installed via pip. Mainly used to validate
> util.is_package("xyz") # False
> ```
| Name | Type | Description |
| ----------- | ------ | -------------------------------------------- |
| `name` | str | Name of package. |
| **RETURNS** | `bool` | `True` if installed package, `False` if not. |
| Name | Description |
| ----------- | ----------------------------------------------------- |
| `name` | Name of package. ~~str~~ |
| **RETURNS** | `True` if installed package, `False` if not. ~~bool~~ |
### util.get_package_path {#util.get_package_path tag="function" new="2"}
@ -666,10 +678,10 @@ Get path to an installed package. Mainly used to resolve the location of
> # /usr/lib/python3.6/site-packages/en_core_web_sm
> ```
| Name | Type | Description |
| -------------- | ------ | -------------------------------- |
| `package_name` | str | Name of installed package. |
| **RETURNS** | `Path` | Path to model package directory. |
| Name | Description |
| -------------- | ----------------------------------------- |
| `package_name` | Name of installed package. ~~str~~ |
| **RETURNS** | Path to model package directory. ~~Path~~ |
### util.is_in_jupyter {#util.is_in_jupyter tag="function" new="2"}
@ -686,9 +698,9 @@ detecting the IPython kernel. Mainly used for the
> display(HTML(html))
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------- |
| **RETURNS** | bool | `True` if in Jupyter, `False` if not. |
| Name | Description |
| ----------- | ---------------------------------------------- |
| **RETURNS** | `True` if in Jupyter, `False` if not. ~~bool~~ |
### util.compile_prefix_regex {#util.compile_prefix_regex tag="function"}
@ -702,10 +714,10 @@ Compile a sequence of prefix rules into a regex object.
> nlp.tokenizer.prefix_search = prefix_regex.search
> ```
| Name | Type | Description |
| ----------- | ------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| `entries` | tuple | The prefix rules, e.g. [`lang.punctuation.TOKENIZER_PREFIXES`](https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py). |
| **RETURNS** | [regex](https://docs.python.org/3/library/re.html#re-objects) | The regex object. to be used for [`Tokenizer.prefix_search`](/api/tokenizer#attributes). |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `entries` | The prefix rules, e.g. [`lang.punctuation.TOKENIZER_PREFIXES`](https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py). ~~Iterable[Union[str, Pattern]]~~ |
| **RETURNS** | The regex object. to be used for [`Tokenizer.prefix_search`](/api/tokenizer#attributes). ~~Pattern~~ |
### util.compile_suffix_regex {#util.compile_suffix_regex tag="function"}
@ -719,10 +731,10 @@ Compile a sequence of suffix rules into a regex object.
> nlp.tokenizer.suffix_search = suffix_regex.search
> ```
| Name | Type | Description |
| ----------- | ------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| `entries` | tuple | The suffix rules, e.g. [`lang.punctuation.TOKENIZER_SUFFIXES`](https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py). |
| **RETURNS** | [regex](https://docs.python.org/3/library/re.html#re-objects) | The regex object. to be used for [`Tokenizer.suffix_search`](/api/tokenizer#attributes). |
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `entries` | The suffix rules, e.g. [`lang.punctuation.TOKENIZER_SUFFIXES`](https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py). ~~Iterable[Union[str, Pattern]]~~ |
| **RETURNS** | The regex object. to be used for [`Tokenizer.suffix_search`](/api/tokenizer#attributes). ~~Pattern~~ |
### util.compile_infix_regex {#util.compile_infix_regex tag="function"}
@ -736,10 +748,10 @@ Compile a sequence of infix rules into a regex object.
> nlp.tokenizer.infix_finditer = infix_regex.finditer
> ```
| Name | Type | Description |
| ----------- | ------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `entries` | tuple | The infix rules, e.g. [`lang.punctuation.TOKENIZER_INFIXES`](https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py). |
| **RETURNS** | [regex](https://docs.python.org/3/library/re.html#re-objects) | The regex object. to be used for [`Tokenizer.infix_finditer`](/api/tokenizer#attributes). |
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `entries` | The infix rules, e.g. [`lang.punctuation.TOKENIZER_INFIXES`](https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py). ~~Iterable[Union[str, Pattern]]~~ |
| **RETURNS** | The regex object. to be used for [`Tokenizer.infix_finditer`](/api/tokenizer#attributes). ~~Pattern~~ |
### util.minibatch {#util.minibatch tag="function" new="2"}
@ -754,11 +766,11 @@ vary on each step.
> nlp.update(batch)
> ```
| Name | Type | Description |
| ---------- | -------------- | ---------------------- |
| `items` | iterable | The items to batch up. |
| `size` | int / iterable | The batch size(s). |
| **YIELDS** | list | The batches. |
| Name | Description |
| ---------- | ---------------------------------------- |
| `items` | The items to batch up. ~~Iterable[Any]~~ |
| `size` | int / iterable | The batch size(s). ~~Union[int, Sequence[int]]~~ |
| **YIELDS** | The batches. |
### util.filter_spans {#util.filter_spans tag="function" new="2.1.4"}
@ -776,17 +788,30 @@ of one entity) or when merging spans with
> filtered = filter_spans(spans)
> ```
| Name | Type | Description |
| ----------- | -------- | -------------------- |
| `spans` | iterable | The spans to filter. |
| **RETURNS** | list | The filtered spans. |
| Name | Description |
| ----------- | --------------------------------------- |
| `spans` | The spans to filter. ~~Iterable[Span]~~ |
| **RETURNS** | The filtered spans. ~~List[Span]~~ |
### util.get_words_and_spaces {#get_words_and_spaces tag="function" new="3"}
<!-- TODO: document -->
Given a list of words and a text, reconstruct the original tokens and return a
list of words and spaces that can be used to create a [`Doc`](/api/doc#init).
This can help recover destructive tokenization that didn't preserve any
whitespace information.
| Name | Type | Description |
| ----------- | ----- | ----------- |
| `words` | list | |
| `text` | str | |
| **RETURNS** | tuple | |
> #### Example
>
> ```python
> orig_words = ["Hey", ",", "what", "'s", "up", "?"]
> orig_text = "Hey, what's up?"
> words, spaces = get_words_and_spaces(orig_words, orig_text)
> # ['Hey', ',', 'what', "'s", 'up', '?']
> # [False, True, False, True, False, False]
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `words` | The list of words. ~~Iterable[str]~~ |
| `text` | The original text. ~~str~~ |
| **RETURNS** | A list of words and a list of boolean values indicating whether the word at this position is followed by a space. ~~Tuple[List[str], List[bool]]~~ |

237
website/docs/api/transformer.md
View File

@ -41,7 +41,8 @@ token, the spaCy token receives the sum of their values. To access the values,
you can use the custom [`Doc._.trf_data`](#custom-attributes) attribute. The
package also adds the function registries [`@span_getters`](#span_getters) and
[`@annotation_setters`](#annotation_setters) with several built-in registered
functions. For more details, see the [usage documentation](/usage/transformers).
functions. For more details, see the
[usage documentation](/usage/embeddings-transformers).
## Config and implementation {#config}
@ -60,11 +61,11 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("transformer", config=DEFAULT_CONFIG)
> ```
| Setting | Type | Description | Default |
| ------------------- | ------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- |
| `max_batch_items` | int | Maximum size of a padded batch. | `4096` |
| `annotation_setter` | Callable | Function that takes a batch of `Doc` objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set additional annotations on the `Doc`. The `Doc._.transformer_data` attribute is set prior to calling the callback. By default, no additional annotations are set. | `null_annotation_setter` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** `List[Doc]`. **Output:** [`FullTransformerBatch`](/api/transformer#fulltransformerbatch). The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. | [TransformerModel](/api/architectures#TransformerModel) |
| Setting | Description |
| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `max_batch_items` | Maximum size of a padded batch. Defaults to `4096`. ~~int~~ |
| `annotation_setter` | Function that takes a batch of `Doc` objects and transformer outputs can set additional annotations on the `Doc`. The `Doc._.transformer_data` attribute is set prior to calling the callback. Defaults to `null_annotation_setter` (no additional annotations). ~~Callable[[List[Doc], FullTransformerBatch], None]~~ |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Defaults to [TransformerModel](/api/architectures#TransformerModel). ~~Model[List[Doc], FullTransformerBatch]~~ |
```python
https://github.com/explosion/spacy-transformers/blob/master/spacy_transformers/pipeline_component.py
@ -101,14 +102,14 @@ attribute. You can also provide a callback to set additional annotations. In
your application, you would normally use a shortcut for this and instantiate the
component using its string name and [`nlp.add_pipe`](/api/language#create_pipe).
| Name | Type | Description |
| ------------------- | ------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | **Input:** `List[Doc]`. **Output:** [`FullTransformerBatch`](/api/transformer#fulltransformerbatch). The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Usually you will want to use the [TransformerModel](/api/architectures#TransformerModel) layer for this. |
| `annotation_setter` | `Callable` | Function that takes a batch of `Doc` objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set additional annotations on the `Doc`. The `Doc._.transformer_data` attribute is set prior to calling the callback. By default, no additional annotations are set. |
| _keyword-only_ | | |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| `max_batch_items` | int | Maximum size of a padded batch. Defaults to `128*32`. |
| Name | Description |
| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Usually you will want to use the [TransformerModel](/api/architectures#TransformerModel) layer for this. ~~Model[List[Doc], FullTransformerBatch]~~ |
| `annotation_setter` | Function that takes a batch of `Doc` objects and transformer outputs can set additional annotations on the `Doc`. The `Doc._.transformer_data` attribute is set prior to calling the callback. By default, no annotations are set. ~~Callable[[List[Doc], FullTransformerBatch], None]~~ |
| _keyword-only_ | |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| `max_batch_items` | Maximum size of a padded batch. Defaults to `128*32`. ~~int~~ |
## Transformer.\_\_call\_\_ {#call tag="method"}
@ -128,10 +129,10 @@ to the [`predict`](/api/transformer#predict) and
> processed = transformer(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## Transformer.pipe {#pipe tag="method"}
@ -150,12 +151,12 @@ applied to the `Doc` in order. Both [`__call__`](/api/transformer#call) and
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------- |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | The processed documents in order. |
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## Transformer.begin_training {#begin_training tag="method"}
@ -175,13 +176,13 @@ setting up the label scheme based on the data.
> optimizer = trf.begin_training(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/transformer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Transformer.predict {#predict tag="method"}
@ -195,10 +196,10 @@ modifying them.
> scores = trf.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ----------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | - | The model's prediction for each document. |
| Name | Description |
| ----------- | ------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The model's prediction for each document. |
## Transformer.set_annotations {#set_annotations tag="method"}
@ -215,10 +216,10 @@ callback is then called, if provided.
> trf.set_annotations(docs, scores)
> ```
| Name | Type | Description |
| -------- | --------------- | ----------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `Transformer.predict`. |
| Name | Description |
| -------- | ----------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `Transformer.predict`. |
## Transformer.update {#update tag="method"}
@ -244,15 +245,15 @@ and call the optimizer, while the others simply increment the gradients.
> losses = trf.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects. Only the [`Example.predicted`](/api/example#predicted) `Doc` object is used, the reference `Doc` is ignored. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/transformer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
| Name | Description |
| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects. Only the [`Example.predicted`](/api/example#predicted) `Doc` object is used, the reference `Doc` is ignored. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `drop` | The dropout rate. ~~float~~ |
| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Transformer.create_optimizer {#create_optimizer tag="method"}
@ -265,9 +266,9 @@ Create an optimizer for the pipeline component.
> optimizer = trf.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## Transformer.use_params {#use_params tag="method, contextmanager"}
@ -282,9 +283,9 @@ context, the original parameters are restored.
> trf.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ----------------------------------------- |
| `params` | dict | The parameter values to use in the model. |
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## Transformer.to_disk {#to_disk tag="method"}
@ -297,11 +298,11 @@ Serialize the pipe to disk.
> trf.to_disk("/path/to/transformer")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Transformer.from_disk {#from_disk tag="method"}
@ -314,12 +315,12 @@ Load the pipe from disk. Modifies the object in place and returns it.
> trf.from_disk("/path/to/transformer")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tok2Vec` | The modified `Tok2Vec` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Transformer` object. ~~Transformer~~ |
## Transformer.to_bytes {#to_bytes tag="method"}
@ -332,11 +333,11 @@ Load the pipe from disk. Modifies the object in place and returns it.
Serialize the pipe to a bytestring.
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Tok2Vec` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `Transformer` object. ~~bytes~~ |
## Transformer.from_bytes {#from_bytes tag="method"}
@ -350,12 +351,12 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
> trf.from_bytes(trf_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tok2Vec` | The `Tok2Vec` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Transformer` object. ~~Transformer~~ |
## Serialization fields {#serialization-fields}
@ -386,20 +387,20 @@ by this class. Instances of this class
are`typically assigned to the [Doc._.trf_data`](/api/transformer#custom-attributes)
extension attribute.
| Name | Type | Description |
| --------- | -------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `tokens` | `Dict` | A slice of the tokens data produced by the tokenizer. This may have several fields, including the token IDs, the texts, and the attention mask. See the [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) object for details. |
| `tensors` | `List[FloatsXd]` | The activations for the Doc from the transformer. Usually the last tensor that is 3-dimensional will be the most important, as that will provide the final hidden state. Generally activations that are 2-dimensional will be attention weights. Details of this variable will differ depending on the underlying transformer model. |
| `align` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | Alignment from the `Doc`'s tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. |
| `width` | int | The width of the last hidden layer. |
| Name | Description |
| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tokens` | A slice of the tokens data produced by the tokenizer. This may have several fields, including the token IDs, the texts, and the attention mask. See the [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) object for details. ~~dict~~ |
| `tensors` | The activations for the Doc from the transformer. Usually the last tensor that is 3-dimensional will be the most important, as that will provide the final hidden state. Generally activations that are 2-dimensional will be attention weights. Details of this variable will differ depending on the underlying transformer model. ~~List[FloatsXd]~~ |
| `align` | Alignment from the `Doc`'s tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. ~~Ragged~~ |
| `width` | The width of the last hidden layer. ~~int~~ |
### TransformerData.empty {#transformerdata-emoty tag="classmethod"}
Create an empty `TransformerData` container.
| Name | Type | Description |
| ----------- | ----------------- | -------------- |
| **RETURNS** | `TransformerData` | The container. |
| Name | Description |
| ----------- | ---------------------------------- |
| **RETURNS** | The container. ~~TransformerData~~ |
## FullTransformerBatch {#fulltransformerbatch tag="dataclass"}
@ -407,13 +408,13 @@ Holds a batch of input and output objects for a transformer model. The data can
then be split to a list of [`TransformerData`](/api/transformer#transformerdata)
objects to associate the outputs to each [`Doc`](/api/doc) in the batch.
| Name | Type | Description |
| ---------- | -------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `spans` | `List[List[Span]]` | The batch of input spans. The outer list refers to the Doc objects in the batch, and the inner list are the spans for that `Doc`. Note that spans are allowed to overlap or exclude tokens, but each Span can only refer to one `Doc` (by definition). This means that within a `Doc`, the regions of the output tensors that correspond to each Span may overlap or have gaps, but for each `Doc`, there is a non-overlapping contiguous slice of the outputs. |
| `tokens` | [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) | The output of the tokenizer. |
| `tensors` | `List[torch.Tensor]` | The output of the transformer model. |
| `align` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | Alignment from the spaCy tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. |
| `doc_data` | `List[TransformerData]` | The outputs, split per `Doc` object. |
| Name | Description |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `spans` | The batch of input spans. The outer list refers to the Doc objects in the batch, and the inner list are the spans for that `Doc`. Note that spans are allowed to overlap or exclude tokens, but each Span can only refer to one `Doc` (by definition). This means that within a `Doc`, the regions of the output tensors that correspond to each Span may overlap or have gaps, but for each `Doc`, there is a non-overlapping contiguous slice of the outputs. ~~List[List[Span]]~~ |
| `tokens` | The output of the tokenizer. ~~transformers.BatchEncoding~~ |
| `tensors` | The output of the transformer model. ~~List[torch.Tensor]~~ |
| `align` | Alignment from the spaCy tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. ~~Ragged~~ |
| `doc_data` | The outputs, split per `Doc` object. ~~List[TransformerData]~~ |
### FullTransformerBatch.unsplit_by_doc {#fulltransformerbatch-unsplit_by_doc tag="method"}
@ -422,19 +423,19 @@ current object's spans, tokens and alignment. This is used during the backward
pass, in order to construct the gradients to pass back into the transformer
model.
| Name | Type | Description |
| ----------- | ---------------------- | ------------------------------- |
| `arrays` | `List[List[Floats3d]]` | The split batch of activations. |
| **RETURNS** | `FullTransformerBatch` | The transformer batch. |
| Name | Description |
| ----------- | -------------------------------------------------------- |
| `arrays` | The split batch of activations. ~~List[List[Floats3d]]~~ |
| **RETURNS** | The transformer batch. ~~FullTransformerBatch~~ |
### FullTransformerBatch.split_by_doc {#fulltransformerbatch-split_by_doc tag="method"}
Split a `TransformerData` object that represents a batch into a list with one
`TransformerData` per `Doc`.
| Name | Type | Description |
| ----------- | ----------------------- | ---------------- |
| **RETURNS** | `List[TransformerData]` | The split batch. |
| Name | Description |
| ----------- | ------------------------------------------ |
| **RETURNS** | The split batch. ~~List[TransformerData]~~ |
## Span getters {#span_getters source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/span_getters.py"}
@ -460,10 +461,10 @@ decorator.
> return get_sent_spans
> ```
| Name | Type | Description |
| ----------- | ------------------ | ---------------------------------------- |
| `docs` | `Iterable[Doc]` | A batch of `Doc` objects. |
| **RETURNS** | `List[List[Span]]` | The spans to process by the transformer. |
| Name | Description |
| ----------- | ------------------------------------------------------------- |
| `docs` | A batch of `Doc` objects. ~~Iterable[Doc]~~ |
| **RETURNS** | The spans to process by the transformer. ~~List[List[Span]]~~ |
### doc_spans.v1 {#doc_spans tag="registered function"}
@ -510,10 +511,10 @@ than `window` will allow for an overlap, so that some tokens are counted twice.
This can be desirable, because it allows all tokens to have both a left and
right context.
| Name | Type | Description |
| --------- | ---- | ---------------- |
|  `window` | int | The window size. |
| `stride` | int | The stride size. |
| Name | Description |
| -------- | ------------------------ |
| `window` | The window size. ~~int~~ |
| `stride` | The stride size. ~~int~~ |
## Annotation setters {#annotation_setters tag="registered functions" source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/annotation_setters.py"}
@ -526,7 +527,7 @@ You can register custom annotation setters using the
> #### Example
>
> ```python
> @registry.annotation_setters("spacy-transformer.null_annotation_setter.v1")
> @registry.annotation_setters("spacy-transformers.null_annotation_setter.v1")
> def configure_null_annotation_setter() -> Callable:
> def setter(docs: List[Doc], trf_data: FullTransformerBatch) -> None:
> pass
@ -534,22 +535,22 @@ You can register custom annotation setters using the
> return setter
> ```
| Name | Type | Description |
| ---------- | ---------------------- | ------------------------------------ |
| `docs` | `List[Doc]` | A batch of `Doc` objects. |
| `trf_data` | `FullTransformerBatch` | The transformers data for the batch. |
| Name | Description |
| ---------- | ------------------------------------------------------------- |
| `docs` | A batch of `Doc` objects. ~~List[Doc]~~ |
| `trf_data` | The transformers data for the batch. ~~FullTransformerBatch~~ |
The following built-in functions are available:
| Name | Description |
| --------------------------------------------- | ------------------------------------- |
| `spacy-transformer.null_annotation_setter.v1` | Don't set any additional annotations. |
| Name | Description |
| ---------------------------------------------- | ------------------------------------- |
| `spacy-transformers.null_annotation_setter.v1` | Don't set any additional annotations. |
## Custom attributes {#custom-attributes}
The component sets the following
[custom extension attributes](/usage/processing-pipeline#custom-components-attributes):
| Name | Type | Description |
| -------------- | ----------------------------------------------------- | ---------------------------------------------------- |
| `Doc.trf_data` | [`TransformerData`](/api/transformer#transformerdata) | Transformer tokens and outputs for the `Doc` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------ |
| `Doc.trf_data` | Transformer tokens and outputs for the `Doc` object. ~~TransformerData~~ |

186
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@ -30,13 +30,13 @@ you can add vectors to later.
> vectors = Vectors(data=data, keys=keys)
> ```
| Name | Type | Description |
| -------------- | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| _keyword-only_ | | |
| `shape` | tuple | Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. |
| `data` | `ndarray[ndim=1, dtype='float32']` | The vector data. |
| `keys` | iterable | A sequence of keys aligned with the data. |
| `name` | str | A name to identify the vectors table. |
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `shape` | Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. ~~Tuple[int, int]~~ |
| `data` | The vector data. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
| `keys` | A sequence of keys aligned with the data. ~~Iterable[Union[str, int]]~~ |
| `name` | A name to identify the vectors table. ~~str~~ |
## Vectors.\_\_getitem\_\_ {#getitem tag="method"}
@ -51,10 +51,10 @@ raised.
> assert cat_vector == nlp.vocab["cat"].vector
> ```
| Name | Type | Description |
| ------- | ---------------------------------- | ------------------------------ |
| `key` | int | The key to get the vector for. |
| returns | `ndarray[ndim=1, dtype='float32']` | The vector for the key. |
| Name | Description |
| ----------- | ---------------------------------------------------------------- |
| `key` | The key to get the vector for. ~~int~~ |
| **RETURNS** | The vector for the key. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vectors.\_\_setitem\_\_ {#setitem tag="method"}
@ -68,10 +68,10 @@ Set a vector for the given key.
> nlp.vocab.vectors[cat_id] = vector
> ```
| Name | Type | Description |
| -------- | ---------------------------------- | ------------------------------ |
| `key` | int | The key to set the vector for. |
| `vector` | `ndarray[ndim=1, dtype='float32']` | The vector to set. |
| Name | Description |
| -------- | ----------------------------------------------------------- |
| `key` | The key to set the vector for. ~~int~~ |
| `vector` | The vector to set. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vectors.\_\_iter\_\_ {#iter tag="method"}
@ -84,9 +84,9 @@ Iterate over the keys in the table.
> print(key, nlp.vocab.strings[key])
> ```
| Name | Type | Description |
| ---------- | ---- | ------------------- |
| **YIELDS** | int | A key in the table. |
| Name | Description |
| ---------- | --------------------------- |
| **YIELDS** | A key in the table. ~~int~~ |
## Vectors.\_\_len\_\_ {#len tag="method"}
@ -99,9 +99,9 @@ Return the number of vectors in the table.
> assert len(vectors) == 3
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------- |
| **RETURNS** | int | The number of vectors in the table. |
| Name | Description |
| ----------- | ------------------------------------------- |
| **RETURNS** | The number of vectors in the table. ~~int~~ |
## Vectors.\_\_contains\_\_ {#contains tag="method"}
@ -115,10 +115,10 @@ Check whether a key has been mapped to a vector entry in the table.
> assert cat_id in vectors
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------- |
| `key` | int | The key to check. |
| **RETURNS** | bool | Whether the key has a vector entry. |
| Name | Description |
| ----------- | -------------------------------------------- |
| `key` | The key to check. ~~int~~ |
| **RETURNS** | Whether the key has a vector entry. ~~bool~~ |
## Vectors.add {#add tag="method"}
@ -138,13 +138,13 @@ mapping separately. If you need to manage the strings, you should use the
> nlp.vocab.vectors.add("dog", row=0)
> ```
| Name | Type | Description |
| -------------- | ---------------------------------- | ----------------------------------------------------- |
| `key` | str / int | The key to add. |
| _keyword-only_ | | |
| `vector` | `ndarray[ndim=1, dtype='float32']` | An optional vector to add for the key. |
| `row` | int | An optional row number of a vector to map the key to. |
| **RETURNS** | int | The row the vector was added to. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------- |
| `key` | The key to add. ~~Union[str, int]~~ |
| _keyword-only_ | |
| `vector` | An optional vector to add for the key. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
| `row` | An optional row number of a vector to map the key to. ~~int~~ |
| **RETURNS** | The row the vector was added to. ~~int~~ |
## Vectors.resize {#resize tag="method"}
@ -160,11 +160,11 @@ These removed items are returned as a list of `(key, row)` tuples.
> removed = nlp.vocab.vectors.resize((10000, 300))
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------------------------------- |
| `shape` | tuple | A `(rows, dims)` tuple describing the number of rows and dimensions. |
| `inplace` | bool | Reallocate the memory. |
| **RETURNS** | list | The removed items as a list of `(key, row)` tuples. |
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------- |
| `shape` | A `(rows, dims)` tuple describing the number of rows and dimensions. ~~Tuple[int, int]~~ |
| `inplace` | Reallocate the memory. ~~bool~~ |
| **RETURNS** | The removed items as a list of `(key, row)` tuples. ~~List[Tuple[int, int]]~~ |
## Vectors.keys {#keys tag="method"}
@ -177,9 +177,9 @@ A sequence of the keys in the table.
> print(key, nlp.vocab.strings[key])
> ```
| Name | Type | Description |
| ----------- | -------- | ----------- |
| **RETURNS** | iterable | The keys. |
| Name | Description |
| ----------- | --------------------------- |
| **RETURNS** | The keys. ~~Iterable[int]~~ |
## Vectors.values {#values tag="method"}
@ -194,9 +194,9 @@ the length of the vectors table.
> print(vector)
> ```
| Name | Type | Description |
| ---------- | ---------------------------------- | ---------------------- |
| **YIELDS** | `ndarray[ndim=1, dtype='float32']` | A vector in the table. |
| Name | Description |
| ---------- | --------------------------------------------------------------- |
| **YIELDS** | A vector in the table. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vectors.items {#items tag="method"}
@ -209,9 +209,9 @@ Iterate over `(key, vector)` pairs, in order.
> print(key, nlp.vocab.strings[key], vector)
> ```
| Name | Type | Description |
| ---------- | ----- | -------------------------------- |
| **YIELDS** | tuple | `(key, vector)` pairs, in order. |
| Name | Description |
| ---------- | ------------------------------------------------------------------------------------- |
| **YIELDS** | `(key, vector)` pairs, in order. ~~Tuple[int, numpy.ndarray[ndim=1, dtype=float32]]~~ |
## Vectors.find {#find tag="method"}
@ -226,14 +226,14 @@ Look up one or more keys by row, or vice versa.
> keys = nlp.vocab.vectors.find(rows=[18, 256, 985])
> ```
| Name | Type | Description |
| -------------- | ------------------------------------- | ------------------------------------------------------------------------ |
| _keyword-only_ | | |
| `key` | str / int | Find the row that the given key points to. Returns int, `-1` if missing. |
| `keys` | iterable | Find rows that the keys point to. Returns `ndarray`. |
| `row` | int | Find the first key that points to the row. Returns int. |
| `rows` | iterable | Find the keys that point to the rows. Returns ndarray. |
| **RETURNS** | The requested key, keys, row or rows. |
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `key` | Find the row that the given key points to. Returns int, `-1` if missing. ~~Union[str, int]~~ |
| `keys` | Find rows that the keys point to. Returns `numpy.ndarray`. ~~Iterable[Union[str, int]]~~ |
| `row` | Find the first key that points to the row. Returns integer. ~~int~~ |
| `rows` | Find the keys that point to the rows. Returns `numpy.ndarray`. ~~Iterable[int]~~ |
| **RETURNS** | The requested key, keys, row or rows. ~~Union[int, numpy.ndarray[ndim=1, dtype=float32]]~~ |
## Vectors.shape {#shape tag="property"}
@ -250,9 +250,9 @@ vector table.
> assert dims == 300
> ```
| Name | Type | Description |
| ----------- | ----- | ---------------------- |
| **RETURNS** | tuple | A `(rows, dims)` pair. |
| Name | Description |
| ----------- | ------------------------------------------ |
| **RETURNS** | A `(rows, dims)` pair. ~~Tuple[int, int]~~ |
## Vectors.size {#size tag="property"}
@ -265,9 +265,9 @@ The vector size, i.e. `rows * dims`.
> assert vectors.size == 150000
> ```
| Name | Type | Description |
| ----------- | ---- | ---------------- |
| **RETURNS** | int | The vector size. |
| Name | Description |
| ----------- | ------------------------ |
| **RETURNS** | The vector size. ~~int~~ |
## Vectors.is_full {#is_full tag="property"}
@ -283,9 +283,9 @@ If a table is full, it can be resized using
> assert vectors.is_full
> ```
| Name | Type | Description |
| ----------- | ---- | ---------------------------------- |
| **RETURNS** | bool | Whether the vectors table is full. |
| Name | Description |
| ----------- | ------------------------------------------- |
| **RETURNS** | Whether the vectors table is full. ~~bool~~ |
## Vectors.n_keys {#n_keys tag="property"}
@ -301,9 +301,9 @@ vectors, they will be counted individually.
> assert vectors.n_keys == 0
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------ |
| **RETURNS** | int | The number of all keys in the table. |
| Name | Description |
| ----------- | -------------------------------------------- |
| **RETURNS** | The number of all keys in the table. ~~int~~ |
## Vectors.most_similar {#most_similar tag="method"}
@ -320,14 +320,14 @@ performed in chunks, to avoid consuming too much memory. You can set the
> most_similar = nlp.vocab.vectors.most_similar(queries, n=10)
> ```
| Name | Type | Description |
| -------------- | --------- | ------------------------------------------------------------------ |
| `queries` | `ndarray` | An array with one or more vectors. |
| _keyword-only_ | | |
| `batch_size` | int | The batch size to use. Default to `1024`. |
| `n` | int | The number of entries to return for each query. Defaults to `1`. |
| `sort` | bool | Whether to sort the entries returned by score. Defaults to `True`. |
| **RETURNS** | tuple | The most similar entries as a `(keys, best_rows, scores)` tuple. |
| Name | Description |
| -------------- | --------------------------------------------------------------------------- |
| `queries` | An array with one or more vectors. ~~numpy.ndarray~~ |
| _keyword-only_ | |
| `batch_size` | The batch size to use. Default to `1024`. ~~int~~ |
| `n` | The number of entries to return for each query. Defaults to `1`. ~~int~~ |
| `sort` | Whether to sort the entries returned by score. Defaults to `True`. ~~bool~~ |
| **RETURNS** | tuple | The most similar entries as a `(keys, best_rows, scores)` tuple. ~~Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]~~ |
## Vectors.to_disk {#to_disk tag="method"}
@ -340,9 +340,9 @@ Save the current state to a directory.
>
> ```
| Name | Type | Description |
| ------ | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| Name | Description |
| ------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
## Vectors.from_disk {#from_disk tag="method"}
@ -355,10 +355,10 @@ Loads state from a directory. Modifies the object in place and returns it.
> vectors.from_disk("/path/to/vectors")
> ```
| Name | Type | Description |
| ----------- | ------------ | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| **RETURNS** | `Vectors` | The modified `Vectors` object. |
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| **RETURNS** | The modified `Vectors` object. ~~Vectors~~ |
## Vectors.to_bytes {#to_bytes tag="method"}
@ -370,9 +370,9 @@ Serialize the current state to a binary string.
> vectors_bytes = vectors.to_bytes()
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------- |
| **RETURNS** | bytes | The serialized form of the `Vectors` object. |
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | The serialized form of the `Vectors` object. ~~bytes~~ |
## Vectors.from_bytes {#from_bytes tag="method"}
@ -387,15 +387,15 @@ Load state from a binary string.
> new_vectors.from_bytes(vectors_bytes)
> ```
| Name | Type | Description |
| ----------- | --------- | ---------------------- |
| `data` | bytes | The data to load from. |
| **RETURNS** | `Vectors` | The `Vectors` object. |
| Name | Description |
| ----------- | --------------------------------- |
| `data` | The data to load from. ~~bytes~~ |
| **RETURNS** | The `Vectors` object. ~~Vectors~~ |
## Attributes {#attributes}
| Name | Type | Description |
| --------- | ---------------------------------- | ------------------------------------------------------------------------------- |
| `data` | `ndarray[ndim=1, dtype='float32']` | Stored vectors data. `numpy` is used for CPU vectors, `cupy` for GPU vectors. |
| `key2row` | dict | Dictionary mapping word hashes to rows in the `Vectors.data` table. |
| `keys` | `ndarray[ndim=1, dtype='float32']` | Array keeping the keys in order, such that `keys[vectors.key2row[key]] == key`. |
| Name | Description |
| --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `data` | Stored vectors data. `numpy` is used for CPU vectors, `cupy` for GPU vectors. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
| `key2row` | Dictionary mapping word hashes to rows in the `Vectors.data` table. ~~Dict[int, int]~~ |
| `keys` | Array keeping the keys in order, such that `keys[vectors.key2row[key]] == key`. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |

161
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@ -21,14 +21,15 @@ Create the vocabulary.
> vocab = Vocab(strings=["hello", "world"])
> ```
| Name | Type | Description |
| -------------------------------------------- | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lex_attr_getters` | dict | A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. |
| `strings` | `StringStore` / list | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings. |
| `lookups` | `Lookups` | A [`Lookups`](/api/lookups) that stores the `lemma_\*`, `lexeme_norm` and other large lookup tables. Defaults to `None`. |
| `lookups_extra` <Tag variant="new">2.3</Tag> | `Lookups` | A [`Lookups`](/api/lookups) that stores the optional `lexeme_cluster`/`lexeme_prob`/`lexeme_sentiment`/`lexeme_settings` lookup tables. Defaults to `None`. |
| `oov_prob` | float | The default OOV probability. Defaults to `-20.0`. |
| `vectors_name` <Tag variant="new">2.2</Tag> | str | A name to identify the vectors table. |
| Name | Description |
| ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lex_attr_getters` | A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. ~~Optional[Dict[str, Callable[[str], Any]]]~~ |
| `strings` | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings. ~~Union[List[str], StringStore]~~ |
| `lookups` | A [`Lookups`](/api/lookups) that stores the `lexeme_norm` and other large lookup tables. Defaults to `None`. ~~Optional[Lookups]~~ |
| `oov_prob` | The default OOV probability. Defaults to `-20.0`. ~~float~~ |
| `vectors_name` <Tag variant="new">2.2</Tag> | A name to identify the vectors table. ~~str~~ |
| `writing_system` | A dictionary describing the language's writing system. Typically provided by [`Language.Defaults`](/api/language#defaults). ~~Dict[str, Any]~~ |
| `get_noun_chunks` | A function that yields base noun phrases, used for [`Doc.noun_chunks`](/ap/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Span]]]]~~ |
## Vocab.\_\_len\_\_ {#len tag="method"}
@ -41,9 +42,9 @@ Get the current number of lexemes in the vocabulary.
> assert len(nlp.vocab) > 0
> ```
| Name | Type | Description |
| ----------- | ---- | ---------------------------------------- |
| **RETURNS** | int | The number of lexemes in the vocabulary. |
| Name | Description |
| ----------- | ------------------------------------------------ |
| **RETURNS** | The number of lexemes in the vocabulary. ~~int~~ |
## Vocab.\_\_getitem\_\_ {#getitem tag="method"}
@ -57,10 +58,10 @@ given, a new lexeme is created and stored.
> assert nlp.vocab[apple] == nlp.vocab["apple"]
> ```
| Name | Type | Description |
| -------------- | --------- | ---------------------------------------- |
| `id_or_string` | int / str | The hash value of a word, or its string. |
| **RETURNS** | `Lexeme` | The lexeme indicated by the given ID. |
| Name | Description |
| -------------- | ------------------------------------------------------------ |
| `id_or_string` | The hash value of a word, or its string. ~~Union[int, str]~~ |
| **RETURNS** | The lexeme indicated by the given ID. ~~Lexeme~~ |
## Vocab.\_\_iter\_\_ {#iter tag="method"}
@ -72,9 +73,9 @@ Iterate over the lexemes in the vocabulary.
> stop_words = (lex for lex in nlp.vocab if lex.is_stop)
> ```
| Name | Type | Description |
| ---------- | -------- | --------------------------- |
| **YIELDS** | `Lexeme` | An entry in the vocabulary. |
| Name | Description |
| ---------- | -------------------------------------- |
| **YIELDS** | An entry in the vocabulary. ~~Lexeme~~ |
## Vocab.\_\_contains\_\_ {#contains tag="method"}
@ -91,10 +92,10 @@ given string, you need to look it up in
> assert oov not in nlp.vocab
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------------------- |
| `string` | str | The ID string. |
| **RETURNS** | bool | Whether the string has an entry in the vocabulary. |
| Name | Description |
| ----------- | ----------------------------------------------------------- |
| `string` | The ID string. ~~str~~ |
| **RETURNS** | Whether the string has an entry in the vocabulary. ~~bool~~ |
## Vocab.add_flag {#add_flag tag="method"}
@ -115,11 +116,11 @@ using `token.check_flag(flag_id)`.
> assert doc[2].check_flag(MY_PRODUCT) == True
> ```
| Name | Type | Description |
| ------------- | ---- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| `flag_getter` | dict | A function `f(str) -> bool`, to get the flag value. |
| `flag_id` | int | An integer between 1 and 63 (inclusive), specifying the bit at which the flag will be stored. If `-1`, the lowest available bit will be chosen. |
| **RETURNS** | int | The integer ID by which the flag value can be checked. |
| Name | Description |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `flag_getter` | A function that takes the lexeme text and returns the boolean flag value. ~~Callable[[str], bool]~~ |
| `flag_id` | An integer between `1` and `63` (inclusive), specifying the bit at which the flag will be stored. If `-1`, the lowest available bit will be chosen. ~~int~~ |
| **RETURNS** | The integer ID by which the flag value can be checked. ~~int~~ |
## Vocab.reset_vectors {#reset_vectors tag="method" new="2"}
@ -133,11 +134,11 @@ have to call this to change the size of the vectors. Only one of the `width` and
> nlp.vocab.reset_vectors(width=300)
> ```
| Name | Type | Description |
| -------------- | ---- | -------------------------------------- |
| _keyword-only_ | | |
| `width` | int | The new width (keyword argument only). |
| `shape` | int | The new shape (keyword argument only). |
| Name | Description |
| -------------- | ---------------------- |
| _keyword-only_ | |
| `width` | The new width. ~~int~~ |
| `shape` | The new shape. ~~int~~ |
## Vocab.prune_vectors {#prune_vectors tag="method" new="2"}
@ -158,11 +159,11 @@ cosines are calculated in minibatches, to reduce memory usage.
> assert len(nlp.vocab.vectors) <= 1000
> ```
| Name | Type | Description |
| ------------ | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nr_row` | int | The number of rows to keep in the vector table. |
| `batch_size` | int | Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory. |
| **RETURNS** | dict | A dictionary keyed by removed words mapped to `(string, score)` tuples, where `string` is the entry the removed word was mapped to, and `score` the similarity score between the two words. |
| Name | Description |
| ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nr_row` | The number of rows to keep in the vector table. ~~int~~ |
| `batch_size` | Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory. ~~int~~ |
| **RETURNS** | A dictionary keyed by removed words mapped to `(string, score)` tuples, where `string` is the entry the removed word was mapped to, and `score` the similarity score between the two words. ~~Dict[str, Tuple[str, float]]~~ |
## Vocab.get_vector {#get_vector tag="method" new="2"}
@ -178,12 +179,12 @@ subword features by average over ngrams of `orth` (introduced in spaCy `v2.1`).
> nlp.vocab.get_vector("apple", minn=1, maxn=5)
> ```
| Name | Type | Description |
| ----------------------------------- | ---------------------------------------- | ---------------------------------------------------------------------------------------------- |
| `orth` | int / str | The hash value of a word, or its unicode string. |
| `minn` <Tag variant="new">2.1</Tag> | int | Minimum n-gram length used for FastText's ngram computation. Defaults to the length of `orth`. |
| `maxn` <Tag variant="new">2.1</Tag> | int | Maximum n-gram length used for FastText's ngram computation. Defaults to the length of `orth`. |
| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A word vector. Size and shape are determined by the `Vocab.vectors` instance. |
| Name | Description |
| ----------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
| `orth` | The hash value of a word, or its unicode string. ~~Union[int, str]~~ |
| `minn` <Tag variant="new">2.1</Tag> | Minimum n-gram length used for FastText's ngram computation. Defaults to the length of `orth`. ~~int~~ |
| `maxn` <Tag variant="new">2.1</Tag> | Maximum n-gram length used for FastText's ngram computation. Defaults to the length of `orth`. ~~int~~ |
| **RETURNS** | A word vector. Size and shape are determined by the `Vocab.vectors` instance. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vocab.set_vector {#set_vector tag="method" new="2"}
@ -196,10 +197,10 @@ or hash value.
> nlp.vocab.set_vector("apple", array([...]))
> ```
| Name | Type | Description |
| -------- | ---------------------------------------- | ------------------------------------------------ |
| `orth` | int / str | The hash value of a word, or its unicode string. |
| `vector` | `numpy.ndarray[ndim=1, dtype='float32']` | The vector to set. |
| Name | Description |
| -------- | -------------------------------------------------------------------- |
| `orth` | The hash value of a word, or its unicode string. ~~Union[int, str]~~ |
| `vector` | The vector to set. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vocab.has_vector {#has_vector tag="method" new="2"}
@ -213,10 +214,10 @@ Words can be looked up by string or hash value.
> vector = nlp.vocab.get_vector("apple")
> ```
| Name | Type | Description |
| ----------- | --------- | ------------------------------------------------ |
| `orth` | int / str | The hash value of a word, or its unicode string. |
| **RETURNS** | bool | Whether the word has a vector. |
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `orth` | The hash value of a word, or its unicode string. ~~Union[int, str]~~ |
| **RETURNS** | Whether the word has a vector. ~~bool~~ |
## Vocab.to_disk {#to_disk tag="method" new="2"}
@ -228,11 +229,11 @@ Save the current state to a directory.
> nlp.vocab.to_disk("/path/to/vocab")
> ```
| Name | Type | Description |
| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Vocab.from_disk {#from_disk tag="method" new="2"}
@ -245,12 +246,12 @@ Loads state from a directory. Modifies the object in place and returns it.
> vocab = Vocab().from_disk("/path/to/vocab")
> ```
| Name | Type | Description |
| -------------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Vocab` | The modified `Vocab` object. |
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Vocab` object. ~~Vocab~~ |
## Vocab.to_bytes {#to_bytes tag="method"}
@ -262,11 +263,11 @@ Serialize the current state to a binary string.
> vocab_bytes = nlp.vocab.to_bytes()
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Vocab` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `Vocab` object. ~~Vocab~~ |
## Vocab.from_bytes {#from_bytes tag="method"}
@ -281,12 +282,12 @@ Load state from a binary string.
> vocab.from_bytes(vocab_bytes)
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| _keyword-only_ | | |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Vocab` | The `Vocab` object. |
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Vocab` object. ~~Vocab~~ |
## Attributes {#attributes}
@ -299,13 +300,13 @@ Load state from a binary string.
> assert type(PERSON) == int
> ```
| Name | Type | Description |
| --------------------------------------------- | ------------- | ------------------------------------------------------------ |
| `strings` | `StringStore` | A table managing the string-to-int mapping. |
| `vectors` <Tag variant="new">2</Tag> | `Vectors` | A table associating word IDs to word vectors. |
| `vectors_length` | int | Number of dimensions for each word vector. |
| `lookups` | `Lookups` | The available lookup tables in this vocab. |
| `writing_system` <Tag variant="new">2.1</Tag> | dict | A dict with information about the language's writing system. |
| Name | Description |
| --------------------------------------------- | ------------------------------------------------------------------------------- |
| `strings` | A table managing the string-to-int mapping. ~~StringStore~~ |
| `vectors` <Tag variant="new">2</Tag> | A table associating word IDs to word vectors. ~~Vectors~~ |
| `vectors_length` | Number of dimensions for each word vector. ~~int~~ |
| `lookups` | The available lookup tables in this vocab. ~~Lookups~~ |
| `writing_system` <Tag variant="new">2.1</Tag> | A dict with information about the language's writing system. ~~Dict[str, Any]~~ |
## Serialization fields {#serialization-fields}

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