mirror of
https://github.com/explosion/spaCy.git
synced 2025-08-27 23:45:01 +03:00
Merge branch 'master' into feature/projects-multiprocessing
This commit is contained in:
Richard Hudson
GitHub
commit
5aa95ce499
2
.github/ISSUE_TEMPLATE/01_bugs.md
vendored
2
.github/ISSUE_TEMPLATE/01_bugs.md
vendored
|
|
@ -10,7 +10,7 @@ about: Use this template if you came across a bug or unexpected behaviour differ
|
|||
<!-- Include a code example or the steps that led to the problem. Please try to be as specific as possible. -->
|
||||
|
||||
## Your Environment
|
||||
<!-- Include details of your environment. If you're using spaCy 1.7+, you can also type `python -m spacy info --markdown` and copy-paste the result here.-->
|
||||
<!-- Include details of your environment. You can also type `python -m spacy info --markdown` and copy-paste the result here.-->
|
||||
* Operating System:
|
||||
* Python Version Used:
|
||||
* spaCy Version Used:
|
||||
|
|
|
|||
39
.github/azure-steps.yml
vendored
39
.github/azure-steps.yml
vendored
|
|
@ -27,6 +27,7 @@ steps:
|
|||
|
||||
- script: python -m mypy spacy
|
||||
displayName: 'Run mypy'
|
||||
condition: ne(variables['python_version'], '3.6')
|
||||
|
||||
- task: DeleteFiles@1
|
||||
inputs:
|
||||
|
|
@ -54,21 +55,21 @@ steps:
|
|||
condition: eq(${{ parameters.gpu }}, true)
|
||||
|
||||
- script: |
|
||||
${{ parameters.prefix }} python -m pytest --pyargs spacy
|
||||
${{ parameters.prefix }} python -m pytest --pyargs spacy -W error
|
||||
displayName: "Run CPU tests"
|
||||
condition: eq(${{ parameters.gpu }}, false)
|
||||
|
||||
- script: |
|
||||
${{ parameters.prefix }} python -m pytest --pyargs spacy -p spacy.tests.enable_gpu
|
||||
${{ parameters.prefix }} python -m pytest --pyargs spacy -W error -p spacy.tests.enable_gpu
|
||||
displayName: "Run GPU tests"
|
||||
condition: eq(${{ parameters.gpu }}, true)
|
||||
|
||||
# - script: |
|
||||
# python -m spacy download ca_core_news_sm
|
||||
# python -m spacy download ca_core_news_md
|
||||
# python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')"
|
||||
# displayName: 'Test download CLI'
|
||||
# condition: eq(variables['python_version'], '3.8')
|
||||
- script: |
|
||||
python -m spacy download ca_core_news_sm
|
||||
python -m spacy download ca_core_news_md
|
||||
python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')"
|
||||
displayName: 'Test download CLI'
|
||||
condition: eq(variables['python_version'], '3.8')
|
||||
|
||||
- script: |
|
||||
python -m spacy convert extra/example_data/ner_example_data/ner-token-per-line-conll2003.json .
|
||||
|
|
@ -92,17 +93,17 @@ steps:
|
|||
displayName: 'Test train CLI'
|
||||
condition: eq(variables['python_version'], '3.8')
|
||||
|
||||
# - script: |
|
||||
# python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
|
||||
# PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
|
||||
# displayName: 'Test assemble CLI'
|
||||
# condition: eq(variables['python_version'], '3.8')
|
||||
#
|
||||
# - script: |
|
||||
# python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
|
||||
# python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
|
||||
# displayName: 'Test assemble CLI vectors warning'
|
||||
# condition: eq(variables['python_version'], '3.8')
|
||||
- script: |
|
||||
python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
|
||||
PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
|
||||
displayName: 'Test assemble CLI'
|
||||
condition: eq(variables['python_version'], '3.8')
|
||||
|
||||
- script: |
|
||||
python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
|
||||
python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
|
||||
displayName: 'Test assemble CLI vectors warning'
|
||||
condition: eq(variables['python_version'], '3.8')
|
||||
|
||||
- script: |
|
||||
python .github/validate_universe_json.py website/meta/universe.json
|
||||
|
|
|
|||
13
.github/no-response.yml
vendored
13
.github/no-response.yml
vendored
|
|
@ -1,13 +0,0 @@
|
|||
# Configuration for probot-no-response - https://github.com/probot/no-response
|
||||
|
||||
# Number of days of inactivity before an Issue is closed for lack of response
|
||||
daysUntilClose: 14
|
||||
# Label requiring a response
|
||||
responseRequiredLabel: more-info-needed
|
||||
# Comment to post when closing an Issue for lack of response. Set to `false` to disable
|
||||
closeComment: >
|
||||
This issue has been automatically closed because there has been no response
|
||||
to a request for more information from the original author. With only the
|
||||
information that is currently in the issue, there's not enough information
|
||||
to take action. If you're the original author, feel free to reopen the issue
|
||||
if you have or find the answers needed to investigate further.
|
||||
8
.github/workflows/issue-manager.yml
vendored
8
.github/workflows/issue-manager.yml
vendored
|
|
@ -15,7 +15,7 @@ jobs:
|
|||
issue-manager:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: tiangolo/issue-manager@0.2.1
|
||||
- uses: tiangolo/issue-manager@0.4.0
|
||||
with:
|
||||
token: ${{ secrets.GITHUB_TOKEN }}
|
||||
config: >
|
||||
|
|
@ -25,5 +25,11 @@ jobs:
|
|||
"message": "This issue has been automatically closed because it was answered and there was no follow-up discussion.",
|
||||
"remove_label_on_comment": true,
|
||||
"remove_label_on_close": true
|
||||
},
|
||||
"more-info-needed": {
|
||||
"delay": "P7D",
|
||||
"message": "This issue has been automatically closed because there has been no response to a request for more information from the original author. With only the information that is currently in the issue, there's not enough information to take action. If you're the original author, feel free to reopen the issue if you have or find the answers needed to investigate further.",
|
||||
"remove_label_on_comment": true,
|
||||
"remove_label_on_close": true
|
||||
}
|
||||
}
|
||||
|
|
|
|||
1
.gitignore
vendored
1
.gitignore
vendored
|
|
@ -24,6 +24,7 @@ quickstart-training-generator.js
|
|||
cythonize.json
|
||||
spacy/*.html
|
||||
*.cpp
|
||||
*.c
|
||||
*.so
|
||||
|
||||
# Vim / VSCode / editors
|
||||
|
|
|
|||
|
|
@ -271,7 +271,8 @@ except: # noqa: E722
|
|||
|
||||
### Python conventions
|
||||
|
||||
All Python code must be written **compatible with Python 3.6+**.
|
||||
All Python code must be written **compatible with Python 3.6+**. More detailed
|
||||
code conventions can be found in the [developer docs](https://github.com/explosion/spaCy/blob/master/extra/DEVELOPER_DOCS/Code%20Conventions.md).
|
||||
|
||||
#### I/O and handling paths
|
||||
|
||||
|
|
|
|||
|
|
@ -31,8 +31,8 @@ jobs:
|
|||
inputs:
|
||||
versionSpec: "3.7"
|
||||
- script: |
|
||||
pip install flake8==3.9.2
|
||||
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823 --show-source --statistics
|
||||
pip install flake8==5.0.4
|
||||
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
|
||||
displayName: "flake8"
|
||||
|
||||
- job: "Test"
|
||||
|
|
|
|||
|
|
@ -191,6 +191,8 @@ def load_model(name: str) -> "Language":
|
|||
...
|
||||
```
|
||||
|
||||
Note that we typically put the `from typing` import statements on the first line(s) of the Python module.
|
||||
|
||||
## Structuring logic
|
||||
|
||||
### Positional and keyword arguments
|
||||
|
|
@ -275,6 +277,27 @@ If you have to use `try`/`except`, make sure to only include what's **absolutely
|
|||
+ return [v.strip() for v in value.split(",")]
|
||||
```
|
||||
|
||||
### Numeric comparisons
|
||||
|
||||
For numeric comparisons, as a general rule we always use `<` and `>=` and avoid the usage of `<=` and `>`. This is to ensure we consistently
|
||||
apply inclusive lower bounds and exclusive upper bounds, helping to prevent off-by-one errors.
|
||||
|
||||
One exception to this rule is the ternary case. With a chain like
|
||||
|
||||
```python
|
||||
if value >= 0 and value < max:
|
||||
...
|
||||
```
|
||||
|
||||
it's fine to rewrite this to the shorter form
|
||||
|
||||
```python
|
||||
if 0 <= value < max:
|
||||
...
|
||||
```
|
||||
|
||||
even though this requires the usage of the `<=` operator.
|
||||
|
||||
### Iteration and comprehensions
|
||||
|
||||
We generally avoid using built-in functions like `filter` or `map` in favor of list or generator comprehensions.
|
||||
|
|
@ -451,7 +474,7 @@ spaCy uses the [`pytest`](http://doc.pytest.org/) framework for testing. Tests f
|
|||
|
||||
When adding tests, make sure to use descriptive names and only test for one behavior at a time. Tests should be grouped into modules dedicated to the same type of functionality and some test modules are organized as directories of test files related to the same larger area of the library, e.g. `matcher` or `tokenizer`.
|
||||
|
||||
Regression tests are tests that refer to bugs reported in specific issues. They should live in the relevant module of the test suite, named according to the issue number (e.g., `test_issue1234.py`), and [marked](https://docs.pytest.org/en/6.2.x/example/markers.html#working-with-custom-markers) appropriately (e.g. `@pytest.mark.issue(1234)`). This system allows us to relate tests for specific bugs back to the original reported issue, which is especially useful if we introduce a regression and a previously passing regression tests suddenly fails again. When fixing a bug, it's often useful to create a regression test for it first.
|
||||
Regression tests are tests that refer to bugs reported in specific issues. They should live in the relevant module of the test suite, named according to the issue number (e.g., `test_issue1234.py`), and [marked](https://docs.pytest.org/en/6.2.x/example/markers.html#working-with-custom-markers) appropriately (e.g. `@pytest.mark.issue(1234)`). This system allows us to relate tests for specific bugs back to the original reported issue, which is especially useful if we introduce a regression and a previously passing regression tests suddenly fails again. When fixing a bug, it's often useful to create a regression test for it first.
|
||||
|
||||
The test suite also provides [fixtures](https://github.com/explosion/spaCy/blob/master/spacy/tests/conftest.py) for different language tokenizers that can be used as function arguments of the same name and will be passed in automatically. Those should only be used for tests related to those specific languages. We also have [test utility functions](https://github.com/explosion/spaCy/blob/master/spacy/tests/util.py) for common operations, like creating a temporary file.
|
||||
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@ Some things to note:
|
|||
@explosion-bot please test_gpu --run-on spacy-transformers --run-on-branch master --spacy-branch current_pr
|
||||
```
|
||||
|
||||
This will launch the GPU pipeline for the `spacy-transformers` repo on its `master` branch, using the current spaCy PR's branch to build spaCy.
|
||||
This will launch the GPU pipeline for the `spacy-transformers` repo on its `master` branch, using the current spaCy PR's branch to build spaCy. The name of the repository passed to `--run-on` is case-sensitive, e.g: use `spaCy` instead of `spacy`.
|
||||
|
||||
- General info about supported commands.
|
||||
|
||||
|
|
|
|||
82
extra/DEVELOPER_DOCS/Satellite Packages.md
Normal file
82
extra/DEVELOPER_DOCS/Satellite Packages.md
Normal file
|
|
@ -0,0 +1,82 @@
|
|||
# spaCy Satellite Packages
|
||||
|
||||
This is a list of all the active repos relevant to spaCy besides the main one, with short descriptions, history, and current status. Archived repos will not be covered.
|
||||
|
||||
## Always Included in spaCy
|
||||
|
||||
These packages are always pulled in when you install spaCy. Most of them are direct dependencies, but some are transitive dependencies through other packages.
|
||||
|
||||
- [spacy-legacy](https://github.com/explosion/spacy-legacy): When an architecture in spaCy changes enough to get a new version, the old version is frozen and moved to spacy-legacy. This allows us to keep the core library slim while also preserving backwards compatability.
|
||||
- [thinc](https://github.com/explosion/thinc): Thinc is the machine learning library that powers trainable components in spaCy. It wraps backends like Numpy, PyTorch, and Tensorflow to provide a functional interface for specifying architectures.
|
||||
- [catalogue](https://github.com/explosion/catalogue): Small library for adding function registries, like those used for model architectures in spaCy.
|
||||
- [confection](https://github.com/explosion/confection): This library contains the functionality for config parsing that was formerly contained directly in Thinc.
|
||||
- [spacy-loggers](https://github.com/explosion/spacy-loggers): Contains loggers beyond the default logger available in spaCy's core code base. This includes loggers integrated with third-party services, which may differ in release cadence from spaCy itself.
|
||||
- [wasabi](https://github.com/explosion/wasabi): A command line formatting library, used for terminal output in spaCy.
|
||||
- [srsly](https://github.com/explosion/srsly): A wrapper that vendors several serialization libraries for spaCy. Includes parsers for JSON, JSONL, MessagePack, (extended) Pickle, and YAML.
|
||||
- [preshed](https://github.com/explosion/preshed): A Cython library for low-level data structures like hash maps, used for memory efficient data storage.
|
||||
- [cython-blis](https://github.com/explosion/cython-blis): Fast matrix multiplication using BLIS without depending on system libraries. Required by Thinc, rather than spaCy directly.
|
||||
- [murmurhash](https://github.com/explosion/murmurhash): A wrapper library for a C++ murmurhash implementation, used for string IDs in spaCy and preshed.
|
||||
- [cymem](https://github.com/explosion/cymem): A small library for RAII-style memory management in Cython.
|
||||
|
||||
## Optional Extensions for spaCy
|
||||
|
||||
These are repos that can be used by spaCy but aren't part of a default installation. Many of these are wrappers to integrate various kinds of third-party libraries.
|
||||
|
||||
- [spacy-transformers](https://github.com/explosion/spacy-transformers): A wrapper for the [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) library, this handles the extensive conversion necessary to coordinate spaCy's powerful `Doc` representation, training pipeline, and the Transformer embeddings. When released, this was known as `spacy-pytorch-transformers`, but it changed to the current name when HuggingFace update the name of their library as well.
|
||||
- [spacy-huggingface-hub](https://github.com/explosion/spacy-huggingface-hub): This package has a CLI script for uploading a packaged spaCy pipeline (created with `spacy package`) to the [Hugging Face Hub](https://huggingface.co/models).
|
||||
- [spacy-alignments](https://github.com/explosion/spacy-alignments): A wrapper for the tokenizations library (mentioned below) with a modified build system to simplify cross-platform wheel creation. Used in spacy-transformers for aligning spaCy and HuggingFace tokenizations.
|
||||
- [spacy-experimental](https://github.com/explosion/spacy-experimental): Experimental components that are not quite ready for inclusion in the main spaCy library. Usually there are unresolved questions around their APIs, so the experimental library allows us to expose them to the community for feedback before fully integrating them.
|
||||
- [spacy-lookups-data](https://github.com/explosion/spacy-lookups-data): A repository of linguistic data, such as lemmas, that takes up a lot of disk space. Originally created to reduce the size of the spaCy core library. This is mainly useful if you want the data included but aren't using a pretrained pipeline; for the affected languages, the relevant data is included in pretrained pipelines directly.
|
||||
- [coreferee](https://github.com/explosion/coreferee): Coreference resolution for English, French, German and Polish, optimised for limited training data and easily extensible for further languages. Used as a spaCy pipeline component.
|
||||
- [spacy-stanza](https://github.com/explosion/spacy-stanza): This is a wrapper that allows the use of Stanford's Stanza library in spaCy.
|
||||
- [spacy-streamlit](https://github.com/explosion/spacy-streamlit): A wrapper for the Streamlit dashboard building library to help with integrating [displaCy](https://spacy.io/api/top-level/#displacy).
|
||||
- [spacymoji](https://github.com/explosion/spacymoji): A library to add extra support for emoji to spaCy, such as including character names.
|
||||
- [thinc-apple-ops](https://github.com/explosion/thinc-apple-ops): A special backend for OSX that uses Apple's native libraries for improved performance.
|
||||
- [os-signpost](https://github.com/explosion/os-signpost): A Python package that allows you to use the `OSSignposter` API in OSX for performance analysis.
|
||||
- [spacy-ray](https://github.com/explosion/spacy-ray): A wrapper to integrate spaCy with Ray, a distributed training framework. Currently a work in progress.
|
||||
|
||||
## Prodigy
|
||||
|
||||
[Prodigy](https://prodi.gy) is Explosion's easy to use and highly customizable tool for annotating data. Prodigy itself requires a license, but the repos below contain documentation, examples, and editor or notebook integrations.
|
||||
|
||||
- [prodigy-recipes](https://github.com/explosion/prodigy-recipes): Sample recipes for Prodigy, along with notebooks and other examples of usage.
|
||||
- [vscode-prodigy](https://github.com/explosion/vscode-prodigy): A VS Code extension that lets you run Prodigy inside VS Code.
|
||||
- [jupyterlab-prodigy](https://github.com/explosion/jupyterlab-prodigy): An extension for JupyterLab that lets you run Prodigy inside JupyterLab.
|
||||
|
||||
## Independent Tools or Projects
|
||||
|
||||
These are tools that may be related to or use spaCy, but are functional independent projects in their own right as well.
|
||||
|
||||
- [floret](https://github.com/explosion/floret): A modification of fastText to use Bloom Embeddings. Can be used to add vectors with subword features to spaCy, and also works independently in the same manner as fastText.
|
||||
- [sense2vec](https://github.com/explosion/sense2vec): A library to make embeddings of noun phrases or words coupled with their part of speech. This library uses spaCy.
|
||||
- [spacy-vectors-builder](https://github.com/explosion/spacy-vectors-builder): This is a spaCy project that builds vectors using floret and a lot of input text. It handles downloading the input data as well as the actual building of vectors.
|
||||
- [holmes-extractor](https://github.com/explosion/holmes-extractor): Information extraction from English and German texts based on predicate logic. Uses spaCy.
|
||||
- [healthsea](https://github.com/explosion/healthsea): Healthsea is a project to extract information from comments about health supplements. Structurally, it's a self-contained, large spaCy project.
|
||||
- [spacy-pkuseg](https://github.com/explosion/spacy-pkuseg): A fork of the pkuseg Chinese tokenizer. Used for Chinese support in spaCy, but also works independently.
|
||||
- [ml-datasets](https://github.com/explosion/ml-datasets): This repo includes loaders for several standard machine learning datasets, like MNIST or WikiNER, and has historically been used in spaCy example code and documentation.
|
||||
|
||||
## Documentation and Informational Repos
|
||||
|
||||
These repos are used to support the spaCy docs or otherwise present information about spaCy or other Explosion projects.
|
||||
|
||||
- [projects](https://github.com/explosion/projects): The projects repo is used to show detailed examples of spaCy usage. Individual projects can be checked out using the spaCy command line tool, rather than checking out the projects repo directly.
|
||||
- [spacy-course](https://github.com/explosion/spacy-course): Home to the interactive spaCy course for learning about how to use the library and some basic NLP principles.
|
||||
- [spacy-io-binder](https://github.com/explosion/spacy-io-binder): Home to the notebooks used for interactive examples in the documentation.
|
||||
|
||||
## Organizational / Meta
|
||||
|
||||
These repos are used for organizing data around spaCy, but are not something an end user would need to install as part of using the library.
|
||||
|
||||
- [spacy-models](https://github.com/explosion/spacy-models): This repo contains metadata (but not training data) for all the spaCy models. This includes information about where their training data came from, version compatability, and performance information. It also includes tests for the model packages, and the built models are hosted as releases of this repo.
|
||||
- [wheelwright](https://github.com/explosion/wheelwright): A tool for automating our PyPI builds and releases.
|
||||
- [ec2buildwheel](https://github.com/explosion/ec2buildwheel): A small project that allows you to build Python packages in the manner of cibuildwheel, but on any EC2 image. Used by wheelwright.
|
||||
|
||||
## Other
|
||||
|
||||
Repos that don't fit in any of the above categories.
|
||||
|
||||
- [blis](https://github.com/explosion/blis): A fork of the official BLIS library. The main branch is not updated, but work continues in various branches. This is used for cython-blis.
|
||||
- [tokenizations](https://github.com/explosion/tokenizations): A library originally by Yohei Tamura to align strings with tolerance to some variations in features like case and diacritics, used for aligning tokens and wordpieces. Adopted and maintained by Explosion, but usually spacy-alignments is used instead.
|
||||
- [conll-2012](https://github.com/explosion/conll-2012): A repo to hold some slightly cleaned up versions of the official scripts for the CoNLL 2012 shared task involving coreference resolution. Used in the coref project.
|
||||
- [fastapi-explosion-extras](https://github.com/explosion/fastapi-explosion-extras): Some small tweaks to FastAPI used at Explosion.
|
||||
|
||||
|
|
@ -127,3 +127,34 @@ distributed under the License is distributed on an "AS IS" BASIS,
|
|||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
|
||||
|
||||
polyleven
|
||||
---------
|
||||
|
||||
* Files: spacy/matcher/polyleven.c
|
||||
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2021 Fujimoto Seiji <fujimoto@ceptord.net>
|
||||
Copyright (c) 2021 Max Bachmann <kontakt@maxbachmann.de>
|
||||
Copyright (c) 2022 Nick Mazuk
|
||||
Copyright (c) 2022 Michael Weiss <code@mweiss.ch>
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
|
|
|
|||
|
|
@ -6,7 +6,6 @@ requires = [
|
|||
"preshed>=3.0.2,<3.1.0",
|
||||
"murmurhash>=0.28.0,<1.1.0",
|
||||
"thinc>=8.1.0,<8.2.0",
|
||||
"pathy",
|
||||
"numpy>=1.15.0",
|
||||
]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
# Our libraries
|
||||
spacy-legacy>=3.0.9,<3.1.0
|
||||
spacy-legacy>=3.0.10,<3.1.0
|
||||
spacy-loggers>=1.0.0,<2.0.0
|
||||
cymem>=2.0.2,<2.1.0
|
||||
preshed>=3.0.2,<3.1.0
|
||||
|
|
@ -30,8 +30,10 @@ pytest-timeout>=1.3.0,<2.0.0
|
|||
mock>=2.0.0,<3.0.0
|
||||
flake8>=3.8.0,<3.10.0
|
||||
hypothesis>=3.27.0,<7.0.0
|
||||
mypy>=0.910,<0.970; platform_machine!='aarch64'
|
||||
mypy>=0.980,<0.990; platform_machine != "aarch64" and python_version >= "3.7"
|
||||
types-dataclasses>=0.1.3; python_version < "3.7"
|
||||
types-mock>=0.1.1
|
||||
types-setuptools>=57.0.0
|
||||
types-requests
|
||||
types-setuptools>=57.0.0
|
||||
black>=22.0,<23.0
|
||||
|
|
|
|||
42
setup.cfg
42
setup.cfg
|
|
@ -41,7 +41,7 @@ setup_requires =
|
|||
thinc>=8.1.0,<8.2.0
|
||||
install_requires =
|
||||
# Our libraries
|
||||
spacy-legacy>=3.0.9,<3.1.0
|
||||
spacy-legacy>=3.0.10,<3.1.0
|
||||
spacy-loggers>=1.0.0,<2.0.0
|
||||
murmurhash>=0.28.0,<1.1.0
|
||||
cymem>=2.0.2,<2.1.0
|
||||
|
|
@ -50,9 +50,9 @@ install_requires =
|
|||
wasabi>=0.9.1,<1.1.0
|
||||
srsly>=2.4.3,<3.0.0
|
||||
catalogue>=2.0.6,<2.1.0
|
||||
# Third-party dependencies
|
||||
typer>=0.3.0,<0.5.0
|
||||
pathy>=0.3.5
|
||||
# Third-party dependencies
|
||||
tqdm>=4.38.0,<5.0.0
|
||||
numpy>=1.15.0
|
||||
requests>=2.13.0,<3.0.0
|
||||
|
|
@ -76,37 +76,41 @@ transformers =
|
|||
ray =
|
||||
spacy_ray>=0.1.0,<1.0.0
|
||||
cuda =
|
||||
cupy>=5.0.0b4,<11.0.0
|
||||
cupy>=5.0.0b4,<12.0.0
|
||||
cuda80 =
|
||||
cupy-cuda80>=5.0.0b4,<11.0.0
|
||||
cupy-cuda80>=5.0.0b4,<12.0.0
|
||||
cuda90 =
|
||||
cupy-cuda90>=5.0.0b4,<11.0.0
|
||||
cupy-cuda90>=5.0.0b4,<12.0.0
|
||||
cuda91 =
|
||||
cupy-cuda91>=5.0.0b4,<11.0.0
|
||||
cupy-cuda91>=5.0.0b4,<12.0.0
|
||||
cuda92 =
|
||||
cupy-cuda92>=5.0.0b4,<11.0.0
|
||||
cupy-cuda92>=5.0.0b4,<12.0.0
|
||||
cuda100 =
|
||||
cupy-cuda100>=5.0.0b4,<11.0.0
|
||||
cupy-cuda100>=5.0.0b4,<12.0.0
|
||||
cuda101 =
|
||||
cupy-cuda101>=5.0.0b4,<11.0.0
|
||||
cupy-cuda101>=5.0.0b4,<12.0.0
|
||||
cuda102 =
|
||||
cupy-cuda102>=5.0.0b4,<11.0.0
|
||||
cupy-cuda102>=5.0.0b4,<12.0.0
|
||||
cuda110 =
|
||||
cupy-cuda110>=5.0.0b4,<11.0.0
|
||||
cupy-cuda110>=5.0.0b4,<12.0.0
|
||||
cuda111 =
|
||||
cupy-cuda111>=5.0.0b4,<11.0.0
|
||||
cupy-cuda111>=5.0.0b4,<12.0.0
|
||||
cuda112 =
|
||||
cupy-cuda112>=5.0.0b4,<11.0.0
|
||||
cupy-cuda112>=5.0.0b4,<12.0.0
|
||||
cuda113 =
|
||||
cupy-cuda113>=5.0.0b4,<11.0.0
|
||||
cupy-cuda113>=5.0.0b4,<12.0.0
|
||||
cuda114 =
|
||||
cupy-cuda114>=5.0.0b4,<11.0.0
|
||||
cupy-cuda114>=5.0.0b4,<12.0.0
|
||||
cuda115 =
|
||||
cupy-cuda115>=5.0.0b4,<11.0.0
|
||||
cupy-cuda115>=5.0.0b4,<12.0.0
|
||||
cuda116 =
|
||||
cupy-cuda116>=5.0.0b4,<11.0.0
|
||||
cupy-cuda116>=5.0.0b4,<12.0.0
|
||||
cuda117 =
|
||||
cupy-cuda117>=5.0.0b4,<11.0.0
|
||||
cupy-cuda117>=5.0.0b4,<12.0.0
|
||||
cuda11x =
|
||||
cupy-cuda11x>=11.0.0,<12.0.0
|
||||
cuda-autodetect =
|
||||
cupy-wheel>=11.0.0,<12.0.0
|
||||
apple =
|
||||
thinc-apple-ops>=0.1.0.dev0,<1.0.0
|
||||
# Language tokenizers with external dependencies
|
||||
|
|
@ -114,7 +118,7 @@ ja =
|
|||
sudachipy>=0.5.2,!=0.6.1
|
||||
sudachidict_core>=20211220
|
||||
ko =
|
||||
natto-py==0.9.0
|
||||
natto-py>=0.9.0
|
||||
th =
|
||||
pythainlp>=2.0
|
||||
|
||||
|
|
|
|||
11
setup.py
11
setup.py
|
|
@ -205,6 +205,17 @@ def setup_package():
|
|||
get_python_inc(plat_specific=True),
|
||||
]
|
||||
ext_modules = []
|
||||
ext_modules.append(
|
||||
Extension(
|
||||
"spacy.matcher.levenshtein",
|
||||
[
|
||||
"spacy/matcher/levenshtein.pyx",
|
||||
"spacy/matcher/polyleven.c",
|
||||
],
|
||||
language="c",
|
||||
include_dirs=include_dirs,
|
||||
)
|
||||
)
|
||||
for name in MOD_NAMES:
|
||||
mod_path = name.replace(".", "/") + ".pyx"
|
||||
ext = Extension(
|
||||
|
|
|
|||
|
|
@ -31,21 +31,21 @@ def load(
|
|||
name: Union[str, Path],
|
||||
*,
|
||||
vocab: Union[Vocab, bool] = True,
|
||||
disable: Iterable[str] = util.SimpleFrozenList(),
|
||||
enable: Iterable[str] = util.SimpleFrozenList(),
|
||||
exclude: Iterable[str] = util.SimpleFrozenList(),
|
||||
disable: Union[str, Iterable[str]] = util._DEFAULT_EMPTY_PIPES,
|
||||
enable: Union[str, Iterable[str]] = util._DEFAULT_EMPTY_PIPES,
|
||||
exclude: Union[str, Iterable[str]] = util._DEFAULT_EMPTY_PIPES,
|
||||
config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
|
||||
) -> Language:
|
||||
"""Load a spaCy model from an installed package or a local path.
|
||||
|
||||
name (str): Package name or model path.
|
||||
vocab (Vocab): A Vocab object. If True, a vocab is created.
|
||||
disable (Iterable[str]): Names of pipeline components to disable. Disabled
|
||||
disable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to disable. Disabled
|
||||
pipes will be loaded but they won't be run unless you explicitly
|
||||
enable them by calling nlp.enable_pipe.
|
||||
enable (Iterable[str]): Names of pipeline components to enable. All other
|
||||
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable. All other
|
||||
pipes will be disabled (but can be enabled later using nlp.enable_pipe).
|
||||
exclude (Iterable[str]): Names of pipeline components to exclude. Excluded
|
||||
exclude (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to exclude. Excluded
|
||||
components won't be loaded.
|
||||
config (Dict[str, Any] / Config): Config overrides as nested dict or dict
|
||||
keyed by section values in dot notation.
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# fmt: off
|
||||
__title__ = "spacy"
|
||||
__version__ = "3.4.0"
|
||||
__version__ = "3.4.1"
|
||||
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
|
||||
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
|
||||
__projects__ = "https://github.com/explosion/projects"
|
||||
|
|
|
|||
|
|
@ -733,3 +733,12 @@ def _get_fileinfo(
|
|||
md5 = get_checksum(file_path) if file_path.exists() else None
|
||||
data.append({"path": path, "md5": md5})
|
||||
return data
|
||||
|
||||
|
||||
def _format_number(number: Union[int, float], ndigits: int = 2) -> str:
|
||||
"""Formats a number (float or int) rounding to `ndigits`, without truncating trailing 0s,
|
||||
as happens with `round(number, ndigits)`"""
|
||||
if isinstance(number, float):
|
||||
return f"{number:.{ndigits}f}"
|
||||
else:
|
||||
return str(number)
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import typer
|
|||
import math
|
||||
|
||||
from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
|
||||
from ._util import import_code, debug_cli
|
||||
from ._util import import_code, debug_cli, _format_number
|
||||
from ..training import Example, remove_bilu_prefix
|
||||
from ..training.initialize import get_sourced_components
|
||||
from ..schemas import ConfigSchemaTraining
|
||||
|
|
@ -989,7 +989,8 @@ def _get_kl_divergence(p: Counter, q: Counter) -> float:
|
|||
def _format_span_row(span_data: List[Dict], labels: List[str]) -> List[Any]:
|
||||
"""Compile into one list for easier reporting"""
|
||||
d = {
|
||||
label: [label] + list(round(d[label], 2) for d in span_data) for label in labels
|
||||
label: [label] + list(_format_number(d[label]) for d in span_data)
|
||||
for label in labels
|
||||
}
|
||||
return list(d.values())
|
||||
|
||||
|
|
@ -1004,6 +1005,10 @@ def _get_span_characteristics(
|
|||
label: _gmean(l)
|
||||
for label, l in compiled_gold["spans_length"][spans_key].items()
|
||||
}
|
||||
spans_per_type = {
|
||||
label: len(spans)
|
||||
for label, spans in compiled_gold["spans_per_type"][spans_key].items()
|
||||
}
|
||||
min_lengths = [min(l) for l in compiled_gold["spans_length"][spans_key].values()]
|
||||
max_lengths = [max(l) for l in compiled_gold["spans_length"][spans_key].values()]
|
||||
|
||||
|
|
@ -1031,6 +1036,7 @@ def _get_span_characteristics(
|
|||
return {
|
||||
"sd": span_distinctiveness,
|
||||
"bd": sb_distinctiveness,
|
||||
"spans_per_type": spans_per_type,
|
||||
"lengths": span_length,
|
||||
"min_length": min(min_lengths),
|
||||
"max_length": max(max_lengths),
|
||||
|
|
@ -1045,12 +1051,15 @@ def _get_span_characteristics(
|
|||
|
||||
def _print_span_characteristics(span_characteristics: Dict[str, Any]):
|
||||
"""Print all span characteristics into a table"""
|
||||
headers = ("Span Type", "Length", "SD", "BD")
|
||||
headers = ("Span Type", "Length", "SD", "BD", "N")
|
||||
# Wasabi has this at 30 by default, but we might have some long labels
|
||||
max_col = max(30, max(len(label) for label in span_characteristics["labels"]))
|
||||
# Prepare table data with all span characteristics
|
||||
table_data = [
|
||||
span_characteristics["lengths"],
|
||||
span_characteristics["sd"],
|
||||
span_characteristics["bd"],
|
||||
span_characteristics["spans_per_type"],
|
||||
]
|
||||
table = _format_span_row(
|
||||
span_data=table_data, labels=span_characteristics["labels"]
|
||||
|
|
@ -1061,8 +1070,18 @@ def _print_span_characteristics(span_characteristics: Dict[str, Any]):
|
|||
span_characteristics["avg_sd"],
|
||||
span_characteristics["avg_bd"],
|
||||
]
|
||||
footer = ["Wgt. Average"] + [str(round(f, 2)) for f in footer_data]
|
||||
msg.table(table, footer=footer, header=headers, divider=True)
|
||||
|
||||
footer = (
|
||||
["Wgt. Average"] + ["{:.2f}".format(round(f, 2)) for f in footer_data] + ["-"]
|
||||
)
|
||||
msg.table(
|
||||
table,
|
||||
footer=footer,
|
||||
header=headers,
|
||||
divider=True,
|
||||
aligns=["l"] + ["r"] * (len(footer_data) + 1),
|
||||
max_col=max_col,
|
||||
)
|
||||
|
||||
|
||||
def _get_spans_length_freq_dist(
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ import typer
|
|||
from ._util import app, Arg, Opt, WHEEL_SUFFIX, SDIST_SUFFIX
|
||||
from .. import about
|
||||
from ..util import is_package, get_minor_version, run_command
|
||||
from ..util import is_prerelease_version
|
||||
from ..errors import OLD_MODEL_SHORTCUTS
|
||||
|
||||
|
||||
|
|
@ -19,7 +20,7 @@ def download_cli(
|
|||
ctx: typer.Context,
|
||||
model: str = Arg(..., help="Name of pipeline package to download"),
|
||||
direct: bool = Opt(False, "--direct", "-d", "-D", help="Force direct download of name + version"),
|
||||
sdist: bool = Opt(False, "--sdist", "-S", help="Download sdist (.tar.gz) archive instead of pre-built binary wheel")
|
||||
sdist: bool = Opt(False, "--sdist", "-S", help="Download sdist (.tar.gz) archive instead of pre-built binary wheel"),
|
||||
# fmt: on
|
||||
):
|
||||
"""
|
||||
|
|
@ -35,7 +36,12 @@ def download_cli(
|
|||
download(model, direct, sdist, *ctx.args)
|
||||
|
||||
|
||||
def download(model: str, direct: bool = False, sdist: bool = False, *pip_args) -> None:
|
||||
def download(
|
||||
model: str,
|
||||
direct: bool = False,
|
||||
sdist: bool = False,
|
||||
*pip_args,
|
||||
) -> None:
|
||||
if (
|
||||
not (is_package("spacy") or is_package("spacy-nightly"))
|
||||
and "--no-deps" not in pip_args
|
||||
|
|
@ -49,13 +55,10 @@ def download(model: str, direct: bool = False, sdist: bool = False, *pip_args) -
|
|||
"dependencies, you'll have to install them manually."
|
||||
)
|
||||
pip_args = pip_args + ("--no-deps",)
|
||||
suffix = SDIST_SUFFIX if sdist else WHEEL_SUFFIX
|
||||
dl_tpl = "{m}-{v}/{m}-{v}{s}#egg={m}=={v}"
|
||||
if direct:
|
||||
components = model.split("-")
|
||||
model_name = "".join(components[:-1])
|
||||
version = components[-1]
|
||||
download_model(dl_tpl.format(m=model_name, v=version, s=suffix), pip_args)
|
||||
else:
|
||||
model_name = model
|
||||
if model in OLD_MODEL_SHORTCUTS:
|
||||
|
|
@ -66,15 +69,31 @@ def download(model: str, direct: bool = False, sdist: bool = False, *pip_args) -
|
|||
model_name = OLD_MODEL_SHORTCUTS[model]
|
||||
compatibility = get_compatibility()
|
||||
version = get_version(model_name, compatibility)
|
||||
download_model(dl_tpl.format(m=model_name, v=version, s=suffix), pip_args)
|
||||
|
||||
filename = get_model_filename(model_name, version, sdist)
|
||||
|
||||
download_model(filename, pip_args)
|
||||
msg.good(
|
||||
"Download and installation successful",
|
||||
f"You can now load the package via spacy.load('{model_name}')",
|
||||
)
|
||||
|
||||
|
||||
def get_model_filename(model_name: str, version: str, sdist: bool = False) -> str:
|
||||
dl_tpl = "{m}-{v}/{m}-{v}{s}"
|
||||
egg_tpl = "#egg={m}=={v}"
|
||||
suffix = SDIST_SUFFIX if sdist else WHEEL_SUFFIX
|
||||
filename = dl_tpl.format(m=model_name, v=version, s=suffix)
|
||||
if sdist:
|
||||
filename += egg_tpl.format(m=model_name, v=version)
|
||||
return filename
|
||||
|
||||
|
||||
def get_compatibility() -> dict:
|
||||
version = get_minor_version(about.__version__)
|
||||
if is_prerelease_version(about.__version__):
|
||||
version: Optional[str] = about.__version__
|
||||
else:
|
||||
version = get_minor_version(about.__version__)
|
||||
r = requests.get(about.__compatibility__)
|
||||
if r.status_code != 200:
|
||||
msg.fail(
|
||||
|
|
@ -101,6 +120,11 @@ def get_version(model: str, comp: dict) -> str:
|
|||
return comp[model][0]
|
||||
|
||||
|
||||
def get_latest_version(model: str) -> str:
|
||||
comp = get_compatibility()
|
||||
return get_version(model, comp)
|
||||
|
||||
|
||||
def download_model(
|
||||
filename: str, user_pip_args: Optional[Sequence[str]] = None
|
||||
) -> None:
|
||||
|
|
|
|||
|
|
@ -1,10 +1,13 @@
|
|||
from typing import Optional, Dict, Any, Union, List
|
||||
import platform
|
||||
import pkg_resources
|
||||
import json
|
||||
from pathlib import Path
|
||||
from wasabi import Printer, MarkdownRenderer
|
||||
import srsly
|
||||
|
||||
from ._util import app, Arg, Opt, string_to_list
|
||||
from .download import get_model_filename, get_latest_version
|
||||
from .. import util
|
||||
from .. import about
|
||||
|
||||
|
|
@ -16,6 +19,7 @@ def info_cli(
|
|||
markdown: bool = Opt(False, "--markdown", "-md", help="Generate Markdown for GitHub issues"),
|
||||
silent: bool = Opt(False, "--silent", "-s", "-S", help="Don't print anything (just return)"),
|
||||
exclude: str = Opt("labels", "--exclude", "-e", help="Comma-separated keys to exclude from the print-out"),
|
||||
url: bool = Opt(False, "--url", "-u", help="Print the URL to download the most recent compatible version of the pipeline"),
|
||||
# fmt: on
|
||||
):
|
||||
"""
|
||||
|
|
@ -23,10 +27,19 @@ def info_cli(
|
|||
print its meta information. Flag --markdown prints details in Markdown for easy
|
||||
copy-pasting to GitHub issues.
|
||||
|
||||
Flag --url prints only the download URL of the most recent compatible
|
||||
version of the pipeline.
|
||||
|
||||
DOCS: https://spacy.io/api/cli#info
|
||||
"""
|
||||
exclude = string_to_list(exclude)
|
||||
info(model, markdown=markdown, silent=silent, exclude=exclude)
|
||||
info(
|
||||
model,
|
||||
markdown=markdown,
|
||||
silent=silent,
|
||||
exclude=exclude,
|
||||
url=url,
|
||||
)
|
||||
|
||||
|
||||
def info(
|
||||
|
|
@ -35,11 +48,20 @@ def info(
|
|||
markdown: bool = False,
|
||||
silent: bool = True,
|
||||
exclude: Optional[List[str]] = None,
|
||||
url: bool = False,
|
||||
) -> Union[str, dict]:
|
||||
msg = Printer(no_print=silent, pretty=not silent)
|
||||
if not exclude:
|
||||
exclude = []
|
||||
if model:
|
||||
if url:
|
||||
if model is not None:
|
||||
title = f"Download info for pipeline '{model}'"
|
||||
data = info_model_url(model)
|
||||
print(data["download_url"])
|
||||
return data
|
||||
else:
|
||||
msg.fail("--url option requires a pipeline name", exits=1)
|
||||
elif model:
|
||||
title = f"Info about pipeline '{model}'"
|
||||
data = info_model(model, silent=silent)
|
||||
else:
|
||||
|
|
@ -99,11 +121,44 @@ def info_model(model: str, *, silent: bool = True) -> Dict[str, Any]:
|
|||
meta["source"] = str(model_path.resolve())
|
||||
else:
|
||||
meta["source"] = str(model_path)
|
||||
download_url = info_installed_model_url(model)
|
||||
if download_url:
|
||||
meta["download_url"] = download_url
|
||||
return {
|
||||
k: v for k, v in meta.items() if k not in ("accuracy", "performance", "speed")
|
||||
}
|
||||
|
||||
|
||||
def info_installed_model_url(model: str) -> Optional[str]:
|
||||
"""Given a pipeline name, get the download URL if available, otherwise
|
||||
return None.
|
||||
|
||||
This is only available for pipelines installed as modules that have
|
||||
dist-info available.
|
||||
"""
|
||||
try:
|
||||
dist = pkg_resources.get_distribution(model)
|
||||
data = json.loads(dist.get_metadata("direct_url.json"))
|
||||
return data["url"]
|
||||
except pkg_resources.DistributionNotFound:
|
||||
# no such package
|
||||
return None
|
||||
except Exception:
|
||||
# something else, like no file or invalid JSON
|
||||
return None
|
||||
|
||||
|
||||
def info_model_url(model: str) -> Dict[str, Any]:
|
||||
"""Return the download URL for the latest version of a pipeline."""
|
||||
version = get_latest_version(model)
|
||||
|
||||
filename = get_model_filename(model, version)
|
||||
download_url = about.__download_url__ + "/" + filename
|
||||
release_tpl = "https://github.com/explosion/spacy-models/releases/tag/{m}-{v}"
|
||||
release_url = release_tpl.format(m=model, v=version)
|
||||
return {"download_url": download_url, "release_url": release_url}
|
||||
|
||||
|
||||
def get_markdown(
|
||||
data: Dict[str, Any],
|
||||
title: Optional[str] = None,
|
||||
|
|
|
|||
|
|
@ -299,8 +299,8 @@ def get_meta(
|
|||
}
|
||||
nlp = util.load_model_from_path(Path(model_path))
|
||||
meta.update(nlp.meta)
|
||||
meta.update(existing_meta)
|
||||
meta["spacy_version"] = util.get_minor_version_range(about.__version__)
|
||||
meta.update(existing_meta)
|
||||
meta["vectors"] = {
|
||||
"width": nlp.vocab.vectors_length,
|
||||
"vectors": len(nlp.vocab.vectors),
|
||||
|
|
|
|||
|
|
@ -61,7 +61,7 @@ def pretrain_cli(
|
|||
# TODO: What's the solution here? How do we handle optional blocks?
|
||||
msg.fail("The [pretraining] block in your config is empty", exits=1)
|
||||
if not output_dir.exists():
|
||||
output_dir.mkdir()
|
||||
output_dir.mkdir(parents=True)
|
||||
msg.good(f"Created output directory: {output_dir}")
|
||||
# Save non-interpolated config
|
||||
raw_config.to_disk(output_dir / "config.cfg")
|
||||
|
|
|
|||
|
|
@ -1,6 +1,8 @@
|
|||
from typing import Optional, List, Dict, Sequence, Any, Iterable, Tuple
|
||||
import sys
|
||||
import os.path
|
||||
from pathlib import Path
|
||||
import pkg_resources
|
||||
from wasabi import msg
|
||||
from wasabi.util import locale_escape
|
||||
import typer
|
||||
|
|
@ -70,6 +72,12 @@ def project_run(
|
|||
commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
|
||||
workflows = config.get("workflows", {})
|
||||
validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand)
|
||||
|
||||
req_path = project_dir / "requirements.txt"
|
||||
if config.get("check_requirements", True) and os.path.exists(req_path):
|
||||
with req_path.open() as requirements_file:
|
||||
_check_requirements([req.replace("\n", "") for req in requirements_file])
|
||||
|
||||
if subcommand in workflows:
|
||||
msg.info(f"Running workflow '{subcommand}'")
|
||||
for workflow_item in workflows[subcommand]:
|
||||
|
|
@ -251,6 +259,8 @@ def validate_subcommand(
|
|||
msg.fail(f"No commands or workflows defined in {PROJECT_FILE}", exits=1)
|
||||
if subcommand not in commands and subcommand not in workflows:
|
||||
help_msg = []
|
||||
if subcommand in ["assets", "asset"]:
|
||||
help_msg.append("Did you mean to run: python -m spacy project assets?")
|
||||
if commands:
|
||||
help_msg.append(f"Available commands: {', '.join(commands)}")
|
||||
if workflows:
|
||||
|
|
@ -260,3 +270,32 @@ def validate_subcommand(
|
|||
". ".join(help_msg),
|
||||
exits=1,
|
||||
)
|
||||
|
||||
|
||||
def _check_requirements(requirements: List[str]) -> Tuple[bool, bool]:
|
||||
"""Checks whether requirements are installed and free of version conflicts.
|
||||
requirements (List[str]): List of requirements.
|
||||
RETURNS (Tuple[bool, bool]): Whether (1) any packages couldn't be imported, (2) any packages with version conflicts
|
||||
exist.
|
||||
"""
|
||||
|
||||
failed_pkgs_msgs: List[str] = []
|
||||
conflicting_pkgs_msgs: List[str] = []
|
||||
|
||||
for req in requirements:
|
||||
try:
|
||||
pkg_resources.require(req)
|
||||
except pkg_resources.DistributionNotFound as dnf:
|
||||
failed_pkgs_msgs.append(dnf.report())
|
||||
except pkg_resources.VersionConflict as vc:
|
||||
conflicting_pkgs_msgs.append(vc.report())
|
||||
|
||||
if len(failed_pkgs_msgs) or len(conflicting_pkgs_msgs):
|
||||
msg.warn(
|
||||
title="Missing requirements or requirement conflicts detected. Make sure your Python environment is set up "
|
||||
"correctly and you installed all requirements specified in your project's requirements.txt: "
|
||||
)
|
||||
for pgk_msg in failed_pkgs_msgs + conflicting_pkgs_msgs:
|
||||
msg.text(pgk_msg)
|
||||
|
||||
return len(failed_pkgs_msgs) > 0, len(conflicting_pkgs_msgs) > 0
|
||||
|
|
@ -271,13 +271,8 @@ factory = "tok2vec"
|
|||
[components.tok2vec.model.embed]
|
||||
@architectures = "spacy.MultiHashEmbed.v2"
|
||||
width = ${components.tok2vec.model.encode.width}
|
||||
{% if has_letters -%}
|
||||
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
|
||||
rows = [5000, 2500, 2500, 2500]
|
||||
{% else -%}
|
||||
attrs = ["ORTH", "SHAPE"]
|
||||
rows = [5000, 2500]
|
||||
{% endif -%}
|
||||
rows = [5000, 1000, 2500, 2500]
|
||||
include_static_vectors = {{ "true" if optimize == "accuracy" else "false" }}
|
||||
|
||||
[components.tok2vec.model.encode]
|
||||
|
|
|
|||
|
|
@ -271,4 +271,3 @@ zh:
|
|||
accuracy:
|
||||
name: bert-base-chinese
|
||||
size_factor: 3
|
||||
has_letters: false
|
||||
|
|
|
|||
|
|
@ -123,7 +123,8 @@ def app(environ, start_response):
|
|||
def parse_deps(orig_doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
|
||||
"""Generate dependency parse in {'words': [], 'arcs': []} format.
|
||||
|
||||
doc (Doc): Document do parse.
|
||||
orig_doc (Doc): Document to parse.
|
||||
options (Dict[str, Any]): Dependency parse specific visualisation options.
|
||||
RETURNS (dict): Generated dependency parse keyed by words and arcs.
|
||||
"""
|
||||
doc = Doc(orig_doc.vocab).from_bytes(
|
||||
|
|
@ -209,7 +210,7 @@ def parse_ents(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
|
|||
|
||||
|
||||
def parse_spans(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
|
||||
"""Generate spans in [{start: i, end: i, label: 'label'}] format.
|
||||
"""Generate spans in [{start_token: i, end_token: i, label: 'label'}] format.
|
||||
|
||||
doc (Doc): Document to parse.
|
||||
options (Dict[str, any]): Span-specific visualisation options.
|
||||
|
|
|
|||
|
|
@ -16,8 +16,8 @@ def setup_default_warnings():
|
|||
filter_warning("ignore", error_msg="numpy.dtype size changed") # noqa
|
||||
filter_warning("ignore", error_msg="numpy.ufunc size changed") # noqa
|
||||
|
||||
# warn about entity_ruler & matcher having no patterns only once
|
||||
for pipe in ["matcher", "entity_ruler"]:
|
||||
# warn about entity_ruler, span_ruler & matcher having no patterns only once
|
||||
for pipe in ["matcher", "entity_ruler", "span_ruler"]:
|
||||
filter_warning("once", error_msg=Warnings.W036.format(name=pipe))
|
||||
|
||||
# warn once about lemmatizer without required POS
|
||||
|
|
@ -212,6 +212,8 @@ class Warnings(metaclass=ErrorsWithCodes):
|
|||
W121 = ("Attempting to trace non-existent method '{method}' in pipe '{pipe}'")
|
||||
W122 = ("Couldn't trace method '{method}' in pipe '{pipe}'. This can happen if the pipe class "
|
||||
"is a Cython extension type.")
|
||||
W123 = ("Argument {arg} with value {arg_value} is used instead of {config_value} as specified in the config. Be "
|
||||
"aware that this might affect other components in your pipeline.")
|
||||
|
||||
|
||||
class Errors(metaclass=ErrorsWithCodes):
|
||||
|
|
@ -230,8 +232,9 @@ class Errors(metaclass=ErrorsWithCodes):
|
|||
"initialized component.")
|
||||
E004 = ("Can't set up pipeline component: a factory for '{name}' already "
|
||||
"exists. Existing factory: {func}. New factory: {new_func}")
|
||||
E005 = ("Pipeline component '{name}' returned None. If you're using a "
|
||||
"custom component, maybe you forgot to return the processed Doc?")
|
||||
E005 = ("Pipeline component '{name}' returned {returned_type} instead of a "
|
||||
"Doc. If you're using a custom component, maybe you forgot to "
|
||||
"return the processed Doc?")
|
||||
E006 = ("Invalid constraints for adding pipeline component. You can only "
|
||||
"set one of the following: before (component name or index), "
|
||||
"after (component name or index), first (True) or last (True). "
|
||||
|
|
@ -389,7 +392,7 @@ class Errors(metaclass=ErrorsWithCodes):
|
|||
"consider using doc.spans instead.")
|
||||
E106 = ("Can't find `doc._.{attr}` attribute specified in the underscore "
|
||||
"settings: {opts}")
|
||||
E107 = ("Value of `doc._.{attr}` is not JSON-serializable: {value}")
|
||||
E107 = ("Value of custom attribute `{attr}` is not JSON-serializable: {value}")
|
||||
E109 = ("Component '{name}' could not be run. Did you forget to "
|
||||
"call `initialize()`?")
|
||||
E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
|
||||
|
|
@ -535,11 +538,12 @@ class Errors(metaclass=ErrorsWithCodes):
|
|||
E198 = ("Unable to return {n} most similar vectors for the current vectors "
|
||||
"table, which contains {n_rows} vectors.")
|
||||
E199 = ("Unable to merge 0-length span at `doc[{start}:{end}]`.")
|
||||
E200 = ("Can't yet set {attr} from Span. Vote for this feature on the "
|
||||
"issue tracker: http://github.com/explosion/spaCy/issues")
|
||||
E200 = ("Can't set {attr} from Span.")
|
||||
E202 = ("Unsupported {name} mode '{mode}'. Supported modes: {modes}.")
|
||||
|
||||
# New errors added in v3.x
|
||||
E853 = ("Unsupported component factory name '{name}'. The character '.' is "
|
||||
"not permitted in factory names.")
|
||||
E854 = ("Unable to set doc.ents. Check that the 'ents_filter' does not "
|
||||
"permit overlapping spans.")
|
||||
E855 = ("Invalid {obj}: {obj} is not from the same doc.")
|
||||
|
|
@ -705,7 +709,7 @@ class Errors(metaclass=ErrorsWithCodes):
|
|||
"need to modify the pipeline, use the built-in methods like "
|
||||
"`nlp.add_pipe`, `nlp.remove_pipe`, `nlp.disable_pipe` or "
|
||||
"`nlp.enable_pipe` instead.")
|
||||
E927 = ("Can't write to frozen list Maybe you're trying to modify a computed "
|
||||
E927 = ("Can't write to frozen list. Maybe you're trying to modify a computed "
|
||||
"property or default function argument?")
|
||||
E928 = ("A KnowledgeBase can only be serialized to/from from a directory, "
|
||||
"but the provided argument {loc} points to a file.")
|
||||
|
|
@ -935,8 +939,9 @@ class Errors(metaclass=ErrorsWithCodes):
|
|||
E1040 = ("Doc.from_json requires all tokens to have the same attributes. "
|
||||
"Some tokens do not contain annotation for: {partial_attrs}")
|
||||
E1041 = ("Expected a string, Doc, or bytes as input, but got: {type}")
|
||||
E1042 = ("Function was called with `{arg1}`={arg1_values} and "
|
||||
"`{arg2}`={arg2_values} but these arguments are conflicting.")
|
||||
E1042 = ("`enable={enable}` and `disable={disable}` are inconsistent with each other.\nIf you only passed "
|
||||
"one of `enable` or `disable`, the other argument is specified in your pipeline's configuration.\nIn that "
|
||||
"case pass an empty list for the previously not specified argument to avoid this error.")
|
||||
E1043 = ("Expected None or a value in range [{range_start}, {range_end}] for entity linker threshold, but got "
|
||||
"{value}.")
|
||||
E1044 = ("Illegal transition from {old_state} to {new_state}.")
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from ..punctuation import TOKENIZER_INFIXES as BASE_TOKENIZER_INFIXES
|
|||
|
||||
|
||||
_infixes = (
|
||||
["·", "ㆍ", "\(", "\)"]
|
||||
["·", "ㆍ", r"\(", r"\)"]
|
||||
+ [r"(?<=[0-9])~(?=[0-9-])"]
|
||||
+ LIST_QUOTES
|
||||
+ BASE_TOKENIZER_INFIXES
|
||||
|
|
|
|||
18
spacy/lang/la/__init__.py
Normal file
18
spacy/lang/la/__init__.py
Normal file
|
|
@ -0,0 +1,18 @@
|
|||
from ...language import Language, BaseDefaults
|
||||
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
|
||||
from .stop_words import STOP_WORDS
|
||||
from .lex_attrs import LEX_ATTRS
|
||||
|
||||
|
||||
class LatinDefaults(BaseDefaults):
|
||||
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
|
||||
stop_words = STOP_WORDS
|
||||
lex_attr_getters = LEX_ATTRS
|
||||
|
||||
|
||||
class Latin(Language):
|
||||
lang = "la"
|
||||
Defaults = LatinDefaults
|
||||
|
||||
|
||||
__all__ = ["Latin"]
|
||||
34
spacy/lang/la/lex_attrs.py
Normal file
34
spacy/lang/la/lex_attrs.py
Normal file
|
|
@ -0,0 +1,34 @@
|
|||
from ...attrs import LIKE_NUM
|
||||
import re
|
||||
|
||||
# cf. Goyvaerts/Levithan 2009; case-insensitive, allow 4
|
||||
roman_numerals_compile = re.compile(
|
||||
r"(?i)^(?=[MDCLXVI])M*(C[MD]|D?C{0,4})(X[CL]|L?X{0,4})(I[XV]|V?I{0,4})$"
|
||||
)
|
||||
|
||||
_num_words = set(
|
||||
"""
|
||||
unus una unum duo duae tres tria quattuor quinque sex septem octo novem decem
|
||||
""".split()
|
||||
)
|
||||
|
||||
_ordinal_words = set(
|
||||
"""
|
||||
primus prima primum secundus secunda secundum tertius tertia tertium
|
||||
""".split()
|
||||
)
|
||||
|
||||
|
||||
def like_num(text):
|
||||
if text.isdigit():
|
||||
return True
|
||||
if roman_numerals_compile.match(text):
|
||||
return True
|
||||
if text.lower() in _num_words:
|
||||
return True
|
||||
if text.lower() in _ordinal_words:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
LEX_ATTRS = {LIKE_NUM: like_num}
|
||||
37
spacy/lang/la/stop_words.py
Normal file
37
spacy/lang/la/stop_words.py
Normal file
|
|
@ -0,0 +1,37 @@
|
|||
# Corrected Perseus list, cf. https://wiki.digitalclassicist.org/Stopwords_for_Greek_and_Latin
|
||||
|
||||
STOP_WORDS = set(
|
||||
"""
|
||||
ab ac ad adhuc aliqui aliquis an ante apud at atque aut autem
|
||||
|
||||
cum cur
|
||||
|
||||
de deinde dum
|
||||
|
||||
ego enim ergo es est et etiam etsi ex
|
||||
|
||||
fio
|
||||
|
||||
haud hic
|
||||
|
||||
iam idem igitur ille in infra inter interim ipse is ita
|
||||
|
||||
magis modo mox
|
||||
|
||||
nam ne nec necque neque nisi non nos
|
||||
|
||||
o ob
|
||||
|
||||
per possum post pro
|
||||
|
||||
quae quam quare qui quia quicumque quidem quilibet quis quisnam quisquam quisque quisquis quo quoniam
|
||||
|
||||
sed si sic sive sub sui sum super suus
|
||||
|
||||
tam tamen trans tu tum
|
||||
|
||||
ubi uel uero
|
||||
|
||||
vel vero
|
||||
""".split()
|
||||
)
|
||||
76
spacy/lang/la/tokenizer_exceptions.py
Normal file
76
spacy/lang/la/tokenizer_exceptions.py
Normal file
|
|
@ -0,0 +1,76 @@
|
|||
from ..tokenizer_exceptions import BASE_EXCEPTIONS
|
||||
from ...symbols import ORTH
|
||||
from ...util import update_exc
|
||||
|
||||
|
||||
## TODO: Look into systematically handling u/v
|
||||
_exc = {
|
||||
"mecum": [{ORTH: "me"}, {ORTH: "cum"}],
|
||||
"tecum": [{ORTH: "te"}, {ORTH: "cum"}],
|
||||
"nobiscum": [{ORTH: "nobis"}, {ORTH: "cum"}],
|
||||
"vobiscum": [{ORTH: "vobis"}, {ORTH: "cum"}],
|
||||
"uobiscum": [{ORTH: "uobis"}, {ORTH: "cum"}],
|
||||
}
|
||||
|
||||
for orth in [
|
||||
"A.",
|
||||
"Agr.",
|
||||
"Ap.",
|
||||
"C.",
|
||||
"Cn.",
|
||||
"D.",
|
||||
"F.",
|
||||
"K.",
|
||||
"L.",
|
||||
"M'.",
|
||||
"M.",
|
||||
"Mam.",
|
||||
"N.",
|
||||
"Oct.",
|
||||
"Opet.",
|
||||
"P.",
|
||||
"Paul.",
|
||||
"Post.",
|
||||
"Pro.",
|
||||
"Q.",
|
||||
"S.",
|
||||
"Ser.",
|
||||
"Sert.",
|
||||
"Sex.",
|
||||
"St.",
|
||||
"Sta.",
|
||||
"T.",
|
||||
"Ti.",
|
||||
"V.",
|
||||
"Vol.",
|
||||
"Vop.",
|
||||
"U.",
|
||||
"Uol.",
|
||||
"Uop.",
|
||||
"Ian.",
|
||||
"Febr.",
|
||||
"Mart.",
|
||||
"Apr.",
|
||||
"Mai.",
|
||||
"Iun.",
|
||||
"Iul.",
|
||||
"Aug.",
|
||||
"Sept.",
|
||||
"Oct.",
|
||||
"Nov.",
|
||||
"Nou.",
|
||||
"Dec.",
|
||||
"Non.",
|
||||
"Id.",
|
||||
"A.D.",
|
||||
"Coll.",
|
||||
"Cos.",
|
||||
"Ord.",
|
||||
"Pl.",
|
||||
"S.C.",
|
||||
"Suff.",
|
||||
"Trib.",
|
||||
]:
|
||||
_exc[orth] = [{ORTH: orth}]
|
||||
|
||||
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)
|
||||
18
spacy/lang/lg/__init__.py
Normal file
18
spacy/lang/lg/__init__.py
Normal file
|
|
@ -0,0 +1,18 @@
|
|||
from .stop_words import STOP_WORDS
|
||||
from .lex_attrs import LEX_ATTRS
|
||||
from .punctuation import TOKENIZER_INFIXES
|
||||
from ...language import Language, BaseDefaults
|
||||
|
||||
|
||||
class LugandaDefaults(BaseDefaults):
|
||||
lex_attr_getters = LEX_ATTRS
|
||||
infixes = TOKENIZER_INFIXES
|
||||
stop_words = STOP_WORDS
|
||||
|
||||
|
||||
class Luganda(Language):
|
||||
lang = "lg"
|
||||
Defaults = LugandaDefaults
|
||||
|
||||
|
||||
__all__ = ["Luganda"]
|
||||
17
spacy/lang/lg/examples.py
Normal file
17
spacy/lang/lg/examples.py
Normal file
|
|
@ -0,0 +1,17 @@
|
|||
"""
|
||||
Example sentences to test spaCy and its language models.
|
||||
|
||||
>>> from spacy.lang.lg.examples import sentences
|
||||
>>> docs = nlp.pipe(sentences)
|
||||
"""
|
||||
|
||||
sentences = [
|
||||
"Mpa ebyafaayo ku byalo Nakatu ne Nkajja",
|
||||
"Okuyita Ttembo kitegeeza kugwa ddalu",
|
||||
"Ekifumu kino kyali kya mulimu ki?",
|
||||
"Ekkovu we liyise wayitibwa mukululo",
|
||||
"Akola mulimu ki oguvaamu ssente?",
|
||||
"Emisumaali egikomerera embaawo giyitibwa nninga",
|
||||
"Abooluganda ab’emmamba ababiri",
|
||||
"Ekisaawe ky'ebyenjigiriza kya mugaso nnyo",
|
||||
]
|
||||
95
spacy/lang/lg/lex_attrs.py
Normal file
95
spacy/lang/lg/lex_attrs.py
Normal file
|
|
@ -0,0 +1,95 @@
|
|||
from ...attrs import LIKE_NUM
|
||||
|
||||
_num_words = [
|
||||
"nnooti", # Zero
|
||||
"zeero", # zero
|
||||
"emu", # one
|
||||
"bbiri", # two
|
||||
"ssatu", # three
|
||||
"nnya", # four
|
||||
"ttaano", # five
|
||||
"mukaaga", # six
|
||||
"musanvu", # seven
|
||||
"munaana", # eight
|
||||
"mwenda", # nine
|
||||
"kkumi", # ten
|
||||
"kkumi n'emu", # eleven
|
||||
"kkumi na bbiri", # twelve
|
||||
"kkumi na ssatu", # thirteen
|
||||
"kkumi na nnya", # forteen
|
||||
"kkumi na ttaano", # fifteen
|
||||
"kkumi na mukaaga", # sixteen
|
||||
"kkumi na musanvu", # seventeen
|
||||
"kkumi na munaana", # eighteen
|
||||
"kkumi na mwenda", # nineteen
|
||||
"amakumi abiri", # twenty
|
||||
"amakumi asatu", # thirty
|
||||
"amakumi ana", # forty
|
||||
"amakumi ataano", # fifty
|
||||
"nkaaga", # sixty
|
||||
"nsanvu", # seventy
|
||||
"kinaana", # eighty
|
||||
"kyenda", # ninety
|
||||
"kikumi", # hundred
|
||||
"lukumi", # thousand
|
||||
"kakadde", # million
|
||||
"kawumbi", # billion
|
||||
"kase", # trillion
|
||||
"katabalika", # quadrillion
|
||||
"keesedde", # gajillion
|
||||
"kafukunya", # bazillion
|
||||
"ekisooka", # first
|
||||
"ekyokubiri", # second
|
||||
"ekyokusatu", # third
|
||||
"ekyokuna", # fourth
|
||||
"ekyokutaano", # fifith
|
||||
"ekyomukaaga", # sixth
|
||||
"ekyomusanvu", # seventh
|
||||
"eky'omunaana", # eighth
|
||||
"ekyomwenda", # nineth
|
||||
"ekyekkumi", # tenth
|
||||
"ekyekkumi n'ekimu", # eleventh
|
||||
"ekyekkumi n'ebibiri", # twelveth
|
||||
"ekyekkumi n'ebisatu", # thirteenth
|
||||
"ekyekkumi n'ebina", # fourteenth
|
||||
"ekyekkumi n'ebitaano", # fifteenth
|
||||
"ekyekkumi n'omukaaga", # sixteenth
|
||||
"ekyekkumi n'omusanvu", # seventeenth
|
||||
"ekyekkumi n'omunaana", # eigteenth
|
||||
"ekyekkumi n'omwenda", # nineteenth
|
||||
"ekyamakumi abiri", # twentieth
|
||||
"ekyamakumi asatu", # thirtieth
|
||||
"ekyamakumi ana", # fortieth
|
||||
"ekyamakumi ataano", # fiftieth
|
||||
"ekyenkaaga", # sixtieth
|
||||
"ekyensanvu", # seventieth
|
||||
"ekyekinaana", # eightieth
|
||||
"ekyekyenda", # ninetieth
|
||||
"ekyekikumi", # hundredth
|
||||
"ekyolukumi", # thousandth
|
||||
"ekyakakadde", # millionth
|
||||
"ekyakawumbi", # billionth
|
||||
"ekyakase", # trillionth
|
||||
"ekyakatabalika", # quadrillionth
|
||||
"ekyakeesedde", # gajillionth
|
||||
"ekyakafukunya", # bazillionth
|
||||
]
|
||||
|
||||
|
||||
def like_num(text):
|
||||
if text.startswith(("+", "-", "±", "~")):
|
||||
text = text[1:]
|
||||
text = text.replace(",", "").replace(".", "")
|
||||
if text.isdigit():
|
||||
return True
|
||||
if text.count("/") == 1:
|
||||
num, denom = text.split("/")
|
||||
if num.isdigit() and denom.isdigit():
|
||||
return True
|
||||
text_lower = text.lower()
|
||||
if text_lower in _num_words:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
LEX_ATTRS = {LIKE_NUM: like_num}
|
||||
19
spacy/lang/lg/punctuation.py
Normal file
19
spacy/lang/lg/punctuation.py
Normal file
|
|
@ -0,0 +1,19 @@
|
|||
from ..char_classes import LIST_ELLIPSES, LIST_ICONS, HYPHENS
|
||||
from ..char_classes import CONCAT_QUOTES, ALPHA_LOWER, ALPHA_UPPER, ALPHA
|
||||
|
||||
_infixes = (
|
||||
LIST_ELLIPSES
|
||||
+ LIST_ICONS
|
||||
+ [
|
||||
r"(?<=[0-9])[+\-\*^](?=[0-9-])",
|
||||
r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
|
||||
al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
|
||||
),
|
||||
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
|
||||
r"(?<=[{a}0-9])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
|
||||
r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
TOKENIZER_INFIXES = _infixes
|
||||
19
spacy/lang/lg/stop_words.py
Normal file
19
spacy/lang/lg/stop_words.py
Normal file
|
|
@ -0,0 +1,19 @@
|
|||
STOP_WORDS = set(
|
||||
"""
|
||||
abadde abalala abamu abangi abava ajja ali alina ani anti ateekeddwa atewamu
|
||||
atya awamu aweebwa ayinza ba baali babadde babalina bajja
|
||||
bajjanewankubade bali balina bandi bangi bano bateekeddwa baweebwa bayina bebombi beera bibye
|
||||
bimu bingi bino bo bokka bonna buli bulijjo bulungi bwabwe bwaffe bwayo bwe bwonna bya byabwe
|
||||
byaffe byebimu byonna ddaa ddala ddi e ebimu ebiri ebweruobulungi ebyo edda ejja ekirala ekyo
|
||||
endala engeri ennyo era erimu erina ffe ffenna ga gujja gumu gunno guno gwa gwe kaseera kati
|
||||
kennyini ki kiki kikino kikye kikyo kino kirungi kki ku kubangabyombi kubangaolwokuba kudda
|
||||
kuva kuwa kwegamba kyaffe kye kyekimuoyo kyekyo kyonna leero liryo lwa lwaki lyabwezaabwe
|
||||
lyaffe lyange mbadde mingi mpozzi mu mulinaoyina munda mwegyabwe nolwekyo nabadde nabo nandiyagadde
|
||||
nandiye nanti naye ne nedda neera nga nnyingi nnyini nnyinza nnyo nti nyinza nze oba ojja okudda
|
||||
okugenda okuggyako okutuusa okuva okuwa oli olina oluvannyuma olwekyobuva omuli ono osobola otya
|
||||
oyina oyo seetaaga si sinakindi singa talina tayina tebaali tebaalina tebayina terina tetulina
|
||||
tetuteekeddwa tewali teyalina teyayina tolina tu tuyina tulina tuyina twafuna twetaaga wa wabula
|
||||
wabweru wadde waggulunnina wakati waliwobangi waliyo wandi wange wano wansi weebwa yabadde yaffe
|
||||
ye yenna yennyini yina yonna ziba zijja zonna
|
||||
""".split()
|
||||
)
|
||||
|
|
@ -40,6 +40,7 @@ def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
|
|||
span_label = doc.vocab.strings.add("NP")
|
||||
|
||||
# Only NOUNS and PRONOUNS matter
|
||||
end_span = -1
|
||||
for i, word in enumerate(filter(lambda x: x.pos in [PRON, NOUN], doclike)):
|
||||
# For NOUNS
|
||||
# Pick children from syntactic parse (only those with certain dependencies)
|
||||
|
|
@ -58,15 +59,17 @@ def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
|
|||
children_i = [c.i for c in children] + [word.i]
|
||||
|
||||
start_span = min(children_i)
|
||||
end_span = max(children_i) + 1
|
||||
yield start_span, end_span, span_label
|
||||
if start_span >= end_span:
|
||||
end_span = max(children_i) + 1
|
||||
yield start_span, end_span, span_label
|
||||
|
||||
# PRONOUNS only if it is the subject of a verb
|
||||
elif word.pos == PRON:
|
||||
if word.dep in pronoun_deps:
|
||||
start_span = word.i
|
||||
end_span = word.i + 1
|
||||
yield start_span, end_span, span_label
|
||||
if start_span >= end_span:
|
||||
end_span = word.i + 1
|
||||
yield start_span, end_span, span_label
|
||||
|
||||
|
||||
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from typing import Iterator, Optional, Any, Dict, Callable, Iterable, Collection
|
||||
from typing import Iterator, Optional, Any, Dict, Callable, Iterable
|
||||
from typing import Union, Tuple, List, Set, Pattern, Sequence
|
||||
from typing import NoReturn, TYPE_CHECKING, TypeVar, cast, overload
|
||||
|
||||
|
|
@ -10,6 +10,7 @@ from contextlib import contextmanager
|
|||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
import warnings
|
||||
|
||||
from thinc.api import get_current_ops, Config, CupyOps, Optimizer
|
||||
import srsly
|
||||
import multiprocessing as mp
|
||||
|
|
@ -24,7 +25,7 @@ from .pipe_analysis import validate_attrs, analyze_pipes, print_pipe_analysis
|
|||
from .training import Example, validate_examples
|
||||
from .training.initialize import init_vocab, init_tok2vec
|
||||
from .scorer import Scorer
|
||||
from .util import registry, SimpleFrozenList, _pipe, raise_error
|
||||
from .util import registry, SimpleFrozenList, _pipe, raise_error, _DEFAULT_EMPTY_PIPES
|
||||
from .util import SimpleFrozenDict, combine_score_weights, CONFIG_SECTION_ORDER
|
||||
from .util import warn_if_jupyter_cupy
|
||||
from .lang.tokenizer_exceptions import URL_MATCH, BASE_EXCEPTIONS
|
||||
|
|
@ -465,6 +466,8 @@ class Language:
|
|||
"""
|
||||
if not isinstance(name, str):
|
||||
raise ValueError(Errors.E963.format(decorator="factory"))
|
||||
if "." in name:
|
||||
raise ValueError(Errors.E853.format(name=name))
|
||||
if not isinstance(default_config, dict):
|
||||
err = Errors.E962.format(
|
||||
style="default config", name=name, cfg_type=type(default_config)
|
||||
|
|
@ -543,8 +546,11 @@ class Language:
|
|||
|
||||
DOCS: https://spacy.io/api/language#component
|
||||
"""
|
||||
if name is not None and not isinstance(name, str):
|
||||
raise ValueError(Errors.E963.format(decorator="component"))
|
||||
if name is not None:
|
||||
if not isinstance(name, str):
|
||||
raise ValueError(Errors.E963.format(decorator="component"))
|
||||
if "." in name:
|
||||
raise ValueError(Errors.E853.format(name=name))
|
||||
component_name = name if name is not None else util.get_object_name(func)
|
||||
|
||||
def add_component(component_func: "Pipe") -> Callable:
|
||||
|
|
@ -1023,8 +1029,8 @@ class Language:
|
|||
raise ValueError(Errors.E109.format(name=name)) from e
|
||||
except Exception as e:
|
||||
error_handler(name, proc, [doc], e)
|
||||
if doc is None:
|
||||
raise ValueError(Errors.E005.format(name=name))
|
||||
if not isinstance(doc, Doc):
|
||||
raise ValueError(Errors.E005.format(name=name, returned_type=type(doc)))
|
||||
return doc
|
||||
|
||||
def disable_pipes(self, *names) -> "DisabledPipes":
|
||||
|
|
@ -1058,7 +1064,7 @@ class Language:
|
|||
"""
|
||||
if enable is None and disable is None:
|
||||
raise ValueError(Errors.E991)
|
||||
if disable is not None and isinstance(disable, str):
|
||||
if isinstance(disable, str):
|
||||
disable = [disable]
|
||||
if enable is not None:
|
||||
if isinstance(enable, str):
|
||||
|
|
@ -1693,9 +1699,9 @@ class Language:
|
|||
config: Union[Dict[str, Any], Config] = {},
|
||||
*,
|
||||
vocab: Union[Vocab, bool] = True,
|
||||
disable: Iterable[str] = SimpleFrozenList(),
|
||||
enable: Iterable[str] = SimpleFrozenList(),
|
||||
exclude: Iterable[str] = SimpleFrozenList(),
|
||||
disable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
meta: Dict[str, Any] = SimpleFrozenDict(),
|
||||
auto_fill: bool = True,
|
||||
validate: bool = True,
|
||||
|
|
@ -1706,12 +1712,12 @@ class Language:
|
|||
|
||||
config (Dict[str, Any] / Config): The loaded config.
|
||||
vocab (Vocab): A Vocab object. If True, a vocab is created.
|
||||
disable (Iterable[str]): Names of pipeline components to disable.
|
||||
disable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to disable.
|
||||
Disabled pipes will be loaded but they won't be run unless you
|
||||
explicitly enable them by calling nlp.enable_pipe.
|
||||
enable (Iterable[str]): Names of pipeline components to enable. All other
|
||||
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable. All other
|
||||
pipes will be disabled (and can be enabled using `nlp.enable_pipe`).
|
||||
exclude (Iterable[str]): Names of pipeline components to exclude.
|
||||
exclude (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to exclude.
|
||||
Excluded components won't be loaded.
|
||||
meta (Dict[str, Any]): Meta overrides for nlp.meta.
|
||||
auto_fill (bool): Automatically fill in missing values in config based
|
||||
|
|
@ -1866,9 +1872,38 @@ class Language:
|
|||
nlp.vocab.from_bytes(vocab_b)
|
||||
|
||||
# Resolve disabled/enabled settings.
|
||||
if isinstance(disable, str):
|
||||
disable = [disable]
|
||||
if isinstance(enable, str):
|
||||
enable = [enable]
|
||||
if isinstance(exclude, str):
|
||||
exclude = [exclude]
|
||||
|
||||
def fetch_pipes_status(value: Iterable[str], key: str) -> Iterable[str]:
|
||||
"""Fetch value for `enable` or `disable` w.r.t. the specified config and passed arguments passed to
|
||||
.load(). If both arguments and config specified values for this field, the passed arguments take precedence
|
||||
and a warning is printed.
|
||||
value (Iterable[str]): Passed value for `enable` or `disable`.
|
||||
key (str): Key for field in config (either "enabled" or "disabled").
|
||||
RETURN (Iterable[str]):
|
||||
"""
|
||||
# We assume that no argument was passed if the value is the specified default value.
|
||||
if id(value) == id(_DEFAULT_EMPTY_PIPES):
|
||||
return config["nlp"].get(key, [])
|
||||
else:
|
||||
if len(config["nlp"].get(key, [])):
|
||||
warnings.warn(
|
||||
Warnings.W123.format(
|
||||
arg=key[:-1],
|
||||
arg_value=value,
|
||||
config_value=config["nlp"][key],
|
||||
)
|
||||
)
|
||||
return value
|
||||
|
||||
disabled_pipes = cls._resolve_component_status(
|
||||
[*config["nlp"]["disabled"], *disable],
|
||||
[*config["nlp"].get("enabled", []), *enable],
|
||||
fetch_pipes_status(disable, "disabled"),
|
||||
fetch_pipes_status(enable, "enabled"),
|
||||
config["nlp"]["pipeline"],
|
||||
)
|
||||
nlp._disabled = set(p for p in disabled_pipes if p not in exclude)
|
||||
|
|
@ -2026,37 +2061,34 @@ class Language:
|
|||
|
||||
@staticmethod
|
||||
def _resolve_component_status(
|
||||
disable: Iterable[str], enable: Iterable[str], pipe_names: Collection[str]
|
||||
disable: Union[str, Iterable[str]],
|
||||
enable: Union[str, Iterable[str]],
|
||||
pipe_names: Iterable[str],
|
||||
) -> Tuple[str, ...]:
|
||||
"""Derives whether (1) `disable` and `enable` values are consistent and (2)
|
||||
resolves those to a single set of disabled components. Raises an error in
|
||||
case of inconsistency.
|
||||
|
||||
disable (Iterable[str]): Names of components or serialization fields to disable.
|
||||
enable (Iterable[str]): Names of pipeline components to enable.
|
||||
disable (Union[str, Iterable[str]]): Name(s) of component(s) or serialization fields to disable.
|
||||
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable.
|
||||
pipe_names (Iterable[str]): Names of all pipeline components.
|
||||
|
||||
RETURNS (Tuple[str, ...]): Names of components to exclude from pipeline w.r.t.
|
||||
specified includes and excludes.
|
||||
"""
|
||||
|
||||
if disable is not None and isinstance(disable, str):
|
||||
if isinstance(disable, str):
|
||||
disable = [disable]
|
||||
to_disable = disable
|
||||
|
||||
if enable:
|
||||
if isinstance(enable, str):
|
||||
enable = [enable]
|
||||
to_disable = [
|
||||
pipe_name for pipe_name in pipe_names if pipe_name not in enable
|
||||
]
|
||||
if disable and disable != to_disable:
|
||||
raise ValueError(
|
||||
Errors.E1042.format(
|
||||
arg1="enable",
|
||||
arg2="disable",
|
||||
arg1_values=enable,
|
||||
arg2_values=disable,
|
||||
)
|
||||
)
|
||||
raise ValueError(Errors.E1042.format(enable=enable, disable=disable))
|
||||
|
||||
return tuple(to_disable)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
from .matcher import Matcher
|
||||
from .phrasematcher import PhraseMatcher
|
||||
from .dependencymatcher import DependencyMatcher
|
||||
from .levenshtein import levenshtein
|
||||
|
||||
__all__ = ["Matcher", "PhraseMatcher", "DependencyMatcher"]
|
||||
__all__ = ["Matcher", "PhraseMatcher", "DependencyMatcher", "levenshtein"]
|
||||
|
|
|
|||
|
|
@ -82,6 +82,10 @@ cdef class DependencyMatcher:
|
|||
"$-": self._imm_left_sib,
|
||||
"$++": self._right_sib,
|
||||
"$--": self._left_sib,
|
||||
">++": self._right_child,
|
||||
">--": self._left_child,
|
||||
"<++": self._right_parent,
|
||||
"<--": self._left_parent,
|
||||
}
|
||||
|
||||
def __reduce__(self):
|
||||
|
|
@ -423,6 +427,22 @@ cdef class DependencyMatcher:
|
|||
def _left_sib(self, doc, node):
|
||||
return [doc[child.i] for child in doc[node].head.children if child.i < node]
|
||||
|
||||
def _right_child(self, doc, node):
|
||||
return [doc[child.i] for child in doc[node].children if child.i > node]
|
||||
|
||||
def _left_child(self, doc, node):
|
||||
return [doc[child.i] for child in doc[node].children if child.i < node]
|
||||
|
||||
def _right_parent(self, doc, node):
|
||||
if doc[node].head.i > node:
|
||||
return [doc[node].head]
|
||||
return []
|
||||
|
||||
def _left_parent(self, doc, node):
|
||||
if doc[node].head.i < node:
|
||||
return [doc[node].head]
|
||||
return []
|
||||
|
||||
def _normalize_key(self, key):
|
||||
if isinstance(key, str):
|
||||
return self.vocab.strings.add(key)
|
||||
|
|
|
|||
15
spacy/matcher/levenshtein.pyx
Normal file
15
spacy/matcher/levenshtein.pyx
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
# cython: profile=True, binding=True, infer_types=True
|
||||
from cpython.object cimport PyObject
|
||||
from libc.stdint cimport int64_t
|
||||
|
||||
from typing import Optional
|
||||
|
||||
|
||||
cdef extern from "polyleven.c":
|
||||
int64_t polyleven(PyObject *o1, PyObject *o2, int64_t k)
|
||||
|
||||
|
||||
cpdef int64_t levenshtein(a: str, b: str, k: Optional[int] = None):
|
||||
if k is None:
|
||||
k = -1
|
||||
return polyleven(<PyObject*>a, <PyObject*>b, k)
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
# cython: infer_types=True, cython: profile=True
|
||||
from typing import List
|
||||
from typing import List, Iterable
|
||||
|
||||
from libcpp.vector cimport vector
|
||||
from libc.stdint cimport int32_t, int8_t
|
||||
|
|
@ -867,20 +867,27 @@ class _SetPredicate:
|
|||
|
||||
def __call__(self, Token token):
|
||||
if self.is_extension:
|
||||
value = get_string_id(token._.get(self.attr))
|
||||
value = token._.get(self.attr)
|
||||
else:
|
||||
value = get_token_attr_for_matcher(token.c, self.attr)
|
||||
|
||||
if self.predicate in ("IS_SUBSET", "IS_SUPERSET", "INTERSECTS"):
|
||||
if self.predicate in ("IN", "NOT_IN"):
|
||||
if isinstance(value, (str, int)):
|
||||
value = get_string_id(value)
|
||||
else:
|
||||
return False
|
||||
elif self.predicate in ("IS_SUBSET", "IS_SUPERSET", "INTERSECTS"):
|
||||
# ensure that all values are enclosed in a set
|
||||
if self.attr == MORPH:
|
||||
# break up MORPH into individual Feat=Val values
|
||||
value = set(get_string_id(v) for v in MorphAnalysis.from_id(self.vocab, value))
|
||||
elif isinstance(value, (str, int)):
|
||||
value = set((get_string_id(value),))
|
||||
elif isinstance(value, Iterable) and all(isinstance(v, (str, int)) for v in value):
|
||||
value = set(get_string_id(v) for v in value)
|
||||
else:
|
||||
# treat a single value as a list
|
||||
if isinstance(value, (str, int)):
|
||||
value = set([get_string_id(value)])
|
||||
else:
|
||||
value = set(get_string_id(v) for v in value)
|
||||
return False
|
||||
|
||||
if self.predicate == "IN":
|
||||
return value in self.value
|
||||
elif self.predicate == "NOT_IN":
|
||||
|
|
|
|||
384
spacy/matcher/polyleven.c
Normal file
384
spacy/matcher/polyleven.c
Normal file
|
|
@ -0,0 +1,384 @@
|
|||
/*
|
||||
* Adapted from Polyleven (https://ceptord.net/)
|
||||
*
|
||||
* Source: https://github.com/fujimotos/polyleven/blob/c3f95a080626c5652f0151a2e449963288ccae84/polyleven.c
|
||||
*
|
||||
* Copyright (c) 2021 Fujimoto Seiji <fujimoto@ceptord.net>
|
||||
* Copyright (c) 2021 Max Bachmann <kontakt@maxbachmann.de>
|
||||
* Copyright (c) 2022 Nick Mazuk
|
||||
* Copyright (c) 2022 Michael Weiss <code@mweiss.ch>
|
||||
*
|
||||
* Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
* of this software and associated documentation files (the "Software"), to deal
|
||||
* in the Software without restriction, including without limitation the rights
|
||||
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
* copies of the Software, and to permit persons to whom the Software is
|
||||
* furnished to do so, subject to the following conditions:
|
||||
*
|
||||
* The above copyright notice and this permission notice shall be included in all
|
||||
* copies or substantial portions of the Software.
|
||||
*
|
||||
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
* SOFTWARE.
|
||||
*/
|
||||
|
||||
#include <Python.h>
|
||||
#include <stdint.h>
|
||||
|
||||
#define MIN(a,b) ((a) < (b) ? (a) : (b))
|
||||
#define MAX(a,b) ((a) > (b) ? (a) : (b))
|
||||
#define CDIV(a,b) ((a) / (b) + ((a) % (b) > 0))
|
||||
#define BIT(i,n) (((i) >> (n)) & 1)
|
||||
#define FLIP(i,n) ((i) ^ ((uint64_t) 1 << (n)))
|
||||
#define ISASCII(kd) ((kd) == PyUnicode_1BYTE_KIND)
|
||||
|
||||
/*
|
||||
* Bare bone of PyUnicode
|
||||
*/
|
||||
struct strbuf {
|
||||
void *ptr;
|
||||
int kind;
|
||||
int64_t len;
|
||||
};
|
||||
|
||||
static void strbuf_init(struct strbuf *s, PyObject *o)
|
||||
{
|
||||
s->ptr = PyUnicode_DATA(o);
|
||||
s->kind = PyUnicode_KIND(o);
|
||||
s->len = PyUnicode_GET_LENGTH(o);
|
||||
}
|
||||
|
||||
#define strbuf_read(s, i) PyUnicode_READ((s)->kind, (s)->ptr, (i))
|
||||
|
||||
/*
|
||||
* An encoded mbleven model table.
|
||||
*
|
||||
* Each 8-bit integer represents an edit sequence, with using two
|
||||
* bits for a single operation.
|
||||
*
|
||||
* 01 = DELETE, 10 = INSERT, 11 = REPLACE
|
||||
*
|
||||
* For example, 13 is '1101' in binary notation, so it means
|
||||
* DELETE + REPLACE.
|
||||
*/
|
||||
static const uint8_t MBLEVEN_MATRIX[] = {
|
||||
3, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 0, 0, 0, 0, 0, 0, 0,
|
||||
15, 9, 6, 0, 0, 0, 0, 0,
|
||||
13, 7, 0, 0, 0, 0, 0, 0,
|
||||
5, 0, 0, 0, 0, 0, 0, 0,
|
||||
63, 39, 45, 57, 54, 30, 27, 0,
|
||||
61, 55, 31, 37, 25, 22, 0, 0,
|
||||
53, 29, 23, 0, 0, 0, 0, 0,
|
||||
21, 0, 0, 0, 0, 0, 0, 0,
|
||||
};
|
||||
|
||||
#define MBLEVEN_MATRIX_GET(k, d) ((((k) + (k) * (k)) / 2 - 1) + (d)) * 8
|
||||
|
||||
static int64_t mbleven_ascii(char *s1, int64_t len1,
|
||||
char *s2, int64_t len2, int k)
|
||||
{
|
||||
int pos;
|
||||
uint8_t m;
|
||||
int64_t i, j, c, r;
|
||||
|
||||
pos = MBLEVEN_MATRIX_GET(k, len1 - len2);
|
||||
r = k + 1;
|
||||
|
||||
while (MBLEVEN_MATRIX[pos]) {
|
||||
m = MBLEVEN_MATRIX[pos++];
|
||||
i = j = c = 0;
|
||||
while (i < len1 && j < len2) {
|
||||
if (s1[i] != s2[j]) {
|
||||
c++;
|
||||
if (!m) break;
|
||||
if (m & 1) i++;
|
||||
if (m & 2) j++;
|
||||
m >>= 2;
|
||||
} else {
|
||||
i++;
|
||||
j++;
|
||||
}
|
||||
}
|
||||
c += (len1 - i) + (len2 - j);
|
||||
r = MIN(r, c);
|
||||
if (r < 2) {
|
||||
return r;
|
||||
}
|
||||
}
|
||||
return r;
|
||||
}
|
||||
|
||||
static int64_t mbleven(PyObject *o1, PyObject *o2, int64_t k)
|
||||
{
|
||||
int pos;
|
||||
uint8_t m;
|
||||
int64_t i, j, c, r;
|
||||
struct strbuf s1, s2;
|
||||
|
||||
strbuf_init(&s1, o1);
|
||||
strbuf_init(&s2, o2);
|
||||
|
||||
if (s1.len < s2.len)
|
||||
return mbleven(o2, o1, k);
|
||||
|
||||
if (k > 3)
|
||||
return -1;
|
||||
|
||||
if (k < s1.len - s2.len)
|
||||
return k + 1;
|
||||
|
||||
if (ISASCII(s1.kind) && ISASCII(s2.kind))
|
||||
return mbleven_ascii(s1.ptr, s1.len, s2.ptr, s2.len, k);
|
||||
|
||||
pos = MBLEVEN_MATRIX_GET(k, s1.len - s2.len);
|
||||
r = k + 1;
|
||||
|
||||
while (MBLEVEN_MATRIX[pos]) {
|
||||
m = MBLEVEN_MATRIX[pos++];
|
||||
i = j = c = 0;
|
||||
while (i < s1.len && j < s2.len) {
|
||||
if (strbuf_read(&s1, i) != strbuf_read(&s2, j)) {
|
||||
c++;
|
||||
if (!m) break;
|
||||
if (m & 1) i++;
|
||||
if (m & 2) j++;
|
||||
m >>= 2;
|
||||
} else {
|
||||
i++;
|
||||
j++;
|
||||
}
|
||||
}
|
||||
c += (s1.len - i) + (s2.len - j);
|
||||
r = MIN(r, c);
|
||||
if (r < 2) {
|
||||
return r;
|
||||
}
|
||||
}
|
||||
return r;
|
||||
}
|
||||
|
||||
/*
|
||||
* Data structure to store Peq (equality bit-vector).
|
||||
*/
|
||||
struct blockmap_entry {
|
||||
uint32_t key[128];
|
||||
uint64_t val[128];
|
||||
};
|
||||
|
||||
struct blockmap {
|
||||
int64_t nr;
|
||||
struct blockmap_entry *list;
|
||||
};
|
||||
|
||||
#define blockmap_key(c) ((c) | 0x80000000U)
|
||||
#define blockmap_hash(c) ((c) % 128)
|
||||
|
||||
static int blockmap_init(struct blockmap *map, struct strbuf *s)
|
||||
{
|
||||
int64_t i;
|
||||
struct blockmap_entry *be;
|
||||
uint32_t c, k;
|
||||
uint8_t h;
|
||||
|
||||
map->nr = CDIV(s->len, 64);
|
||||
map->list = calloc(1, map->nr * sizeof(struct blockmap_entry));
|
||||
if (map->list == NULL) {
|
||||
PyErr_NoMemory();
|
||||
return -1;
|
||||
}
|
||||
|
||||
for (i = 0; i < s->len; i++) {
|
||||
be = &(map->list[i / 64]);
|
||||
c = strbuf_read(s, i);
|
||||
h = blockmap_hash(c);
|
||||
k = blockmap_key(c);
|
||||
|
||||
while (be->key[h] && be->key[h] != k)
|
||||
h = blockmap_hash(h + 1);
|
||||
be->key[h] = k;
|
||||
be->val[h] |= (uint64_t) 1 << (i % 64);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
static void blockmap_clear(struct blockmap *map)
|
||||
{
|
||||
if (map->list)
|
||||
free(map->list);
|
||||
map->list = NULL;
|
||||
map->nr = 0;
|
||||
}
|
||||
|
||||
static uint64_t blockmap_get(struct blockmap *map, int block, uint32_t c)
|
||||
{
|
||||
struct blockmap_entry *be;
|
||||
uint8_t h;
|
||||
uint32_t k;
|
||||
|
||||
h = blockmap_hash(c);
|
||||
k = blockmap_key(c);
|
||||
|
||||
be = &(map->list[block]);
|
||||
while (be->key[h] && be->key[h] != k)
|
||||
h = blockmap_hash(h + 1);
|
||||
return be->key[h] == k ? be->val[h] : 0;
|
||||
}
|
||||
|
||||
/*
|
||||
* Myers' bit-parallel algorithm
|
||||
*
|
||||
* See: G. Myers. "A fast bit-vector algorithm for approximate string
|
||||
* matching based on dynamic programming." Journal of the ACM, 1999.
|
||||
*/
|
||||
static int64_t myers1999_block(struct strbuf *s1, struct strbuf *s2,
|
||||
struct blockmap *map)
|
||||
{
|
||||
uint64_t Eq, Xv, Xh, Ph, Mh, Pv, Mv, Last;
|
||||
uint64_t *Mhc, *Phc;
|
||||
int64_t i, b, hsize, vsize, Score;
|
||||
uint8_t Pb, Mb;
|
||||
|
||||
hsize = CDIV(s1->len, 64);
|
||||
vsize = CDIV(s2->len, 64);
|
||||
Score = s2->len;
|
||||
|
||||
Phc = malloc(hsize * 2 * sizeof(uint64_t));
|
||||
if (Phc == NULL) {
|
||||
PyErr_NoMemory();
|
||||
return -1;
|
||||
}
|
||||
Mhc = Phc + hsize;
|
||||
memset(Phc, -1, hsize * sizeof(uint64_t));
|
||||
memset(Mhc, 0, hsize * sizeof(uint64_t));
|
||||
Last = (uint64_t)1 << ((s2->len - 1) % 64);
|
||||
|
||||
for (b = 0; b < vsize; b++) {
|
||||
Mv = 0;
|
||||
Pv = (uint64_t) -1;
|
||||
Score = s2->len;
|
||||
|
||||
for (i = 0; i < s1->len; i++) {
|
||||
Eq = blockmap_get(map, b, strbuf_read(s1, i));
|
||||
|
||||
Pb = BIT(Phc[i / 64], i % 64);
|
||||
Mb = BIT(Mhc[i / 64], i % 64);
|
||||
|
||||
Xv = Eq | Mv;
|
||||
Xh = ((((Eq | Mb) & Pv) + Pv) ^ Pv) | Eq | Mb;
|
||||
|
||||
Ph = Mv | ~ (Xh | Pv);
|
||||
Mh = Pv & Xh;
|
||||
|
||||
if (Ph & Last) Score++;
|
||||
if (Mh & Last) Score--;
|
||||
|
||||
if ((Ph >> 63) ^ Pb)
|
||||
Phc[i / 64] = FLIP(Phc[i / 64], i % 64);
|
||||
|
||||
if ((Mh >> 63) ^ Mb)
|
||||
Mhc[i / 64] = FLIP(Mhc[i / 64], i % 64);
|
||||
|
||||
Ph = (Ph << 1) | Pb;
|
||||
Mh = (Mh << 1) | Mb;
|
||||
|
||||
Pv = Mh | ~ (Xv | Ph);
|
||||
Mv = Ph & Xv;
|
||||
}
|
||||
}
|
||||
free(Phc);
|
||||
return Score;
|
||||
}
|
||||
|
||||
static int64_t myers1999_simple(uint8_t *s1, int64_t len1, uint8_t *s2, int64_t len2)
|
||||
{
|
||||
uint64_t Peq[256];
|
||||
uint64_t Eq, Xv, Xh, Ph, Mh, Pv, Mv, Last;
|
||||
int64_t i;
|
||||
int64_t Score = len2;
|
||||
|
||||
memset(Peq, 0, sizeof(Peq));
|
||||
|
||||
for (i = 0; i < len2; i++)
|
||||
Peq[s2[i]] |= (uint64_t) 1 << i;
|
||||
|
||||
Mv = 0;
|
||||
Pv = (uint64_t) -1;
|
||||
Last = (uint64_t) 1 << (len2 - 1);
|
||||
|
||||
for (i = 0; i < len1; i++) {
|
||||
Eq = Peq[s1[i]];
|
||||
|
||||
Xv = Eq | Mv;
|
||||
Xh = (((Eq & Pv) + Pv) ^ Pv) | Eq;
|
||||
|
||||
Ph = Mv | ~ (Xh | Pv);
|
||||
Mh = Pv & Xh;
|
||||
|
||||
if (Ph & Last) Score++;
|
||||
if (Mh & Last) Score--;
|
||||
|
||||
Ph = (Ph << 1) | 1;
|
||||
Mh = (Mh << 1);
|
||||
|
||||
Pv = Mh | ~ (Xv | Ph);
|
||||
Mv = Ph & Xv;
|
||||
}
|
||||
return Score;
|
||||
}
|
||||
|
||||
static int64_t myers1999(PyObject *o1, PyObject *o2)
|
||||
{
|
||||
struct strbuf s1, s2;
|
||||
struct blockmap map;
|
||||
int64_t ret;
|
||||
|
||||
strbuf_init(&s1, o1);
|
||||
strbuf_init(&s2, o2);
|
||||
|
||||
if (s1.len < s2.len)
|
||||
return myers1999(o2, o1);
|
||||
|
||||
if (ISASCII(s1.kind) && ISASCII(s2.kind) && s2.len < 65)
|
||||
return myers1999_simple(s1.ptr, s1.len, s2.ptr, s2.len);
|
||||
|
||||
if (blockmap_init(&map, &s2))
|
||||
return -1;
|
||||
|
||||
ret = myers1999_block(&s1, &s2, &map);
|
||||
blockmap_clear(&map);
|
||||
return ret;
|
||||
}
|
||||
|
||||
/*
|
||||
* Interface functions
|
||||
*/
|
||||
static int64_t polyleven(PyObject *o1, PyObject *o2, int64_t k)
|
||||
{
|
||||
int64_t len1, len2;
|
||||
|
||||
len1 = PyUnicode_GET_LENGTH(o1);
|
||||
len2 = PyUnicode_GET_LENGTH(o2);
|
||||
|
||||
if (len1 < len2)
|
||||
return polyleven(o2, o1, k);
|
||||
|
||||
if (k == 0)
|
||||
return PyUnicode_Compare(o1, o2) ? 1 : 0;
|
||||
|
||||
if (0 < k && k < len1 - len2)
|
||||
return k + 1;
|
||||
|
||||
if (len2 == 0)
|
||||
return len1;
|
||||
|
||||
if (0 < k && k < 4)
|
||||
return mbleven(o1, o2, k);
|
||||
|
||||
return myers1999(o1, o2);
|
||||
}
|
||||
|
|
@ -26,7 +26,11 @@ def forward(model, X, is_train):
|
|||
Yf = model.ops.alloc2f(X.shape[0] + 1, nF * nO * nP, zeros=False)
|
||||
model.ops.gemm(X, W.reshape((nF * nO * nP, nI)), trans2=True, out=Yf[1:])
|
||||
Yf = Yf.reshape((Yf.shape[0], nF, nO, nP))
|
||||
Yf[0] = model.get_param("pad")
|
||||
|
||||
# Set padding. Padding has shape (1, nF, nO, nP). Unfortunately, we cannot
|
||||
# change its shape to (nF, nO, nP) without breaking existing models. So
|
||||
# we'll squeeze the first dimension here.
|
||||
Yf[0] = model.ops.xp.squeeze(model.get_param("pad"), 0)
|
||||
|
||||
def backward(dY_ids):
|
||||
# This backprop is particularly tricky, because we get back a different
|
||||
|
|
|
|||
|
|
@ -89,11 +89,14 @@ def pipes_with_nvtx_range(
|
|||
types.MethodType(nvtx_range_wrapper_for_pipe_method, pipe), func
|
||||
)
|
||||
|
||||
# Try to preserve the original function signature.
|
||||
# We need to preserve the original function signature so that
|
||||
# the original parameters are passed to pydantic for validation downstream.
|
||||
try:
|
||||
wrapped_func.__signature__ = inspect.signature(func) # type: ignore
|
||||
except:
|
||||
pass
|
||||
# Can fail for Cython methods that do not have bindings.
|
||||
warnings.warn(Warnings.W122.format(method=name, pipe=pipe.name))
|
||||
continue
|
||||
|
||||
try:
|
||||
setattr(
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
import warnings
|
||||
from typing import Optional, Union, List, Dict, Tuple, Iterable, Any, Callable, Sequence
|
||||
from typing import cast
|
||||
import warnings
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
import srsly
|
||||
|
|
@ -317,7 +316,7 @@ class EntityRuler(Pipe):
|
|||
phrase_pattern["id"] = ent_id
|
||||
phrase_patterns.append(phrase_pattern)
|
||||
for entry in token_patterns + phrase_patterns: # type: ignore[operator]
|
||||
label = entry["label"]
|
||||
label = entry["label"] # type: ignore
|
||||
if "id" in entry:
|
||||
ent_label = label
|
||||
label = self._create_label(label, entry["id"])
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# cython: infer_types=True, profile=True
|
||||
# cython: infer_types=True, profile=True, binding=True
|
||||
from typing import Optional, Tuple, Iterable, Iterator, Callable, Union, Dict
|
||||
import srsly
|
||||
import warnings
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# cython: infer_types=True, profile=True
|
||||
# cython: infer_types=True, profile=True, binding=True
|
||||
from typing import Iterable, Iterator, Optional, Dict, Tuple, Callable
|
||||
import srsly
|
||||
from thinc.api import set_dropout_rate, Model, Optimizer
|
||||
|
|
|
|||
|
|
@ -207,7 +207,7 @@ class TokenPatternOperatorSimple(str, Enum):
|
|||
|
||||
|
||||
class TokenPatternOperatorMinMax(ConstrainedStr):
|
||||
regex = re.compile("^({\d+}|{\d+,\d*}|{\d*,\d+})$")
|
||||
regex = re.compile(r"^({\d+}|{\d+,\d*}|{\d*,\d+})$")
|
||||
|
||||
|
||||
TokenPatternOperator = Union[TokenPatternOperatorSimple, TokenPatternOperatorMinMax]
|
||||
|
|
@ -515,6 +515,14 @@ class DocJSONSchema(BaseModel):
|
|||
tokens: List[Dict[StrictStr, Union[StrictStr, StrictInt]]] = Field(
|
||||
..., title="Token information - ID, start, annotations"
|
||||
)
|
||||
_: Optional[Dict[StrictStr, Any]] = Field(
|
||||
None, title="Any custom data stored in the document's _ attribute"
|
||||
underscore_doc: Optional[Dict[StrictStr, Any]] = Field(
|
||||
None,
|
||||
title="Any custom data stored in the document's _ attribute",
|
||||
alias="_",
|
||||
)
|
||||
underscore_token: Optional[Dict[StrictStr, Dict[StrictStr, Any]]] = Field(
|
||||
None, title="Any custom data stored in the token's _ attribute"
|
||||
)
|
||||
underscore_span: Optional[Dict[StrictStr, Dict[StrictStr, Any]]] = Field(
|
||||
None, title="Any custom data stored in the span's _ attribute"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -256,11 +256,21 @@ def ko_tokenizer_tokenizer():
|
|||
return nlp.tokenizer
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def la_tokenizer():
|
||||
return get_lang_class("la")().tokenizer
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def lb_tokenizer():
|
||||
return get_lang_class("lb")().tokenizer
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def lg_tokenizer():
|
||||
return get_lang_class("lg")().tokenizer
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def lt_tokenizer():
|
||||
return get_lang_class("lt")().tokenizer
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import weakref
|
|||
import numpy
|
||||
from numpy.testing import assert_array_equal
|
||||
import pytest
|
||||
import warnings
|
||||
from thinc.api import NumpyOps, get_current_ops
|
||||
|
||||
from spacy.attrs import DEP, ENT_IOB, ENT_TYPE, HEAD, IS_ALPHA, MORPH, POS
|
||||
|
|
@ -81,6 +82,21 @@ def test_issue2396(en_vocab):
|
|||
assert (span.get_lca_matrix() == matrix).all()
|
||||
|
||||
|
||||
@pytest.mark.issue(11499)
|
||||
def test_init_args_unmodified(en_vocab):
|
||||
words = ["A", "sentence"]
|
||||
ents = ["B-TYPE1", ""]
|
||||
sent_starts = [True, False]
|
||||
Doc(
|
||||
vocab=en_vocab,
|
||||
words=words,
|
||||
ents=ents,
|
||||
sent_starts=sent_starts,
|
||||
)
|
||||
assert ents == ["B-TYPE1", ""]
|
||||
assert sent_starts == [True, False]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("text", ["-0.23", "+123,456", "±1"])
|
||||
@pytest.mark.parametrize("lang_cls", [English, MultiLanguage])
|
||||
@pytest.mark.issue(2782)
|
||||
|
|
@ -529,9 +545,9 @@ def test_doc_from_array_sent_starts(en_vocab):
|
|||
# no warning using default attrs
|
||||
attrs = doc._get_array_attrs()
|
||||
arr = doc.to_array(attrs)
|
||||
with pytest.warns(None) as record:
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("error")
|
||||
new_doc.from_array(attrs, arr)
|
||||
assert len(record) == 0
|
||||
# only SENT_START uses SENT_START
|
||||
attrs = [SENT_START]
|
||||
arr = doc.to_array(attrs)
|
||||
|
|
|
|||
|
|
@ -1,12 +1,15 @@
|
|||
import pytest
|
||||
import spacy
|
||||
from spacy import schemas
|
||||
from spacy.tokens import Doc, Span
|
||||
from spacy.tokens import Doc, Span, Token
|
||||
import srsly
|
||||
from .test_underscore import clean_underscore # noqa: F401
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def doc(en_vocab):
|
||||
words = ["c", "d", "e"]
|
||||
spaces = [True, True, True]
|
||||
pos = ["VERB", "NOUN", "NOUN"]
|
||||
tags = ["VBP", "NN", "NN"]
|
||||
heads = [0, 0, 1]
|
||||
|
|
@ -17,6 +20,7 @@ def doc(en_vocab):
|
|||
return Doc(
|
||||
en_vocab,
|
||||
words=words,
|
||||
spaces=spaces,
|
||||
pos=pos,
|
||||
tags=tags,
|
||||
heads=heads,
|
||||
|
|
@ -45,6 +49,47 @@ def doc_without_deps(en_vocab):
|
|||
)
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def doc_json():
|
||||
return {
|
||||
"text": "c d e ",
|
||||
"ents": [{"start": 2, "end": 3, "label": "ORG"}],
|
||||
"sents": [{"start": 0, "end": 5}],
|
||||
"tokens": [
|
||||
{
|
||||
"id": 0,
|
||||
"start": 0,
|
||||
"end": 1,
|
||||
"tag": "VBP",
|
||||
"pos": "VERB",
|
||||
"morph": "Feat1=A",
|
||||
"dep": "ROOT",
|
||||
"head": 0,
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"start": 2,
|
||||
"end": 3,
|
||||
"tag": "NN",
|
||||
"pos": "NOUN",
|
||||
"morph": "Feat1=B",
|
||||
"dep": "dobj",
|
||||
"head": 0,
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"start": 4,
|
||||
"end": 5,
|
||||
"tag": "NN",
|
||||
"pos": "NOUN",
|
||||
"morph": "Feat1=A|Feat2=D",
|
||||
"dep": "dobj",
|
||||
"head": 1,
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def test_doc_to_json(doc):
|
||||
json_doc = doc.to_json()
|
||||
assert json_doc["text"] == "c d e "
|
||||
|
|
@ -56,7 +101,8 @@ def test_doc_to_json(doc):
|
|||
assert json_doc["ents"][0]["start"] == 2 # character offset!
|
||||
assert json_doc["ents"][0]["end"] == 3 # character offset!
|
||||
assert json_doc["ents"][0]["label"] == "ORG"
|
||||
assert not schemas.validate(schemas.DocJSONSchema, json_doc)
|
||||
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
|
||||
assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
|
||||
|
||||
|
||||
def test_doc_to_json_underscore(doc):
|
||||
|
|
@ -64,11 +110,96 @@ def test_doc_to_json_underscore(doc):
|
|||
Doc.set_extension("json_test2", default=False)
|
||||
doc._.json_test1 = "hello world"
|
||||
doc._.json_test2 = [1, 2, 3]
|
||||
|
||||
json_doc = doc.to_json(underscore=["json_test1", "json_test2"])
|
||||
assert "_" in json_doc
|
||||
assert json_doc["_"]["json_test1"] == "hello world"
|
||||
assert json_doc["_"]["json_test2"] == [1, 2, 3]
|
||||
assert not schemas.validate(schemas.DocJSONSchema, json_doc)
|
||||
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
|
||||
assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
|
||||
|
||||
|
||||
def test_doc_to_json_with_token_span_attributes(doc):
|
||||
Doc.set_extension("json_test1", default=False)
|
||||
Doc.set_extension("json_test2", default=False)
|
||||
Token.set_extension("token_test", default=False)
|
||||
Span.set_extension("span_test", default=False)
|
||||
|
||||
doc._.json_test1 = "hello world"
|
||||
doc._.json_test2 = [1, 2, 3]
|
||||
doc[0:1]._.span_test = "span_attribute"
|
||||
doc[0]._.token_test = 117
|
||||
doc.spans["span_group"] = [doc[0:1]]
|
||||
json_doc = doc.to_json(
|
||||
underscore=["json_test1", "json_test2", "token_test", "span_test"]
|
||||
)
|
||||
|
||||
assert "_" in json_doc
|
||||
assert json_doc["_"]["json_test1"] == "hello world"
|
||||
assert json_doc["_"]["json_test2"] == [1, 2, 3]
|
||||
assert "underscore_token" in json_doc
|
||||
assert "underscore_span" in json_doc
|
||||
assert json_doc["underscore_token"]["token_test"]["value"] == 117
|
||||
assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
|
||||
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
|
||||
assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
|
||||
|
||||
|
||||
def test_doc_to_json_with_custom_user_data(doc):
|
||||
Doc.set_extension("json_test", default=False)
|
||||
Token.set_extension("token_test", default=False)
|
||||
Span.set_extension("span_test", default=False)
|
||||
|
||||
doc._.json_test = "hello world"
|
||||
doc[0:1]._.span_test = "span_attribute"
|
||||
doc[0]._.token_test = 117
|
||||
json_doc = doc.to_json(underscore=["json_test", "token_test", "span_test"])
|
||||
doc.user_data["user_data_test"] = 10
|
||||
doc.user_data[("user_data_test2", True)] = 10
|
||||
|
||||
assert "_" in json_doc
|
||||
assert json_doc["_"]["json_test"] == "hello world"
|
||||
assert "underscore_token" in json_doc
|
||||
assert "underscore_span" in json_doc
|
||||
assert json_doc["underscore_token"]["token_test"]["value"] == 117
|
||||
assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
|
||||
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
|
||||
assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
|
||||
|
||||
|
||||
def test_doc_to_json_with_token_span_same_identifier(doc):
|
||||
Doc.set_extension("my_ext", default=False)
|
||||
Token.set_extension("my_ext", default=False)
|
||||
Span.set_extension("my_ext", default=False)
|
||||
|
||||
doc._.my_ext = "hello world"
|
||||
doc[0:1]._.my_ext = "span_attribute"
|
||||
doc[0]._.my_ext = 117
|
||||
json_doc = doc.to_json(underscore=["my_ext"])
|
||||
|
||||
assert "_" in json_doc
|
||||
assert json_doc["_"]["my_ext"] == "hello world"
|
||||
assert "underscore_token" in json_doc
|
||||
assert "underscore_span" in json_doc
|
||||
assert json_doc["underscore_token"]["my_ext"]["value"] == 117
|
||||
assert json_doc["underscore_span"]["my_ext"]["value"] == "span_attribute"
|
||||
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
|
||||
assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
|
||||
|
||||
|
||||
def test_doc_to_json_with_token_attributes_missing(doc):
|
||||
Token.set_extension("token_test", default=False)
|
||||
Span.set_extension("span_test", default=False)
|
||||
|
||||
doc[0:1]._.span_test = "span_attribute"
|
||||
doc[0]._.token_test = 117
|
||||
json_doc = doc.to_json(underscore=["span_test"])
|
||||
|
||||
assert "underscore_token" in json_doc
|
||||
assert "underscore_span" in json_doc
|
||||
assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
|
||||
assert "token_test" not in json_doc["underscore_token"]
|
||||
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
|
||||
|
||||
|
||||
def test_doc_to_json_underscore_error_attr(doc):
|
||||
|
|
@ -94,11 +225,29 @@ def test_doc_to_json_span(doc):
|
|||
assert len(json_doc["spans"]) == 1
|
||||
assert len(json_doc["spans"]["test"]) == 2
|
||||
assert json_doc["spans"]["test"][0]["start"] == 0
|
||||
assert not schemas.validate(schemas.DocJSONSchema, json_doc)
|
||||
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
|
||||
|
||||
|
||||
def test_json_to_doc(doc):
|
||||
new_doc = Doc(doc.vocab).from_json(doc.to_json(), validate=True)
|
||||
json_doc = doc.to_json()
|
||||
json_doc = srsly.json_loads(srsly.json_dumps(json_doc))
|
||||
new_doc = Doc(doc.vocab).from_json(json_doc, validate=True)
|
||||
assert new_doc.text == doc.text == "c d e "
|
||||
assert len(new_doc) == len(doc) == 3
|
||||
assert new_doc[0].pos == doc[0].pos
|
||||
assert new_doc[0].tag == doc[0].tag
|
||||
assert new_doc[0].dep == doc[0].dep
|
||||
assert new_doc[0].head.idx == doc[0].head.idx
|
||||
assert new_doc[0].lemma == doc[0].lemma
|
||||
assert len(new_doc.ents) == 1
|
||||
assert new_doc.ents[0].start == 1
|
||||
assert new_doc.ents[0].end == 2
|
||||
assert new_doc.ents[0].label_ == "ORG"
|
||||
assert doc.to_bytes() == new_doc.to_bytes()
|
||||
|
||||
|
||||
def test_json_to_doc_compat(doc, doc_json):
|
||||
new_doc = Doc(doc.vocab).from_json(doc_json, validate=True)
|
||||
new_tokens = [token for token in new_doc]
|
||||
assert new_doc.text == doc.text == "c d e "
|
||||
assert len(new_tokens) == len([token for token in doc]) == 3
|
||||
|
|
@ -114,11 +263,8 @@ def test_json_to_doc(doc):
|
|||
|
||||
|
||||
def test_json_to_doc_underscore(doc):
|
||||
if not Doc.has_extension("json_test1"):
|
||||
Doc.set_extension("json_test1", default=False)
|
||||
if not Doc.has_extension("json_test2"):
|
||||
Doc.set_extension("json_test2", default=False)
|
||||
|
||||
Doc.set_extension("json_test1", default=False)
|
||||
Doc.set_extension("json_test2", default=False)
|
||||
doc._.json_test1 = "hello world"
|
||||
doc._.json_test2 = [1, 2, 3]
|
||||
json_doc = doc.to_json(underscore=["json_test1", "json_test2"])
|
||||
|
|
@ -126,6 +272,34 @@ def test_json_to_doc_underscore(doc):
|
|||
assert all([new_doc.has_extension(f"json_test{i}") for i in range(1, 3)])
|
||||
assert new_doc._.json_test1 == "hello world"
|
||||
assert new_doc._.json_test2 == [1, 2, 3]
|
||||
assert doc.to_bytes() == new_doc.to_bytes()
|
||||
|
||||
|
||||
def test_json_to_doc_with_token_span_attributes(doc):
|
||||
Doc.set_extension("json_test1", default=False)
|
||||
Doc.set_extension("json_test2", default=False)
|
||||
Token.set_extension("token_test", default=False)
|
||||
Span.set_extension("span_test", default=False)
|
||||
doc._.json_test1 = "hello world"
|
||||
doc._.json_test2 = [1, 2, 3]
|
||||
doc[0:1]._.span_test = "span_attribute"
|
||||
doc[0]._.token_test = 117
|
||||
|
||||
json_doc = doc.to_json(
|
||||
underscore=["json_test1", "json_test2", "token_test", "span_test"]
|
||||
)
|
||||
json_doc = srsly.json_loads(srsly.json_dumps(json_doc))
|
||||
new_doc = Doc(doc.vocab).from_json(json_doc, validate=True)
|
||||
|
||||
assert all([new_doc.has_extension(f"json_test{i}") for i in range(1, 3)])
|
||||
assert new_doc._.json_test1 == "hello world"
|
||||
assert new_doc._.json_test2 == [1, 2, 3]
|
||||
assert new_doc[0]._.token_test == 117
|
||||
assert new_doc[0:1]._.span_test == "span_attribute"
|
||||
assert new_doc.user_data == doc.user_data
|
||||
assert new_doc.to_bytes(exclude=["user_data"]) == doc.to_bytes(
|
||||
exclude=["user_data"]
|
||||
)
|
||||
|
||||
|
||||
def test_json_to_doc_spans(doc):
|
||||
|
|
|
|||
0
spacy/tests/lang/la/__init__.py
Normal file
0
spacy/tests/lang/la/__init__.py
Normal file
8
spacy/tests/lang/la/test_exception.py
Normal file
8
spacy/tests/lang/la/test_exception.py
Normal file
|
|
@ -0,0 +1,8 @@
|
|||
import pytest
|
||||
|
||||
|
||||
def test_la_tokenizer_handles_exc_in_text(la_tokenizer):
|
||||
text = "scio te omnia facturum, ut nobiscum quam primum sis"
|
||||
tokens = la_tokenizer(text)
|
||||
assert len(tokens) == 11
|
||||
assert tokens[6].text == "nobis"
|
||||
35
spacy/tests/lang/la/test_text.py
Normal file
35
spacy/tests/lang/la/test_text.py
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
import pytest
|
||||
from spacy.lang.la.lex_attrs import like_num
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"text,match",
|
||||
[
|
||||
("IIII", True),
|
||||
("VI", True),
|
||||
("vi", True),
|
||||
("IV", True),
|
||||
("iv", True),
|
||||
("IX", True),
|
||||
("ix", True),
|
||||
("MMXXII", True),
|
||||
("0", True),
|
||||
("1", True),
|
||||
("quattuor", True),
|
||||
("decem", True),
|
||||
("tertius", True),
|
||||
("canis", False),
|
||||
("MMXX11", False),
|
||||
(",", False),
|
||||
],
|
||||
)
|
||||
def test_lex_attrs_like_number(la_tokenizer, text, match):
|
||||
tokens = la_tokenizer(text)
|
||||
assert len(tokens) == 1
|
||||
assert tokens[0].like_num == match
|
||||
|
||||
|
||||
@pytest.mark.parametrize("word", ["quinque"])
|
||||
def test_la_lex_attrs_capitals(word):
|
||||
assert like_num(word)
|
||||
assert like_num(word.upper())
|
||||
0
spacy/tests/lang/lg/__init__.py
Normal file
0
spacy/tests/lang/lg/__init__.py
Normal file
15
spacy/tests/lang/lg/test_tokenizer.py
Normal file
15
spacy/tests/lang/lg/test_tokenizer.py
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
import pytest
|
||||
|
||||
LG_BASIC_TOKENIZATION_TESTS = [
|
||||
(
|
||||
"Abooluganda ab’emmamba ababiri",
|
||||
["Abooluganda", "ab’emmamba", "ababiri"],
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("text,expected_tokens", LG_BASIC_TOKENIZATION_TESTS)
|
||||
def test_lg_tokenizer_basic(lg_tokenizer, text, expected_tokens):
|
||||
tokens = lg_tokenizer(text)
|
||||
token_list = [token.text for token in tokens if not token.is_space]
|
||||
assert expected_tokens == token_list
|
||||
|
|
@ -1,5 +1,6 @@
|
|||
from spacy.tokens import Doc
|
||||
import pytest
|
||||
from spacy.tokens import Doc
|
||||
from spacy.util import filter_spans
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -207,3 +208,18 @@ def test_chunking(nl_sample, nl_reference_chunking):
|
|||
"""
|
||||
chunks = [s.text.lower() for s in nl_sample.noun_chunks]
|
||||
assert chunks == nl_reference_chunking
|
||||
|
||||
|
||||
@pytest.mark.issue(10846)
|
||||
def test_no_overlapping_chunks(nl_vocab):
|
||||
# fmt: off
|
||||
doc = Doc(
|
||||
nl_vocab,
|
||||
words=["Dit", "programma", "wordt", "beschouwd", "als", "'s", "werelds", "eerste", "computerprogramma"],
|
||||
deps=["det", "nsubj:pass", "aux:pass", "ROOT", "mark", "det", "fixed", "amod", "xcomp"],
|
||||
heads=[1, 3, 3, 3, 8, 8, 5, 8, 3],
|
||||
pos=["DET", "NOUN", "AUX", "VERB", "SCONJ", "DET", "NOUN", "ADJ", "NOUN"],
|
||||
)
|
||||
# fmt: on
|
||||
chunks = list(doc.noun_chunks)
|
||||
assert filter_spans(chunks) == chunks
|
||||
|
|
|
|||
|
|
@ -2,6 +2,9 @@ import pytest
|
|||
from spacy.tokens import Doc
|
||||
|
||||
|
||||
pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning")
|
||||
|
||||
|
||||
def test_ru_doc_lemmatization(ru_lemmatizer):
|
||||
words = ["мама", "мыла", "раму"]
|
||||
pos = ["NOUN", "VERB", "NOUN"]
|
||||
|
|
|
|||
|
|
@ -1,6 +1,10 @@
|
|||
import pytest
|
||||
from spacy.tokens import Doc
|
||||
|
||||
|
||||
pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning")
|
||||
|
||||
|
||||
def test_uk_lemmatizer(uk_lemmatizer):
|
||||
"""Check that the default uk lemmatizer runs."""
|
||||
doc = Doc(uk_lemmatizer.vocab, words=["a", "b", "c"])
|
||||
|
|
|
|||
|
|
@ -316,6 +316,20 @@ def test_dependency_matcher_precedence_ops(en_vocab, op, num_matches):
|
|||
("the", "brown", "$--", 0),
|
||||
("brown", "the", "$--", 1),
|
||||
("brown", "brown", "$--", 0),
|
||||
("quick", "fox", "<++", 1),
|
||||
("quick", "over", "<++", 0),
|
||||
("over", "jumped", "<++", 0),
|
||||
("the", "fox", "<++", 2),
|
||||
("brown", "fox", "<--", 0),
|
||||
("fox", "jumped", "<--", 0),
|
||||
("fox", "over", "<--", 1),
|
||||
("jumped", "over", ">++", 1),
|
||||
("fox", "lazy", ">++", 0),
|
||||
("over", "the", ">++", 0),
|
||||
("brown", "fox", ">--", 0),
|
||||
("fox", "brown", ">--", 1),
|
||||
("jumped", "fox", ">--", 1),
|
||||
("fox", "the", ">--", 2),
|
||||
],
|
||||
)
|
||||
def test_dependency_matcher_ops(en_vocab, doc, left, right, op, num_matches):
|
||||
|
|
|
|||
44
spacy/tests/matcher/test_levenshtein.py
Normal file
44
spacy/tests/matcher/test_levenshtein.py
Normal file
|
|
@ -0,0 +1,44 @@
|
|||
import pytest
|
||||
from spacy.matcher import levenshtein
|
||||
|
||||
|
||||
# empty string plus 10 random ASCII, 10 random unicode, and 2 random long tests
|
||||
# from polyleven
|
||||
@pytest.mark.parametrize(
|
||||
"dist,a,b",
|
||||
[
|
||||
(0, "", ""),
|
||||
(4, "bbcb", "caba"),
|
||||
(3, "abcb", "cacc"),
|
||||
(3, "aa", "ccc"),
|
||||
(1, "cca", "ccac"),
|
||||
(1, "aba", "aa"),
|
||||
(4, "bcbb", "abac"),
|
||||
(3, "acbc", "bba"),
|
||||
(3, "cbba", "a"),
|
||||
(2, "bcc", "ba"),
|
||||
(4, "aaa", "ccbb"),
|
||||
(3, "うあい", "いいうい"),
|
||||
(2, "あううい", "うあい"),
|
||||
(3, "いういい", "うううあ"),
|
||||
(2, "うい", "あいあ"),
|
||||
(2, "いあい", "いう"),
|
||||
(1, "いい", "あいい"),
|
||||
(3, "あうあ", "いいああ"),
|
||||
(4, "いあうう", "ううああ"),
|
||||
(3, "いあいい", "ういああ"),
|
||||
(3, "いいああ", "ううあう"),
|
||||
(
|
||||
166,
|
||||
"TCTGGGCACGGATTCGTCAGATTCCATGTCCATATTTGAGGCTCTTGCAGGCAAAATTTGGGCATGTGAACTCCTTATAGTCCCCGTGC",
|
||||
"ATATGGATTGGGGGCATTCAAAGATACGGTTTCCCTTTCTTCAGTTTCGCGCGGCGCACGTCCGGGTGCGAGCCAGTTCGTCTTACTCACATTGTCGACTTCACGAATCGCGCATGATGTGCTTAGCCTGTACTTACGAACGAACTTTCGGTCCAAATACATTCTATCAACACCGAGGTATCCGTGCCACACGCCGAAGCTCGACCGTGTTCGTTGAGAGGTGGAAATGGTAAAAGATGAACATAGTC",
|
||||
),
|
||||
(
|
||||
111,
|
||||
"GGTTCGGCCGAATTCATAGAGCGTGGTAGTCGACGGTATCCCGCCTGGTAGGGGCCCCTTCTACCTAGCGGAAGTTTGTCAGTACTCTATAACACGAGGGCCTCTCACACCCTAGATCGTCCAGCCACTCGAAGATCGCAGCACCCTTACAGAAAGGCATTAATGTTTCTCCTAGCACTTGTGCAATGGTGAAGGAGTGATG",
|
||||
"CGTAACACTTCGCGCTACTGGGCTGCAACGTCTTGGGCATACATGCAAGATTATCTAATGCAAGCTTGAGCCCCGCTTGCGGAATTTCCCTAATCGGGGTCCCTTCCTGTTACGATAAGGACGCGTGCACT",
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_levenshtein(dist, a, b):
|
||||
assert levenshtein(a, b) == dist
|
||||
|
|
@ -368,6 +368,16 @@ def test_matcher_intersect_value_operator(en_vocab):
|
|||
doc[0]._.ext = ["A", "B"]
|
||||
assert len(matcher(doc)) == 1
|
||||
|
||||
# INTERSECTS matches nothing for iterables that aren't all str or int
|
||||
matcher = Matcher(en_vocab)
|
||||
pattern = [{"_": {"ext": {"INTERSECTS": ["Abx", "C"]}}}]
|
||||
matcher.add("M", [pattern])
|
||||
doc = Doc(en_vocab, words=["a", "b", "c"])
|
||||
doc[0]._.ext = [["Abx"], "B"]
|
||||
assert len(matcher(doc)) == 0
|
||||
doc[0]._.ext = ["Abx", "B"]
|
||||
assert len(matcher(doc)) == 1
|
||||
|
||||
# INTERSECTS with an empty pattern list matches nothing
|
||||
matcher = Matcher(en_vocab)
|
||||
pattern = [{"_": {"ext": {"INTERSECTS": []}}}]
|
||||
|
|
@ -476,14 +486,22 @@ def test_matcher_extension_set_membership(en_vocab):
|
|||
assert len(matches) == 0
|
||||
|
||||
|
||||
@pytest.mark.xfail(reason="IN predicate must handle sequence values in extensions")
|
||||
def test_matcher_extension_in_set_predicate(en_vocab):
|
||||
matcher = Matcher(en_vocab)
|
||||
Token.set_extension("ext", default=[])
|
||||
pattern = [{"_": {"ext": {"IN": ["A", "C"]}}}]
|
||||
matcher.add("M", [pattern])
|
||||
doc = Doc(en_vocab, words=["a", "b", "c"])
|
||||
|
||||
# The IN predicate expects an exact match between the
|
||||
# extension value and one of the pattern's values.
|
||||
doc[0]._.ext = ["A", "B"]
|
||||
assert len(matcher(doc)) == 0
|
||||
|
||||
doc[0]._.ext = ["A"]
|
||||
assert len(matcher(doc)) == 0
|
||||
|
||||
doc[0]._.ext = "A"
|
||||
assert len(matcher(doc)) == 1
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import pytest
|
||||
import warnings
|
||||
import srsly
|
||||
from mock import Mock
|
||||
|
||||
|
|
@ -344,13 +345,13 @@ def test_phrase_matcher_validation(en_vocab):
|
|||
matcher.add("TEST1", [doc1])
|
||||
with pytest.warns(UserWarning):
|
||||
matcher.add("TEST2", [doc2])
|
||||
with pytest.warns(None) as record:
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("error")
|
||||
matcher.add("TEST3", [doc3])
|
||||
assert not record.list
|
||||
matcher = PhraseMatcher(en_vocab, attr="POS", validate=True)
|
||||
with pytest.warns(None) as record:
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("error")
|
||||
matcher.add("TEST4", [doc2])
|
||||
assert not record.list
|
||||
|
||||
|
||||
def test_attr_validation(en_vocab):
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ def test_build_dependencies():
|
|||
"types-dataclasses",
|
||||
"types-mock",
|
||||
"types-requests",
|
||||
"types-setuptools",
|
||||
]
|
||||
# ignore language-specific packages that shouldn't be installed by all
|
||||
libs_ignore_setup = [
|
||||
|
|
|
|||
|
|
@ -1048,6 +1048,10 @@ def test_no_gold_ents(patterns):
|
|||
for eg in train_examples:
|
||||
eg.predicted = ruler(eg.predicted)
|
||||
|
||||
# Entity ruler is no longer needed (initialization below wipes out the
|
||||
# patterns and causes warnings)
|
||||
nlp.remove_pipe("entity_ruler")
|
||||
|
||||
def create_kb(vocab):
|
||||
# create artificial KB
|
||||
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
|
||||
|
|
|
|||
|
|
@ -605,10 +605,35 @@ def test_update_with_annotates():
|
|||
assert results[component] == ""
|
||||
|
||||
|
||||
def test_load_disable_enable() -> None:
|
||||
"""
|
||||
Tests spacy.load() with dis-/enabling components.
|
||||
"""
|
||||
@pytest.mark.issue(11443)
|
||||
def test_enable_disable_conflict_with_config():
|
||||
"""Test conflict between enable/disable w.r.t. `nlp.disabled` set in the config."""
|
||||
nlp = English()
|
||||
nlp.add_pipe("tagger")
|
||||
nlp.add_pipe("senter")
|
||||
nlp.add_pipe("sentencizer")
|
||||
|
||||
with make_tempdir() as tmp_dir:
|
||||
nlp.to_disk(tmp_dir)
|
||||
# Expected to fail, as config and arguments conflict.
|
||||
with pytest.raises(ValueError):
|
||||
spacy.load(
|
||||
tmp_dir, enable=["tagger"], config={"nlp": {"disabled": ["senter"]}}
|
||||
)
|
||||
# Expected to succeed without warning due to the lack of a conflicting config option.
|
||||
spacy.load(tmp_dir, enable=["tagger"])
|
||||
# Expected to succeed with a warning, as disable=[] should override the config setting.
|
||||
with pytest.warns(UserWarning):
|
||||
spacy.load(
|
||||
tmp_dir,
|
||||
enable=["tagger"],
|
||||
disable=[],
|
||||
config={"nlp": {"disabled": ["senter"]}},
|
||||
)
|
||||
|
||||
|
||||
def test_load_disable_enable():
|
||||
"""Tests spacy.load() with dis-/enabling components."""
|
||||
|
||||
base_nlp = English()
|
||||
for pipe in ("sentencizer", "tagger", "parser"):
|
||||
|
|
@ -618,6 +643,7 @@ def test_load_disable_enable() -> None:
|
|||
base_nlp.to_disk(tmp_dir)
|
||||
to_disable = ["parser", "tagger"]
|
||||
to_enable = ["tagger", "parser"]
|
||||
single_str = "tagger"
|
||||
|
||||
# Setting only `disable`.
|
||||
nlp = spacy.load(tmp_dir, disable=to_disable)
|
||||
|
|
@ -632,6 +658,16 @@ def test_load_disable_enable() -> None:
|
|||
]
|
||||
)
|
||||
|
||||
# Loading with a string representing one component
|
||||
nlp = spacy.load(tmp_dir, exclude=single_str)
|
||||
assert single_str not in nlp.component_names
|
||||
|
||||
nlp = spacy.load(tmp_dir, disable=single_str)
|
||||
assert single_str in nlp.component_names
|
||||
assert single_str not in nlp.pipe_names
|
||||
assert nlp._disabled == {single_str}
|
||||
assert nlp.disabled == [single_str]
|
||||
|
||||
# Testing consistent enable/disable combination.
|
||||
nlp = spacy.load(
|
||||
tmp_dir,
|
||||
|
|
|
|||
|
|
@ -404,10 +404,11 @@ def test_serialize_pipeline_disable_enable():
|
|||
assert nlp3.component_names == ["ner", "tagger"]
|
||||
with make_tempdir() as d:
|
||||
nlp3.to_disk(d)
|
||||
nlp4 = spacy.load(d, disable=["ner"])
|
||||
assert nlp4.pipe_names == []
|
||||
with pytest.warns(UserWarning):
|
||||
nlp4 = spacy.load(d, disable=["ner"])
|
||||
assert nlp4.pipe_names == ["tagger"]
|
||||
assert nlp4.component_names == ["ner", "tagger"]
|
||||
assert nlp4.disabled == ["ner", "tagger"]
|
||||
assert nlp4.disabled == ["ner"]
|
||||
with make_tempdir() as d:
|
||||
nlp.to_disk(d)
|
||||
nlp5 = spacy.load(d, exclude=["tagger"])
|
||||
|
|
|
|||
|
|
@ -589,7 +589,6 @@ def test_string_to_list_intify(value):
|
|||
assert string_to_list(value, intify=True) == [1, 2, 3]
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Temporarily skip for dev version")
|
||||
def test_download_compatibility():
|
||||
spec = SpecifierSet("==" + about.__version__)
|
||||
spec.prereleases = False
|
||||
|
|
@ -600,7 +599,6 @@ def test_download_compatibility():
|
|||
assert get_minor_version(about.__version__) == get_minor_version(version)
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Temporarily skip for dev version")
|
||||
def test_validate_compatibility_table():
|
||||
spec = SpecifierSet("==" + about.__version__)
|
||||
spec.prereleases = False
|
||||
|
|
|
|||
|
|
@ -659,3 +659,36 @@ def test_multiprocessing_gpu_warning(nlp2, texts):
|
|||
# Trigger multi-processing.
|
||||
for _ in docs:
|
||||
pass
|
||||
|
||||
|
||||
def test_dot_in_factory_names(nlp):
|
||||
Language.component("my_evil_component", func=evil_component)
|
||||
nlp.add_pipe("my_evil_component")
|
||||
|
||||
with pytest.raises(ValueError, match="not permitted"):
|
||||
Language.component("my.evil.component.v1", func=evil_component)
|
||||
|
||||
with pytest.raises(ValueError, match="not permitted"):
|
||||
Language.factory("my.evil.component.v1", func=evil_component)
|
||||
|
||||
|
||||
def test_component_return():
|
||||
"""Test that an error is raised if components return a type other than a
|
||||
doc."""
|
||||
nlp = English()
|
||||
|
||||
@Language.component("test_component_good_pipe")
|
||||
def good_pipe(doc):
|
||||
return doc
|
||||
|
||||
nlp.add_pipe("test_component_good_pipe")
|
||||
nlp("text")
|
||||
nlp.remove_pipe("test_component_good_pipe")
|
||||
|
||||
@Language.component("test_component_bad_pipe")
|
||||
def bad_pipe(doc):
|
||||
return doc.text
|
||||
|
||||
nlp.add_pipe("test_component_bad_pipe")
|
||||
with pytest.raises(ValueError, match="instead of a Doc"):
|
||||
nlp("text")
|
||||
|
|
|
|||
|
|
@ -10,7 +10,8 @@ from spacy.ml._precomputable_affine import _backprop_precomputable_affine_paddin
|
|||
from spacy.util import dot_to_object, SimpleFrozenList, import_file
|
||||
from spacy.util import to_ternary_int
|
||||
from thinc.api import Config, Optimizer, ConfigValidationError
|
||||
from thinc.api import set_current_ops
|
||||
from thinc.api import get_current_ops, set_current_ops, NumpyOps, CupyOps, MPSOps
|
||||
from thinc.compat import has_cupy_gpu, has_torch_mps_gpu
|
||||
from spacy.training.batchers import minibatch_by_words
|
||||
from spacy.lang.en import English
|
||||
from spacy.lang.nl import Dutch
|
||||
|
|
@ -18,7 +19,6 @@ from spacy.language import DEFAULT_CONFIG_PATH
|
|||
from spacy.schemas import ConfigSchemaTraining, TokenPattern, TokenPatternSchema
|
||||
from pydantic import ValidationError
|
||||
|
||||
from thinc.api import get_current_ops, NumpyOps, CupyOps
|
||||
|
||||
from .util import get_random_doc, make_tempdir
|
||||
|
||||
|
|
@ -111,26 +111,25 @@ def test_PrecomputableAffine(nO=4, nI=5, nF=3, nP=2):
|
|||
|
||||
def test_prefer_gpu():
|
||||
current_ops = get_current_ops()
|
||||
try:
|
||||
import cupy # noqa: F401
|
||||
|
||||
prefer_gpu()
|
||||
if has_cupy_gpu:
|
||||
assert prefer_gpu()
|
||||
assert isinstance(get_current_ops(), CupyOps)
|
||||
except ImportError:
|
||||
elif has_torch_mps_gpu:
|
||||
assert prefer_gpu()
|
||||
assert isinstance(get_current_ops(), MPSOps)
|
||||
else:
|
||||
assert not prefer_gpu()
|
||||
set_current_ops(current_ops)
|
||||
|
||||
|
||||
def test_require_gpu():
|
||||
current_ops = get_current_ops()
|
||||
try:
|
||||
import cupy # noqa: F401
|
||||
|
||||
if has_cupy_gpu:
|
||||
require_gpu()
|
||||
assert isinstance(get_current_ops(), CupyOps)
|
||||
except ImportError:
|
||||
with pytest.raises(ValueError):
|
||||
require_gpu()
|
||||
elif has_torch_mps_gpu:
|
||||
require_gpu()
|
||||
assert isinstance(get_current_ops(), MPSOps)
|
||||
set_current_ops(current_ops)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ def doc(nlp):
|
|||
words = ["Sarah", "'s", "sister", "flew", "to", "Silicon", "Valley", "via", "London", "."]
|
||||
tags = ["NNP", "POS", "NN", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
|
||||
pos = ["PROPN", "PART", "NOUN", "VERB", "ADP", "PROPN", "PROPN", "ADP", "PROPN", "PUNCT"]
|
||||
ents = ["B-PERSON", "I-PERSON", "O", "O", "O", "B-LOC", "I-LOC", "O", "B-GPE", "O"]
|
||||
ents = ["B-PERSON", "I-PERSON", "O", "", "O", "B-LOC", "I-LOC", "O", "B-GPE", "O"]
|
||||
cats = {"TRAVEL": 1.0, "BAKING": 0.0}
|
||||
# fmt: on
|
||||
doc = Doc(nlp.vocab, words=words, tags=tags, pos=pos, ents=ents)
|
||||
|
|
@ -106,6 +106,7 @@ def test_lowercase_augmenter(nlp, doc):
|
|||
assert [(e.start, e.end, e.label) for e in eg.reference.ents] == ents
|
||||
for ref_ent, orig_ent in zip(eg.reference.ents, doc.ents):
|
||||
assert ref_ent.text == orig_ent.text.lower()
|
||||
assert [t.ent_iob for t in doc] == [t.ent_iob for t in eg.reference]
|
||||
assert [t.pos_ for t in eg.reference] == [t.pos_ for t in doc]
|
||||
|
||||
# check that augmentation works when lowercasing leads to different
|
||||
|
|
@ -166,7 +167,7 @@ def test_make_whitespace_variant(nlp):
|
|||
lemmas = ["they", "fly", "to", "New", "York", "City", ".", "\n", "then", "they", "drive", "to", "Washington", ",", "D.C."]
|
||||
heads = [1, 1, 1, 4, 5, 2, 1, 10, 10, 10, 10, 10, 11, 12, 12]
|
||||
deps = ["nsubj", "ROOT", "prep", "compound", "compound", "pobj", "punct", "dep", "advmod", "nsubj", "ROOT", "prep", "pobj", "punct", "appos"]
|
||||
ents = ["O", "O", "O", "B-GPE", "I-GPE", "I-GPE", "O", "O", "O", "O", "O", "O", "B-GPE", "O", "B-GPE"]
|
||||
ents = ["O", "", "O", "B-GPE", "I-GPE", "I-GPE", "O", "O", "O", "O", "O", "O", "B-GPE", "O", "B-GPE"]
|
||||
# fmt: on
|
||||
doc = Doc(
|
||||
nlp.vocab,
|
||||
|
|
@ -215,6 +216,8 @@ def test_make_whitespace_variant(nlp):
|
|||
assert mod_ex2.reference[j].head.i == j - 1
|
||||
# entities are well-formed
|
||||
assert len(doc.ents) == len(mod_ex.reference.ents)
|
||||
# there is one token with missing entity information
|
||||
assert any(t.ent_iob == 0 for t in mod_ex.reference)
|
||||
for ent in mod_ex.reference.ents:
|
||||
assert not ent[0].is_space
|
||||
assert not ent[-1].is_space
|
||||
|
|
|
|||
30
spacy/tests/training/test_logger.py
Normal file
30
spacy/tests/training/test_logger.py
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
import pytest
|
||||
import spacy
|
||||
|
||||
from spacy.training import loggers
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def nlp():
|
||||
nlp = spacy.blank("en")
|
||||
nlp.add_pipe("ner")
|
||||
return nlp
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def info():
|
||||
return {
|
||||
"losses": {"ner": 100},
|
||||
"other_scores": {"ENTS_F": 0.85, "ENTS_P": 0.90, "ENTS_R": 0.80},
|
||||
"epoch": 100,
|
||||
"step": 125,
|
||||
"score": 85,
|
||||
}
|
||||
|
||||
|
||||
def test_console_logger(nlp, info):
|
||||
console_logger = loggers.console_logger(
|
||||
progress_bar=True, console_output=True, output_file=None
|
||||
)
|
||||
log_step, finalize = console_logger(nlp)
|
||||
log_step(info)
|
||||
|
|
@ -431,3 +431,41 @@ def test_Example_aligned_whitespace(en_vocab):
|
|||
|
||||
example = Example(predicted, reference)
|
||||
assert example.get_aligned("TAG", as_string=True) == tags
|
||||
|
||||
|
||||
@pytest.mark.issue("11260")
|
||||
def test_issue11260():
|
||||
annots = {
|
||||
"words": ["I", "like", "New", "York", "."],
|
||||
"spans": {
|
||||
"cities": [(7, 15, "LOC", "")],
|
||||
"people": [(0, 1, "PERSON", "")],
|
||||
},
|
||||
}
|
||||
vocab = Vocab()
|
||||
predicted = Doc(vocab, words=annots["words"])
|
||||
example = Example.from_dict(predicted, annots)
|
||||
assert len(example.reference.spans["cities"]) == 1
|
||||
assert len(example.reference.spans["people"]) == 1
|
||||
|
||||
output_dict = example.to_dict()
|
||||
assert "spans" in output_dict["doc_annotation"]
|
||||
assert output_dict["doc_annotation"]["spans"]["cities"] == annots["spans"]["cities"]
|
||||
assert output_dict["doc_annotation"]["spans"]["people"] == annots["spans"]["people"]
|
||||
|
||||
output_example = Example.from_dict(predicted, output_dict)
|
||||
|
||||
assert len(output_example.reference.spans["cities"]) == len(
|
||||
example.reference.spans["cities"]
|
||||
)
|
||||
assert len(output_example.reference.spans["people"]) == len(
|
||||
example.reference.spans["people"]
|
||||
)
|
||||
for span in example.reference.spans["cities"]:
|
||||
assert span.label_ == "LOC"
|
||||
assert span.text == "New York"
|
||||
assert span.start_char == 7
|
||||
for span in example.reference.spans["people"]:
|
||||
assert span.label_ == "PERSON"
|
||||
assert span.text == "I"
|
||||
assert span.start_char == 0
|
||||
|
|
|
|||
|
|
@ -72,7 +72,7 @@ class Doc:
|
|||
lemmas: Optional[List[str]] = ...,
|
||||
heads: Optional[List[int]] = ...,
|
||||
deps: Optional[List[str]] = ...,
|
||||
sent_starts: Optional[List[Union[bool, None]]] = ...,
|
||||
sent_starts: Optional[List[Union[bool, int, None]]] = ...,
|
||||
ents: Optional[List[str]] = ...,
|
||||
) -> None: ...
|
||||
@property
|
||||
|
|
|
|||
|
|
@ -217,9 +217,9 @@ cdef class Doc:
|
|||
head in the doc. Defaults to None.
|
||||
deps (Optional[List[str]]): A list of unicode strings, of the same
|
||||
length as words, to assign as token.dep. Defaults to None.
|
||||
sent_starts (Optional[List[Union[bool, None]]]): A list of values, of
|
||||
the same length as words, to assign as token.is_sent_start. Will be
|
||||
overridden by heads if heads is provided. Defaults to None.
|
||||
sent_starts (Optional[List[Union[bool, int, None]]]): A list of values,
|
||||
of the same length as words, to assign as token.is_sent_start. Will
|
||||
be overridden by heads if heads is provided. Defaults to None.
|
||||
ents (Optional[List[str]]): A list of unicode strings, of the same
|
||||
length as words, as IOB tags to assign as token.ent_iob and
|
||||
token.ent_type. Defaults to None.
|
||||
|
|
@ -285,6 +285,7 @@ cdef class Doc:
|
|||
heads = [0] * len(deps)
|
||||
if heads and not deps:
|
||||
raise ValueError(Errors.E1017)
|
||||
sent_starts = list(sent_starts) if sent_starts is not None else None
|
||||
if sent_starts is not None:
|
||||
for i in range(len(sent_starts)):
|
||||
if sent_starts[i] is True:
|
||||
|
|
@ -300,12 +301,11 @@ cdef class Doc:
|
|||
ent_iobs = None
|
||||
ent_types = None
|
||||
if ents is not None:
|
||||
ents = [ent if ent != "" else None for ent in ents]
|
||||
iob_strings = Token.iob_strings()
|
||||
# make valid IOB2 out of IOB1 or IOB2
|
||||
for i, ent in enumerate(ents):
|
||||
if ent is "":
|
||||
ents[i] = None
|
||||
elif ent is not None and not isinstance(ent, str):
|
||||
if ent is not None and not isinstance(ent, str):
|
||||
raise ValueError(Errors.E177.format(tag=ent))
|
||||
if i < len(ents) - 1:
|
||||
# OI -> OB
|
||||
|
|
@ -1602,13 +1602,30 @@ cdef class Doc:
|
|||
ents.append(char_span)
|
||||
self.ents = ents
|
||||
|
||||
# Add custom attributes. Note that only Doc extensions are currently considered, Token and Span extensions are
|
||||
# not yet supported.
|
||||
# Add custom attributes for the whole Doc object.
|
||||
for attr in doc_json.get("_", {}):
|
||||
if not Doc.has_extension(attr):
|
||||
Doc.set_extension(attr)
|
||||
self._.set(attr, doc_json["_"][attr])
|
||||
|
||||
if doc_json.get("underscore_token", {}):
|
||||
for token_attr in doc_json["underscore_token"]:
|
||||
token_start = doc_json["underscore_token"][token_attr]["token_start"]
|
||||
value = doc_json["underscore_token"][token_attr]["value"]
|
||||
|
||||
if not Token.has_extension(token_attr):
|
||||
Token.set_extension(token_attr)
|
||||
self[token_start]._.set(token_attr, value)
|
||||
|
||||
if doc_json.get("underscore_span", {}):
|
||||
for span_attr in doc_json["underscore_span"]:
|
||||
token_start = doc_json["underscore_span"][span_attr]["token_start"]
|
||||
token_end = doc_json["underscore_span"][span_attr]["token_end"]
|
||||
value = doc_json["underscore_span"][span_attr]["value"]
|
||||
|
||||
if not Span.has_extension(span_attr):
|
||||
Span.set_extension(span_attr)
|
||||
self[token_start:token_end]._.set(span_attr, value)
|
||||
return self
|
||||
|
||||
def to_json(self, underscore=None):
|
||||
|
|
@ -1650,20 +1667,40 @@ cdef class Doc:
|
|||
for span_group in self.spans:
|
||||
data["spans"][span_group] = []
|
||||
for span in self.spans[span_group]:
|
||||
span_data = {
|
||||
"start": span.start_char, "end": span.end_char, "label": span.label_, "kb_id": span.kb_id_
|
||||
}
|
||||
span_data = {"start": span.start_char, "end": span.end_char, "label": span.label_, "kb_id": span.kb_id_}
|
||||
data["spans"][span_group].append(span_data)
|
||||
|
||||
if underscore:
|
||||
data["_"] = {}
|
||||
user_keys = set()
|
||||
if self.user_data:
|
||||
data["_"] = {}
|
||||
data["underscore_token"] = {}
|
||||
data["underscore_span"] = {}
|
||||
for data_key in self.user_data:
|
||||
if type(data_key) == tuple and len(data_key) >= 4 and data_key[0] == "._.":
|
||||
attr = data_key[1]
|
||||
start = data_key[2]
|
||||
end = data_key[3]
|
||||
if attr in underscore:
|
||||
user_keys.add(attr)
|
||||
value = self.user_data[data_key]
|
||||
if not srsly.is_json_serializable(value):
|
||||
raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
|
||||
# Check if doc attribute
|
||||
if start is None:
|
||||
data["_"][attr] = value
|
||||
# Check if token attribute
|
||||
elif end is None:
|
||||
if attr not in data["underscore_token"]:
|
||||
data["underscore_token"][attr] = {"token_start": start, "value": value}
|
||||
# Else span attribute
|
||||
else:
|
||||
if attr not in data["underscore_span"]:
|
||||
data["underscore_span"][attr] = {"token_start": start, "token_end": end, "value": value}
|
||||
|
||||
for attr in underscore:
|
||||
if not self.has_extension(attr):
|
||||
if attr not in user_keys:
|
||||
raise ValueError(Errors.E106.format(attr=attr, opts=underscore))
|
||||
value = self._.get(attr)
|
||||
if not srsly.is_json_serializable(value):
|
||||
raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
|
||||
data["_"][attr] = value
|
||||
return data
|
||||
|
||||
def to_utf8_array(self, int nr_char=-1):
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from functools import partial
|
|||
|
||||
from ..util import registry
|
||||
from .example import Example
|
||||
from .iob_utils import split_bilu_label
|
||||
from .iob_utils import split_bilu_label, _doc_to_biluo_tags_with_partial
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..language import Language # noqa: F401
|
||||
|
|
@ -62,6 +62,9 @@ def combined_augmenter(
|
|||
if orth_variants and random.random() < orth_level:
|
||||
raw_text = example.text
|
||||
orig_dict = example.to_dict()
|
||||
orig_dict["doc_annotation"]["entities"] = _doc_to_biluo_tags_with_partial(
|
||||
example.reference
|
||||
)
|
||||
variant_text, variant_token_annot = make_orth_variants(
|
||||
nlp,
|
||||
raw_text,
|
||||
|
|
@ -128,6 +131,9 @@ def lower_casing_augmenter(
|
|||
|
||||
def make_lowercase_variant(nlp: "Language", example: Example):
|
||||
example_dict = example.to_dict()
|
||||
example_dict["doc_annotation"]["entities"] = _doc_to_biluo_tags_with_partial(
|
||||
example.reference
|
||||
)
|
||||
doc = nlp.make_doc(example.text.lower())
|
||||
example_dict["token_annotation"]["ORTH"] = [t.lower_ for t in example.reference]
|
||||
return example.from_dict(doc, example_dict)
|
||||
|
|
@ -146,6 +152,9 @@ def orth_variants_augmenter(
|
|||
else:
|
||||
raw_text = example.text
|
||||
orig_dict = example.to_dict()
|
||||
orig_dict["doc_annotation"]["entities"] = _doc_to_biluo_tags_with_partial(
|
||||
example.reference
|
||||
)
|
||||
variant_text, variant_token_annot = make_orth_variants(
|
||||
nlp,
|
||||
raw_text,
|
||||
|
|
@ -248,6 +257,9 @@ def make_whitespace_variant(
|
|||
RETURNS (Example): Example with one additional space token.
|
||||
"""
|
||||
example_dict = example.to_dict()
|
||||
example_dict["doc_annotation"]["entities"] = _doc_to_biluo_tags_with_partial(
|
||||
example.reference
|
||||
)
|
||||
doc_dict = example_dict.get("doc_annotation", {})
|
||||
token_dict = example_dict.get("token_annotation", {})
|
||||
# returned unmodified if:
|
||||
|
|
|
|||
|
|
@ -361,6 +361,7 @@ cdef class Example:
|
|||
"doc_annotation": {
|
||||
"cats": dict(self.reference.cats),
|
||||
"entities": doc_to_biluo_tags(self.reference),
|
||||
"spans": self._spans_to_dict(),
|
||||
"links": self._links_to_dict()
|
||||
},
|
||||
"token_annotation": {
|
||||
|
|
@ -376,6 +377,18 @@ cdef class Example:
|
|||
}
|
||||
}
|
||||
|
||||
def _spans_to_dict(self):
|
||||
span_dict = {}
|
||||
for key in self.reference.spans:
|
||||
span_tuples = []
|
||||
for span in self.reference.spans[key]:
|
||||
span_tuple = (span.start_char, span.end_char, span.label_, span.kb_id_)
|
||||
span_tuples.append(span_tuple)
|
||||
span_dict[key] = span_tuples
|
||||
|
||||
return span_dict
|
||||
|
||||
|
||||
def _links_to_dict(self):
|
||||
links = {}
|
||||
for ent in self.reference.ents:
|
||||
|
|
|
|||
|
|
@ -337,3 +337,5 @@ def ensure_shape(vectors_loc):
|
|||
# store all the results in a list in memory
|
||||
lines2 = open_file(vectors_loc)
|
||||
yield from lines2
|
||||
lines2.close()
|
||||
lines.close()
|
||||
|
|
|
|||
|
|
@ -60,6 +60,14 @@ def doc_to_biluo_tags(doc: Doc, missing: str = "O"):
|
|||
)
|
||||
|
||||
|
||||
def _doc_to_biluo_tags_with_partial(doc: Doc) -> List[str]:
|
||||
ents = doc_to_biluo_tags(doc, missing="-")
|
||||
for i, token in enumerate(doc):
|
||||
if token.ent_iob == 2:
|
||||
ents[i] = "O"
|
||||
return ents
|
||||
|
||||
|
||||
def offsets_to_biluo_tags(
|
||||
doc: Doc, entities: Iterable[Tuple[int, int, Union[str, int]]], missing: str = "O"
|
||||
) -> List[str]:
|
||||
|
|
|
|||
|
|
@ -1,10 +1,13 @@
|
|||
from typing import TYPE_CHECKING, Dict, Any, Tuple, Callable, List, Optional, IO
|
||||
from typing import TYPE_CHECKING, Dict, Any, Tuple, Callable, List, Optional, IO, Union
|
||||
from wasabi import Printer
|
||||
from pathlib import Path
|
||||
import tqdm
|
||||
import sys
|
||||
import srsly
|
||||
|
||||
from ..util import registry
|
||||
from ..errors import Errors
|
||||
from .. import util
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..language import Language # noqa: F401
|
||||
|
|
@ -23,13 +26,44 @@ def setup_table(
|
|||
return final_cols, final_widths, ["r" for _ in final_widths]
|
||||
|
||||
|
||||
@registry.loggers("spacy.ConsoleLogger.v1")
|
||||
def console_logger(progress_bar: bool = False):
|
||||
@registry.loggers("spacy.ConsoleLogger.v2")
|
||||
def console_logger(
|
||||
progress_bar: bool = False,
|
||||
console_output: bool = True,
|
||||
output_file: Optional[Union[str, Path]] = None,
|
||||
):
|
||||
"""The ConsoleLogger.v2 prints out training logs in the console and/or saves them to a jsonl file.
|
||||
progress_bar (bool): Whether the logger should print the progress bar.
|
||||
console_output (bool): Whether the logger should print the logs on the console.
|
||||
output_file (Optional[Union[str, Path]]): The file to save the training logs to.
|
||||
"""
|
||||
_log_exist = False
|
||||
if output_file:
|
||||
output_file = util.ensure_path(output_file) # type: ignore
|
||||
if output_file.exists(): # type: ignore
|
||||
_log_exist = True
|
||||
if not output_file.parents[0].exists(): # type: ignore
|
||||
output_file.parents[0].mkdir(parents=True) # type: ignore
|
||||
|
||||
def setup_printer(
|
||||
nlp: "Language", stdout: IO = sys.stdout, stderr: IO = sys.stderr
|
||||
) -> Tuple[Callable[[Optional[Dict[str, Any]]], None], Callable[[], None]]:
|
||||
write = lambda text: print(text, file=stdout, flush=True)
|
||||
msg = Printer(no_print=True)
|
||||
|
||||
nonlocal output_file
|
||||
output_stream = None
|
||||
if _log_exist:
|
||||
write(
|
||||
msg.warn(
|
||||
f"Saving logs is disabled because {output_file} already exists."
|
||||
)
|
||||
)
|
||||
output_file = None
|
||||
elif output_file:
|
||||
write(msg.info(f"Saving results to {output_file}"))
|
||||
output_stream = open(output_file, "w", encoding="utf-8")
|
||||
|
||||
# ensure that only trainable components are logged
|
||||
logged_pipes = [
|
||||
name
|
||||
|
|
@ -40,13 +74,15 @@ def console_logger(progress_bar: bool = False):
|
|||
score_weights = nlp.config["training"]["score_weights"]
|
||||
score_cols = [col for col, value in score_weights.items() if value is not None]
|
||||
loss_cols = [f"Loss {pipe}" for pipe in logged_pipes]
|
||||
spacing = 2
|
||||
table_header, table_widths, table_aligns = setup_table(
|
||||
cols=["E", "#"] + loss_cols + score_cols + ["Score"],
|
||||
widths=[3, 6] + [8 for _ in loss_cols] + [6 for _ in score_cols] + [6],
|
||||
)
|
||||
write(msg.row(table_header, widths=table_widths, spacing=spacing))
|
||||
write(msg.row(["-" * width for width in table_widths], spacing=spacing))
|
||||
|
||||
if console_output:
|
||||
spacing = 2
|
||||
table_header, table_widths, table_aligns = setup_table(
|
||||
cols=["E", "#"] + loss_cols + score_cols + ["Score"],
|
||||
widths=[3, 6] + [8 for _ in loss_cols] + [6 for _ in score_cols] + [6],
|
||||
)
|
||||
write(msg.row(table_header, widths=table_widths, spacing=spacing))
|
||||
write(msg.row(["-" * width for width in table_widths], spacing=spacing))
|
||||
progress = None
|
||||
|
||||
def log_step(info: Optional[Dict[str, Any]]) -> None:
|
||||
|
|
@ -57,12 +93,15 @@ def console_logger(progress_bar: bool = False):
|
|||
if progress is not None:
|
||||
progress.update(1)
|
||||
return
|
||||
losses = [
|
||||
"{0:.2f}".format(float(info["losses"][pipe_name]))
|
||||
for pipe_name in logged_pipes
|
||||
]
|
||||
|
||||
losses = []
|
||||
log_losses = {}
|
||||
for pipe_name in logged_pipes:
|
||||
losses.append("{0:.2f}".format(float(info["losses"][pipe_name])))
|
||||
log_losses[pipe_name] = float(info["losses"][pipe_name])
|
||||
|
||||
scores = []
|
||||
log_scores = {}
|
||||
for col in score_cols:
|
||||
score = info["other_scores"].get(col, 0.0)
|
||||
try:
|
||||
|
|
@ -73,6 +112,7 @@ def console_logger(progress_bar: bool = False):
|
|||
if col != "speed":
|
||||
score *= 100
|
||||
scores.append("{0:.2f}".format(score))
|
||||
log_scores[str(col)] = score
|
||||
|
||||
data = (
|
||||
[info["epoch"], info["step"]]
|
||||
|
|
@ -80,20 +120,36 @@ def console_logger(progress_bar: bool = False):
|
|||
+ scores
|
||||
+ ["{0:.2f}".format(float(info["score"]))]
|
||||
)
|
||||
|
||||
if output_stream:
|
||||
# Write to log file per log_step
|
||||
log_data = {
|
||||
"epoch": info["epoch"],
|
||||
"step": info["step"],
|
||||
"losses": log_losses,
|
||||
"scores": log_scores,
|
||||
"score": float(info["score"]),
|
||||
}
|
||||
output_stream.write(srsly.json_dumps(log_data) + "\n")
|
||||
|
||||
if progress is not None:
|
||||
progress.close()
|
||||
write(
|
||||
msg.row(data, widths=table_widths, aligns=table_aligns, spacing=spacing)
|
||||
)
|
||||
if progress_bar:
|
||||
# Set disable=None, so that it disables on non-TTY
|
||||
progress = tqdm.tqdm(
|
||||
total=eval_frequency, disable=None, leave=False, file=stderr
|
||||
if console_output:
|
||||
write(
|
||||
msg.row(
|
||||
data, widths=table_widths, aligns=table_aligns, spacing=spacing
|
||||
)
|
||||
)
|
||||
progress.set_description(f"Epoch {info['epoch']+1}")
|
||||
if progress_bar:
|
||||
# Set disable=None, so that it disables on non-TTY
|
||||
progress = tqdm.tqdm(
|
||||
total=eval_frequency, disable=None, leave=False, file=stderr
|
||||
)
|
||||
progress.set_description(f"Epoch {info['epoch']+1}")
|
||||
|
||||
def finalize() -> None:
|
||||
pass
|
||||
if output_stream:
|
||||
output_stream.close()
|
||||
|
||||
return log_step, finalize
|
||||
|
||||
|
|
|
|||
|
|
@ -67,7 +67,6 @@ LEXEME_NORM_LANGS = ["cs", "da", "de", "el", "en", "id", "lb", "mk", "pt", "ru",
|
|||
CONFIG_SECTION_ORDER = ["paths", "variables", "system", "nlp", "components", "corpora", "training", "pretraining", "initialize"]
|
||||
# fmt: on
|
||||
|
||||
|
||||
logger = logging.getLogger("spacy")
|
||||
logger_stream_handler = logging.StreamHandler()
|
||||
logger_stream_handler.setFormatter(
|
||||
|
|
@ -394,13 +393,17 @@ def get_module_path(module: ModuleType) -> Path:
|
|||
return file_path.parent
|
||||
|
||||
|
||||
# Default value for passed enable/disable values.
|
||||
_DEFAULT_EMPTY_PIPES = SimpleFrozenList()
|
||||
|
||||
|
||||
def load_model(
|
||||
name: Union[str, Path],
|
||||
*,
|
||||
vocab: Union["Vocab", bool] = True,
|
||||
disable: Iterable[str] = SimpleFrozenList(),
|
||||
enable: Iterable[str] = SimpleFrozenList(),
|
||||
exclude: Iterable[str] = SimpleFrozenList(),
|
||||
disable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
|
||||
) -> "Language":
|
||||
"""Load a model from a package or data path.
|
||||
|
|
@ -408,9 +411,9 @@ def load_model(
|
|||
name (str): Package name or model path.
|
||||
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.
|
||||
enable (Iterable[str]): Names of pipeline components to enable. All others will be disabled.
|
||||
exclude (Iterable[str]): Names of pipeline components to exclude.
|
||||
disable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to disable.
|
||||
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable. All others will be disabled.
|
||||
exclude (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to exclude.
|
||||
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.
|
||||
|
|
@ -440,9 +443,9 @@ def load_model_from_package(
|
|||
name: str,
|
||||
*,
|
||||
vocab: Union["Vocab", bool] = True,
|
||||
disable: Iterable[str] = SimpleFrozenList(),
|
||||
enable: Iterable[str] = SimpleFrozenList(),
|
||||
exclude: Iterable[str] = SimpleFrozenList(),
|
||||
disable: Union[str, Iterable[str]] = SimpleFrozenList(),
|
||||
enable: Union[str, Iterable[str]] = SimpleFrozenList(),
|
||||
exclude: Union[str, Iterable[str]] = SimpleFrozenList(),
|
||||
config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
|
||||
) -> "Language":
|
||||
"""Load a model from an installed package.
|
||||
|
|
@ -450,12 +453,12 @@ def load_model_from_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. Disabled
|
||||
disable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to disable. Disabled
|
||||
pipes will be loaded but they won't be run unless you explicitly
|
||||
enable them by calling nlp.enable_pipe.
|
||||
enable (Iterable[str]): Names of pipeline components to enable. All other
|
||||
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable. All other
|
||||
pipes will be disabled (and can be enabled using `nlp.enable_pipe`).
|
||||
exclude (Iterable[str]): Names of pipeline components to exclude. Excluded
|
||||
exclude (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to exclude. Excluded
|
||||
components won't be loaded.
|
||||
config (Dict[str, Any] / Config): Config overrides as nested dict or dict
|
||||
keyed by section values in dot notation.
|
||||
|
|
@ -470,9 +473,9 @@ def load_model_from_path(
|
|||
*,
|
||||
meta: Optional[Dict[str, Any]] = None,
|
||||
vocab: Union["Vocab", bool] = True,
|
||||
disable: Iterable[str] = SimpleFrozenList(),
|
||||
enable: Iterable[str] = SimpleFrozenList(),
|
||||
exclude: Iterable[str] = SimpleFrozenList(),
|
||||
disable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
|
||||
) -> "Language":
|
||||
"""Load a model from a data directory path. Creates Language class with
|
||||
|
|
@ -482,12 +485,12 @@ def load_model_from_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. Disabled
|
||||
disable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to disable. Disabled
|
||||
pipes will be loaded but they won't be run unless you explicitly
|
||||
enable them by calling nlp.enable_pipe.
|
||||
enable (Iterable[str]): Names of pipeline components to enable. All other
|
||||
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable. All other
|
||||
pipes will be disabled (and can be enabled using `nlp.enable_pipe`).
|
||||
exclude (Iterable[str]): Names of pipeline components to exclude. Excluded
|
||||
exclude (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to exclude. Excluded
|
||||
components won't be loaded.
|
||||
config (Dict[str, Any] / Config): Config overrides as nested dict or dict
|
||||
keyed by section values in dot notation.
|
||||
|
|
@ -516,9 +519,9 @@ def load_model_from_config(
|
|||
*,
|
||||
meta: Dict[str, Any] = SimpleFrozenDict(),
|
||||
vocab: Union["Vocab", bool] = True,
|
||||
disable: Iterable[str] = SimpleFrozenList(),
|
||||
enable: Iterable[str] = SimpleFrozenList(),
|
||||
exclude: Iterable[str] = SimpleFrozenList(),
|
||||
disable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
|
||||
auto_fill: bool = False,
|
||||
validate: bool = True,
|
||||
) -> "Language":
|
||||
|
|
@ -529,12 +532,12 @@ def load_model_from_config(
|
|||
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. Disabled
|
||||
disable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to disable. Disabled
|
||||
pipes will be loaded but they won't be run unless you explicitly
|
||||
enable them by calling nlp.enable_pipe.
|
||||
enable (Iterable[str]): Names of pipeline components to enable. All other
|
||||
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable. All other
|
||||
pipes will be disabled (and can be enabled using `nlp.enable_pipe`).
|
||||
exclude (Iterable[str]): Names of pipeline components to exclude. Excluded
|
||||
exclude (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to exclude. Excluded
|
||||
components won't be loaded.
|
||||
auto_fill (bool): Whether to auto-fill config with missing defaults.
|
||||
validate (bool): Whether to show config validation errors.
|
||||
|
|
@ -616,9 +619,9 @@ def load_model_from_init_py(
|
|||
init_file: Union[Path, str],
|
||||
*,
|
||||
vocab: Union["Vocab", bool] = True,
|
||||
disable: Iterable[str] = SimpleFrozenList(),
|
||||
enable: Iterable[str] = SimpleFrozenList(),
|
||||
exclude: Iterable[str] = SimpleFrozenList(),
|
||||
disable: Union[str, Iterable[str]] = SimpleFrozenList(),
|
||||
enable: Union[str, Iterable[str]] = SimpleFrozenList(),
|
||||
exclude: Union[str, Iterable[str]] = SimpleFrozenList(),
|
||||
config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
|
||||
) -> "Language":
|
||||
"""Helper function to use in the `load()` method of a model package's
|
||||
|
|
@ -626,12 +629,12 @@ def load_model_from_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. Disabled
|
||||
disable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to disable. Disabled
|
||||
pipes will be loaded but they won't be run unless you explicitly
|
||||
enable them by calling nlp.enable_pipe.
|
||||
enable (Iterable[str]): Names of pipeline components to enable. All other
|
||||
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable. All other
|
||||
pipes will be disabled (and can be enabled using `nlp.enable_pipe`).
|
||||
exclude (Iterable[str]): Names of pipeline components to exclude. Excluded
|
||||
exclude (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to exclude. Excluded
|
||||
components won't be loaded.
|
||||
config (Dict[str, Any] / Config): Config overrides as nested dict or dict
|
||||
keyed by section values in dot notation.
|
||||
|
|
@ -795,6 +798,15 @@ def get_model_lower_version(constraint: str) -> Optional[str]:
|
|||
return None
|
||||
|
||||
|
||||
def is_prerelease_version(version: str) -> bool:
|
||||
"""Check whether a version is a prerelease version.
|
||||
|
||||
version (str): The version, e.g. "3.0.0.dev1".
|
||||
RETURNS (bool): Whether the version is a prerelease version.
|
||||
"""
|
||||
return Version(version).is_prerelease
|
||||
|
||||
|
||||
def get_base_version(version: str) -> str:
|
||||
"""Generate the base version without any prerelease identifiers.
|
||||
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ menu:
|
|||
- ['Text Classification', 'textcat']
|
||||
- ['Span Classification', 'spancat']
|
||||
- ['Entity Linking', 'entitylinker']
|
||||
- ['Coreference', 'coref-architectures']
|
||||
---
|
||||
|
||||
A **model architecture** is a function that wires up a
|
||||
|
|
@ -587,8 +588,8 @@ consists of either two or three subnetworks:
|
|||
run once for each batch.
|
||||
- **lower**: Construct a feature-specific vector for each `(token, feature)`
|
||||
pair. This is also run once for each batch. Constructing the state
|
||||
representation is then a matter of summing the component features and
|
||||
applying the non-linearity.
|
||||
representation is then a matter of summing the component features and applying
|
||||
the non-linearity.
|
||||
- **upper** (optional): A feed-forward network that predicts scores from the
|
||||
state representation. If not present, the output from the lower model is used
|
||||
as action scores directly.
|
||||
|
|
@ -628,8 +629,8 @@ same signature, but the `use_upper` argument was `True` by default.
|
|||
> ```
|
||||
|
||||
Build a tagger model, using a provided token-to-vector component. The tagger
|
||||
model adds a linear layer with softmax activation to predict scores given
|
||||
the token vectors.
|
||||
model adds a linear layer with softmax activation to predict scores given the
|
||||
token vectors.
|
||||
|
||||
| Name | Description |
|
||||
| ----------- | ------------------------------------------------------------------------------------------ |
|
||||
|
|
@ -920,5 +921,84 @@ A function that reads an existing `KnowledgeBase` from file.
|
|||
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`](/api/kb/#candidate) objects. The default
|
||||
`CandidateGenerator` uses the text of a mention to find its potential
|
||||
aliases in the `KnowledgeBase`. Note that this function is case-dependent.
|
||||
`CandidateGenerator` uses the text of a mention to find its potential aliases in
|
||||
the `KnowledgeBase`. Note that this function is case-dependent.
|
||||
|
||||
## Coreference {#coref-architectures tag="experimental"}
|
||||
|
||||
A [`CoreferenceResolver`](/api/coref) component identifies tokens that refer to
|
||||
the same entity. A [`SpanResolver`](/api/span-resolver) component infers spans
|
||||
from single tokens. Together these components can be used to reproduce
|
||||
traditional coreference models. You can also omit the `SpanResolver` if working
|
||||
with only token-level clusters is acceptable.
|
||||
|
||||
### spacy-experimental.Coref.v1 {#Coref tag="experimental"}
|
||||
|
||||
> #### Example Config
|
||||
>
|
||||
> ```ini
|
||||
>
|
||||
> [model]
|
||||
> @architectures = "spacy-experimental.Coref.v1"
|
||||
> distance_embedding_size = 20
|
||||
> dropout = 0.3
|
||||
> hidden_size = 1024
|
||||
> depth = 2
|
||||
> antecedent_limit = 50
|
||||
> antecedent_batch_size = 512
|
||||
>
|
||||
> [model.tok2vec]
|
||||
> @architectures = "spacy-transformers.TransformerListener.v1"
|
||||
> grad_factor = 1.0
|
||||
> upstream = "transformer"
|
||||
> pooling = {"@layers":"reduce_mean.v1"}
|
||||
> ```
|
||||
|
||||
The `Coref` model architecture is a Thinc `Model`.
|
||||
|
||||
| Name | Description |
|
||||
| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
|
||||
| `distance_embedding_size` | A representation of the distance between candidates. ~~int~~ |
|
||||
| `dropout` | The dropout to use internally. Unlike some Thinc models, this has separate dropout for the internal PyTorch layers. ~~float~~ |
|
||||
| `hidden_size` | Size of the main internal layers. ~~int~~ |
|
||||
| `depth` | Depth of the internal network. ~~int~~ |
|
||||
| `antecedent_limit` | How many candidate antecedents to keep after rough scoring. This has a significant effect on memory usage. Typical values would be 50 to 200, or higher for very long documents. ~~int~~ |
|
||||
| `antecedent_batch_size` | Internal batch size. ~~int~~ |
|
||||
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
|
||||
|
||||
### spacy-experimental.SpanResolver.v1 {#SpanResolver tag="experimental"}
|
||||
|
||||
> #### Example Config
|
||||
>
|
||||
> ```ini
|
||||
>
|
||||
> [model]
|
||||
> @architectures = "spacy-experimental.SpanResolver.v1"
|
||||
> hidden_size = 1024
|
||||
> distance_embedding_size = 64
|
||||
> conv_channels = 4
|
||||
> window_size = 1
|
||||
> max_distance = 128
|
||||
> prefix = "coref_head_clusters"
|
||||
>
|
||||
> [model.tok2vec]
|
||||
> @architectures = "spacy-transformers.TransformerListener.v1"
|
||||
> grad_factor = 1.0
|
||||
> upstream = "transformer"
|
||||
> pooling = {"@layers":"reduce_mean.v1"}
|
||||
> ```
|
||||
|
||||
The `SpanResolver` model architecture is a Thinc `Model`. Note that
|
||||
`MentionClusters` is `List[List[Tuple[int, int]]]`.
|
||||
|
||||
| Name | Description |
|
||||
| ------------------------- | -------------------------------------------------------------------------------------------------------------------- |
|
||||
| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
|
||||
| `hidden_size` | Size of the main internal layers. ~~int~~ |
|
||||
| `distance_embedding_size` | A representation of the distance between two candidates. ~~int~~ |
|
||||
| `conv_channels` | The number of channels in the internal CNN. ~~int~~ |
|
||||
| `window_size` | The number of neighboring tokens to consider in the internal CNN. `1` means consider one token on each side. ~~int~~ |
|
||||
| `max_distance` | The longest possible length of a predicted span. ~~int~~ |
|
||||
| `prefix` | The prefix that indicates spans to use for input data. ~~string~~ |
|
||||
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[MentionClusters]]~~ |
|
||||
|
|
|
|||
|
|
@ -77,14 +77,15 @@ $ python -m spacy info [--markdown] [--silent] [--exclude]
|
|||
$ python -m spacy info [model] [--markdown] [--silent] [--exclude]
|
||||
```
|
||||
|
||||
| Name | Description |
|
||||
| ------------------------------------------------ | --------------------------------------------------------------------------------------------- |
|
||||
| `model` | A trained pipeline, i.e. package name or path (optional). ~~Optional[str] \(option)~~ |
|
||||
| `--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)~~ |
|
||||
| `--exclude`, `-e` | Comma-separated keys to exclude from the print-out. Defaults to `"labels"`. ~~Optional[str]~~ |
|
||||
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
|
||||
| **PRINTS** | Information about your spaCy installation. |
|
||||
| Name | Description |
|
||||
| ------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------- |
|
||||
| `model` | A trained pipeline, i.e. package name or path (optional). ~~Optional[str] \(option)~~ |
|
||||
| `--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)~~ |
|
||||
| `--exclude`, `-e` | Comma-separated keys to exclude from the print-out. Defaults to `"labels"`. ~~Optional[str]~~ |
|
||||
| `--url`, `-u` <Tag variant="new">3.5.0</Tag> | Print the URL to download the most recent compatible version of the pipeline. Requires a pipeline name. ~~bool (flag)~~ |
|
||||
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
|
||||
| **PRINTS** | Information about your spaCy installation. |
|
||||
|
||||
## validate {#validate new="2" tag="command"}
|
||||
|
||||
|
|
|
|||
353
website/docs/api/coref.md
Normal file
353
website/docs/api/coref.md
Normal file
|
|
@ -0,0 +1,353 @@
|
|||
---
|
||||
title: CoreferenceResolver
|
||||
tag: class,experimental
|
||||
source: spacy-experimental/coref/coref_component.py
|
||||
teaser: 'Pipeline component for word-level coreference resolution'
|
||||
api_base_class: /api/pipe
|
||||
api_string_name: coref
|
||||
api_trainable: true
|
||||
---
|
||||
|
||||
> #### Installation
|
||||
>
|
||||
> ```bash
|
||||
> $ pip install -U spacy-experimental
|
||||
> ```
|
||||
|
||||
<Infobox title="Important note" variant="warning">
|
||||
|
||||
This component is not yet integrated into spaCy core, and is available via the
|
||||
extension package
|
||||
[`spacy-experimental`](https://github.com/explosion/spacy-experimental) starting
|
||||
in version 0.6.0. It exposes the component via
|
||||
[entry points](/usage/saving-loading/#entry-points), so if you have the package
|
||||
installed, using `factory = "experimental_coref"` in your
|
||||
[training config](/usage/training#config) or
|
||||
`nlp.add_pipe("experimental_coref")` will work out-of-the-box.
|
||||
|
||||
</Infobox>
|
||||
|
||||
A `CoreferenceResolver` component groups tokens into clusters that refer to the
|
||||
same thing. Clusters are represented as SpanGroups that start with a prefix
|
||||
(`coref_clusters` by default).
|
||||
|
||||
A `CoreferenceResolver` component can be paired with a
|
||||
[`SpanResolver`](/api/span-resolver) to expand single tokens to spans.
|
||||
|
||||
## Assigned Attributes {#assigned-attributes}
|
||||
|
||||
Predictions will be saved to `Doc.spans` as a [`SpanGroup`](/api/spangroup). The
|
||||
span key will be a prefix plus a serial number referring to the coreference
|
||||
cluster, starting from zero.
|
||||
|
||||
The span key prefix defaults to `"coref_clusters"`, but can be passed as a
|
||||
parameter.
|
||||
|
||||
| Location | Value |
|
||||
| ------------------------------------------ | ------------------------------------------------------------------------------------------------------- |
|
||||
| `Doc.spans[prefix + "_" + cluster_number]` | One coreference cluster, represented as single-token spans. Cluster numbers start from 1. ~~SpanGroup~~ |
|
||||
|
||||
## Config and implementation {#config}
|
||||
|
||||
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). See the
|
||||
[model architectures](/api/architectures#coref-architectures) documentation for
|
||||
details on the architectures and their arguments and hyperparameters.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> from spacy_experimental.coref.coref_component import DEFAULT_COREF_MODEL
|
||||
> from spacy_experimental.coref.coref_util import DEFAULT_CLUSTER_PREFIX
|
||||
> config={
|
||||
> "model": DEFAULT_COREF_MODEL,
|
||||
> "span_cluster_prefix": DEFAULT_CLUSTER_PREFIX,
|
||||
> },
|
||||
> nlp.add_pipe("experimental_coref", config=config)
|
||||
> ```
|
||||
|
||||
| Setting | Description |
|
||||
| --------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [Coref](/api/architectures#Coref). ~~Model~~ |
|
||||
| `span_cluster_prefix` | The prefix for the keys for clusters saved to `doc.spans`. Defaults to `coref_clusters`. ~~str~~ |
|
||||
|
||||
## CoreferenceResolver.\_\_init\_\_ {#init tag="method"}
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> # Construction via add_pipe with default model
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
>
|
||||
> # Construction via add_pipe with custom model
|
||||
> config = {"model": {"@architectures": "my_coref.v1"}}
|
||||
> coref = nlp.add_pipe("experimental_coref", config=config)
|
||||
>
|
||||
> # Construction from class
|
||||
> from spacy_experimental.coref.coref_component import CoreferenceResolver
|
||||
> coref = CoreferenceResolver(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 | 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_ | |
|
||||
| `span_cluster_prefix` | The prefix for the key for saving clusters of spans. ~~bool~~ |
|
||||
|
||||
## CoreferenceResolver.\_\_call\_\_ {#call tag="method"}
|
||||
|
||||
Apply the pipe to one document. The document is modified in place and returned.
|
||||
This usually happens under the hood when the `nlp` object is called on a text
|
||||
and all pipeline components are applied to the `Doc` in order. Both
|
||||
[`__call__`](/api/coref#call) and [`pipe`](/api/coref#pipe) delegate to the
|
||||
[`predict`](/api/coref#predict) and
|
||||
[`set_annotations`](/api/coref#set_annotations) methods.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> doc = nlp("This is a sentence.")
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> # This usually happens under the hood
|
||||
> processed = coref(doc)
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ----------- | -------------------------------- |
|
||||
| `doc` | The document to process. ~~Doc~~ |
|
||||
| **RETURNS** | The processed document. ~~Doc~~ |
|
||||
|
||||
## CoreferenceResolver.pipe {#pipe tag="method"}
|
||||
|
||||
Apply the pipe to a stream of documents. This usually happens under the hood
|
||||
when the `nlp` object is called on a text and all pipeline components are
|
||||
applied to the `Doc` in order. Both [`__call__`](/api/coref#call) and
|
||||
[`pipe`](/api/coref#pipe) delegate to the [`predict`](/api/coref#predict) and
|
||||
[`set_annotations`](/api/coref#set_annotations) methods.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> for doc in coref.pipe(docs, batch_size=50):
|
||||
> pass
|
||||
> ```
|
||||
|
||||
| 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~~ |
|
||||
|
||||
## CoreferenceResolver.initialize {#initialize tag="method"}
|
||||
|
||||
Initialize the component for training. `get_examples` should be a function that
|
||||
returns an iterable of [`Example`](/api/example) objects. **At least one example
|
||||
should be supplied.** The data examples are used to **initialize the model** of
|
||||
the component and can either be the full training data or a representative
|
||||
sample. Initialization includes validating the network,
|
||||
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
||||
setting up the label scheme based on the data. This method is typically called
|
||||
by [`Language.initialize`](/api/language#initialize).
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> coref.initialize(lambda: examples, nlp=nlp)
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
||||
|
||||
## CoreferenceResolver.predict {#predict tag="method"}
|
||||
|
||||
Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
|
||||
modifying them. Clusters are returned as a list of `MentionClusters`, one for
|
||||
each input `Doc`. A `MentionClusters` instance is just a list of lists of pairs
|
||||
of `int`s, where each item corresponds to a cluster, and the `int`s correspond
|
||||
to token indices.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> clusters = coref.predict([doc1, doc2])
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ----------- | ---------------------------------------------------------------------------- |
|
||||
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
|
||||
| **RETURNS** | The predicted coreference clusters for the `docs`. ~~List[MentionClusters]~~ |
|
||||
|
||||
## CoreferenceResolver.set_annotations {#set_annotations tag="method"}
|
||||
|
||||
Modify a batch of documents, saving coreference clusters in `Doc.spans`.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> clusters = coref.predict([doc1, doc2])
|
||||
> coref.set_annotations([doc1, doc2], clusters)
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ---------- | ---------------------------------------------------------------------------- |
|
||||
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
|
||||
| `clusters` | The predicted coreference clusters for the `docs`. ~~List[MentionClusters]~~ |
|
||||
|
||||
## CoreferenceResolver.update {#update tag="method"}
|
||||
|
||||
Learn from a batch of [`Example`](/api/example) objects. Delegates to
|
||||
[`predict`](/api/coref#predict).
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> optimizer = nlp.initialize()
|
||||
> losses = coref.update(examples, sgd=optimizer)
|
||||
> ```
|
||||
|
||||
| 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]~~ |
|
||||
|
||||
## CoreferenceResolver.create_optimizer {#create_optimizer tag="method"}
|
||||
|
||||
Create an optimizer for the pipeline component.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> optimizer = coref.create_optimizer()
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ----------- | ---------------------------- |
|
||||
| **RETURNS** | The optimizer. ~~Optimizer~~ |
|
||||
|
||||
## CoreferenceResolver.use_params {#use_params tag="method, contextmanager"}
|
||||
|
||||
Modify the pipe's model, to use the given parameter values. At the end of the
|
||||
context, the original parameters are restored.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> with coref.use_params(optimizer.averages):
|
||||
> coref.to_disk("/best_model")
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| -------- | -------------------------------------------------- |
|
||||
| `params` | The parameter values to use in the model. ~~dict~~ |
|
||||
|
||||
## CoreferenceResolver.to_disk {#to_disk tag="method"}
|
||||
|
||||
Serialize the pipe to disk.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> coref.to_disk("/path/to/coref")
|
||||
> ```
|
||||
|
||||
| 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]~~ |
|
||||
|
||||
## CoreferenceResolver.from_disk {#from_disk tag="method"}
|
||||
|
||||
Load the pipe from disk. Modifies the object in place and returns it.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> coref.from_disk("/path/to/coref")
|
||||
> ```
|
||||
|
||||
| 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 `CoreferenceResolver` object. ~~CoreferenceResolver~~ |
|
||||
|
||||
## CoreferenceResolver.to_bytes {#to_bytes tag="method"}
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> coref_bytes = coref.to_bytes()
|
||||
> ```
|
||||
|
||||
Serialize the pipe to a bytestring, including the `KnowledgeBase`.
|
||||
|
||||
| Name | Description |
|
||||
| -------------- | ------------------------------------------------------------------------------------------- |
|
||||
| _keyword-only_ | |
|
||||
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
||||
| **RETURNS** | The serialized form of the `CoreferenceResolver` object. ~~bytes~~ |
|
||||
|
||||
## CoreferenceResolver.from_bytes {#from_bytes tag="method"}
|
||||
|
||||
Load the pipe from a bytestring. Modifies the object in place and returns it.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> coref_bytes = coref.to_bytes()
|
||||
> coref = nlp.add_pipe("experimental_coref")
|
||||
> coref.from_bytes(coref_bytes)
|
||||
> ```
|
||||
|
||||
| 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 `CoreferenceResolver` object. ~~CoreferenceResolver~~ |
|
||||
|
||||
## Serialization fields {#serialization-fields}
|
||||
|
||||
During serialization, spaCy will export several data fields used to restore
|
||||
different aspects of the object. If needed, you can exclude them from
|
||||
serialization by passing in the string names via the `exclude` argument.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> data = coref.to_disk("/path", exclude=["vocab"])
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ------- | -------------------------------------------------------------- |
|
||||
| `vocab` | The shared [`Vocab`](/api/vocab). |
|
||||
| `cfg` | The config file. You usually don't want to exclude this. |
|
||||
| `model` | The binary model data. You usually don't want to exclude this. |
|
||||
|
|
@ -395,12 +395,13 @@ file to keep track of your settings and hyperparameters and your own
|
|||
> "pos": List[str],
|
||||
> "morphs": List[str],
|
||||
> "sent_starts": List[Optional[bool]],
|
||||
> "deps": List[string],
|
||||
> "deps": List[str],
|
||||
> "heads": List[int],
|
||||
> "entities": List[str],
|
||||
> "entities": List[(int, int, str)],
|
||||
> "cats": Dict[str, float],
|
||||
> "links": Dict[(int, int), dict],
|
||||
> "spans": Dict[str, List[Tuple]],
|
||||
> }
|
||||
> ```
|
||||
|
||||
|
|
@ -417,9 +418,10 @@ file to keep track of your settings and hyperparameters and your own
|
|||
| `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]]~~ |
|
||||
| `entities` | **Option 2:** List of `(start_char, end_char, 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]~~ |
|
||||
| `spans` | Dictionary of `spans_key`/`List[Tuple]` pairs defining the spans for each spans key as `(start_char, end_char, label, kb_id)` tuples. ~~Dict[str, List[Tuple[int, int, str, str]]~~ |
|
||||
|
||||
<Infobox title="Notes and caveats">
|
||||
|
||||
|
|
|
|||
|
|
@ -62,7 +62,7 @@ of relations, see the usage guide on
|
|||
|
||||
</Infobox>
|
||||
|
||||
### Operators
|
||||
### Operators {#operators}
|
||||
|
||||
The following operators are supported by the `DependencyMatcher`, most of which
|
||||
come directly from
|
||||
|
|
@ -82,6 +82,11 @@ come directly from
|
|||
| `A $- B` | `B` is a left immediate sibling of `A`, i.e. `A` and `B` have the same parent and `A.i == B.i + 1`. |
|
||||
| `A $++ B` | `B` is a right sibling of `A`, i.e. `A` and `B` have the same parent and `A.i < B.i`. |
|
||||
| `A $-- B` | `B` is a left sibling of `A`, i.e. `A` and `B` have the same parent and `A.i > B.i`. |
|
||||
| `A >++ B` | `B` is a right child of `A`, i.e. `A` is a parent of `B` and `A.i < B.i` _(not in Semgrex)_. |
|
||||
| `A >-- B` | `B` is a left child of `A`, i.e. `A` is a parent of `B` and `A.i > B.i` _(not in Semgrex)_. |
|
||||
| `A <++ B` | `B` is a right parent of `A`, i.e. `A` is a child of `B` and `A.i < B.i` _(not in Semgrex)_. |
|
||||
| `A <-- B` | `B` is a left parent of `A`, i.e. `A` is a child of `B` and `A.i > B.i` _(not in Semgrex)_. |
|
||||
|
||||
|
||||
## DependencyMatcher.\_\_init\_\_ {#init tag="method"}
|
||||
|
||||
|
|
|
|||
|
|
@ -158,10 +158,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/dependencyparser#call) and
|
|||
## DependencyParser.initialize {#initialize tag="method" new="3"}
|
||||
|
||||
Initialize the component for training. `get_examples` should be a function that
|
||||
returns an iterable of [`Example`](/api/example) objects. The data examples are
|
||||
used to **initialize the model** of the component and can either be the full
|
||||
training data or a representative sample. Initialization includes validating the
|
||||
network,
|
||||
returns an iterable of [`Example`](/api/example) objects. **At least one example
|
||||
should be supplied.** The data examples are used to **initialize the model** of
|
||||
the component and can either be the full training data or a representative
|
||||
sample. Initialization includes validating the network,
|
||||
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
||||
setting up the label scheme based on the data. This method is typically called
|
||||
by [`Language.initialize`](/api/language#initialize) and lets you customize
|
||||
|
|
@ -179,7 +179,7 @@ This method was previously called `begin_training`.
|
|||
>
|
||||
> ```python
|
||||
> parser = nlp.add_pipe("parser")
|
||||
> parser.initialize(lambda: [], nlp=nlp)
|
||||
> parser.initialize(lambda: examples, nlp=nlp)
|
||||
> ```
|
||||
>
|
||||
> ```ini
|
||||
|
|
@ -193,7 +193,7 @@ This method was previously called `begin_training`.
|
|||
|
||||
| Name | Description |
|
||||
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
||||
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Dict[str, Dict[str, int]]]~~ |
|
||||
|
|
|
|||
|
|
@ -31,21 +31,21 @@ 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 | Description |
|
||||
| ---------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `vocab` | A storage container for lexical types. ~~Vocab~~ |
|
||||
| `words` | A list of strings or integer hash values to add to the document as words. ~~Optional[List[Union[str,int]]]~~ |
|
||||
| `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]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `user\_data` | Optional extra data to attach to the Doc. ~~Dict~~ |
|
||||
| `tags` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.tag` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `pos` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.pos` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `morphs` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.morph` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `lemmas` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.lemma` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `heads` <Tag variant="new">3</Tag> | A list of values, of the same length as `words`, to assign as the head for each word. Head indices are the absolute position of the head in the `Doc`. Defaults to `None`. ~~Optional[List[int]]~~ |
|
||||
| `deps` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.dep` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `sent_starts` <Tag variant="new">3</Tag> | A list of values, of the same length as `words`, to assign as `token.is_sent_start`. Will be overridden by heads if `heads` is provided. Defaults to `None`. ~~Optional[List[Optional[bool]]]~~ |
|
||||
| `ents` <Tag variant="new">3</Tag> | A list of strings, of the same length of `words`, to assign the token-based IOB tag. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| Name | Description |
|
||||
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `vocab` | A storage container for lexical types. ~~Vocab~~ |
|
||||
| `words` | A list of strings or integer hash values to add to the document as words. ~~Optional[List[Union[str,int]]]~~ |
|
||||
| `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]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `user\_data` | Optional extra data to attach to the Doc. ~~Dict~~ |
|
||||
| `tags` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.tag` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `pos` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.pos` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `morphs` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.morph` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `lemmas` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.lemma` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `heads` <Tag variant="new">3</Tag> | A list of values, of the same length as `words`, to assign as the head for each word. Head indices are the absolute position of the head in the `Doc`. Defaults to `None`. ~~Optional[List[int]]~~ |
|
||||
| `deps` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.dep` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
| `sent_starts` <Tag variant="new">3</Tag> | A list of values, of the same length as `words`, to assign as `token.is_sent_start`. Will be overridden by heads if `heads` is provided. Defaults to `None`. ~~Optional[List[Union[bool, int, None]]]~~ |
|
||||
| `ents` <Tag variant="new">3</Tag> | A list of strings, of the same length of `words`, to assign the token-based IOB tag. Defaults to `None`. ~~Optional[List[str]]~~ |
|
||||
|
||||
## Doc.\_\_getitem\_\_ {#getitem tag="method"}
|
||||
|
||||
|
|
|
|||
|
|
@ -141,10 +141,10 @@ and [`pipe`](/api/edittreelemmatizer#pipe) delegate to the
|
|||
## EditTreeLemmatizer.initialize {#initialize tag="method" new="3"}
|
||||
|
||||
Initialize the component for training. `get_examples` should be a function that
|
||||
returns an iterable of [`Example`](/api/example) objects. The data examples are
|
||||
used to **initialize the model** of the component and can either be the full
|
||||
training data or a representative sample. Initialization includes validating the
|
||||
network,
|
||||
returns an iterable of [`Example`](/api/example) objects. **At least one example
|
||||
should be supplied.** The data examples are used to **initialize the model** of
|
||||
the component and can either be the full training data or a representative
|
||||
sample. Initialization includes validating the network,
|
||||
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
||||
setting up the label scheme based on the data. This method is typically called
|
||||
by [`Language.initialize`](/api/language#initialize) and lets you customize
|
||||
|
|
@ -156,7 +156,7 @@ config.
|
|||
>
|
||||
> ```python
|
||||
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
||||
> lemmatizer.initialize(lambda: [], nlp=nlp)
|
||||
> lemmatizer.initialize(lambda: examples, nlp=nlp)
|
||||
> ```
|
||||
>
|
||||
> ```ini
|
||||
|
|
@ -170,7 +170,7 @@ config.
|
|||
|
||||
| Name | Description |
|
||||
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
||||
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ |
|
||||
|
|
|
|||
|
|
@ -185,10 +185,10 @@ with the current vocab.
|
|||
## EntityLinker.initialize {#initialize tag="method" new="3"}
|
||||
|
||||
Initialize the component for training. `get_examples` should be a function that
|
||||
returns an iterable of [`Example`](/api/example) objects. The data examples are
|
||||
used to **initialize the model** of the component and can either be the full
|
||||
training data or a representative sample. Initialization includes validating the
|
||||
network,
|
||||
returns an iterable of [`Example`](/api/example) objects. **At least one example
|
||||
should be supplied.** The data examples are used to **initialize the model** of
|
||||
the component and can either be the full training data or a representative
|
||||
sample. Initialization includes validating the network,
|
||||
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
||||
setting up the label scheme based on the data. This method is typically called
|
||||
by [`Language.initialize`](/api/language#initialize).
|
||||
|
|
@ -208,15 +208,15 @@ This method was previously called `begin_training`.
|
|||
>
|
||||
> ```python
|
||||
> entity_linker = nlp.add_pipe("entity_linker")
|
||||
> entity_linker.initialize(lambda: [], nlp=nlp, kb_loader=my_kb)
|
||||
> entity_linker.initialize(lambda: examples, nlp=nlp, kb_loader=my_kb)
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
||||
| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
|
||||
| Name | Description |
|
||||
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
||||
| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
|
||||
|
||||
## EntityLinker.predict {#predict tag="method"}
|
||||
|
||||
|
|
|
|||
|
|
@ -154,10 +154,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/entityrecognizer#call) and
|
|||
## EntityRecognizer.initialize {#initialize tag="method" new="3"}
|
||||
|
||||
Initialize the component for training. `get_examples` should be a function that
|
||||
returns an iterable of [`Example`](/api/example) objects. The data examples are
|
||||
used to **initialize the model** of the component and can either be the full
|
||||
training data or a representative sample. Initialization includes validating the
|
||||
network,
|
||||
returns an iterable of [`Example`](/api/example) objects. **At least one example
|
||||
should be supplied.** The data examples are used to **initialize the model** of
|
||||
the component and can either be the full training data or a representative
|
||||
sample. Initialization includes validating the network,
|
||||
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
||||
setting up the label scheme based on the data. This method is typically called
|
||||
by [`Language.initialize`](/api/language#initialize) and lets you customize
|
||||
|
|
@ -175,7 +175,7 @@ This method was previously called `begin_training`.
|
|||
>
|
||||
> ```python
|
||||
> ner = nlp.add_pipe("ner")
|
||||
> ner.initialize(lambda: [], nlp=nlp)
|
||||
> ner.initialize(lambda: examples, nlp=nlp)
|
||||
> ```
|
||||
>
|
||||
> ```ini
|
||||
|
|
@ -189,7 +189,7 @@ This method was previously called `begin_training`.
|
|||
|
||||
| Name | Description |
|
||||
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
||||
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Dict[str, Dict[str, int]]]~~ |
|
||||
|
|
|
|||
|
|
@ -23,11 +23,13 @@ both documents.
|
|||
> ```python
|
||||
> from spacy.tokens import Doc
|
||||
> from spacy.training import Example
|
||||
>
|
||||
> words = ["hello", "world", "!"]
|
||||
> spaces = [True, False, False]
|
||||
> predicted = Doc(nlp.vocab, words=words, spaces=spaces)
|
||||
> reference = parse_gold_doc(my_data)
|
||||
> pred_words = ["Apply", "some", "sunscreen"]
|
||||
> pred_spaces = [True, True, False]
|
||||
> gold_words = ["Apply", "some", "sun", "screen"]
|
||||
> gold_spaces = [True, True, False, False]
|
||||
> gold_tags = ["VERB", "DET", "NOUN", "NOUN"]
|
||||
> predicted = Doc(nlp.vocab, words=pred_words, spaces=pred_spaces)
|
||||
> reference = Doc(nlp.vocab, words=gold_words, spaces=gold_spaces, tags=gold_tags)
|
||||
> example = Example(predicted, reference)
|
||||
> ```
|
||||
|
||||
|
|
@ -286,10 +288,14 @@ Calculate alignment tables between two tokenizations.
|
|||
|
||||
### Alignment attributes {#alignment-attributes"}
|
||||
|
||||
| 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~~ |
|
||||
Alignment attributes are managed using `AlignmentArray`, which is a
|
||||
simplified version of Thinc's [Ragged](https://thinc.ai/docs/api-types#ragged)
|
||||
type that only supports the `data` and `length` attributes.
|
||||
|
||||
| Name | Description |
|
||||
| ----- | ------------------------------------------------------------------------------------- |
|
||||
| `x2y` | The `AlignmentArray` object holding the alignment from `x` to `y`. ~~AlignmentArray~~ |
|
||||
| `y2x` | The `AlignmentArray` object holding the alignment from `y` to `x`. ~~AlignmentArray~~ |
|
||||
|
||||
<Infobox title="Important note" variant="warning">
|
||||
|
||||
|
|
@ -309,10 +315,10 @@ tokenizations add up to the same string. For example, you'll be able to align
|
|||
> spacy_tokens = ["obama", "'s", "podcast"]
|
||||
> alignment = Alignment.from_strings(bert_tokens, spacy_tokens)
|
||||
> a2b = alignment.x2y
|
||||
> assert list(a2b.dataXd) == [0, 1, 1, 2]
|
||||
> assert list(a2b.data) == [0, 1, 1, 2]
|
||||
> ```
|
||||
>
|
||||
> If `a2b.dataXd[1] == a2b.dataXd[2] == 1`, that means that `A[1]` (`"'"`) and
|
||||
> If `a2b.data[1] == a2b.data[2] == 1`, that means that `A[1]` (`"'"`) and
|
||||
> `A[2]` (`"s"`) both align to `B[1]` (`"'s"`).
|
||||
|
||||
### Alignment.from_strings {#classmethod tag="function"}
|
||||
|
|
|
|||
|
|
@ -63,17 +63,18 @@ spaCy loads a model under the hood based on its
|
|||
> nlp = Language.from_config(config)
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `config` | The loaded config. ~~Union[Dict[str, Any], Config]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~ |
|
||||
| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [`nlp.enable_pipe`](/api/language#enable_pipe). ~~List[str]~~ |
|
||||
| `exclude` | Names of pipeline components to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~List[str]~~ |
|
||||
| `meta` | [Meta data](/api/data-formats#meta) overrides. ~~Dict[str, Any]~~ |
|
||||
| `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~~ |
|
||||
| Name | Description |
|
||||
| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `config` | The loaded config. ~~Union[Dict[str, Any], Config]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~ |
|
||||
| `disable` | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [`nlp.enable_pipe`](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~ |
|
||||
| `enable` <Tag variant="new">3.4</Tag> | Name(s) of pipeline component(s) to [enable](/usage/processing-pipelines#disabling). All other pipes will be disabled, but can be enabled again using [`nlp.enable_pipe`](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~ |
|
||||
| `exclude` | Name(s) of pipeline component(s) to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~Union[str, Iterable[str]]~~ |
|
||||
| `meta` | [Meta data](/api/data-formats#meta) overrides. ~~Dict[str, Any]~~ |
|
||||
| `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"}
|
||||
|
||||
|
|
@ -163,6 +164,9 @@ examples, see the
|
|||
Apply the pipeline to some text. The text can span multiple sentences, and can
|
||||
contain arbitrary whitespace. Alignment into the original string is preserved.
|
||||
|
||||
Instead of text, a `Doc` can be passed as input, in which case tokenization is
|
||||
skipped, but the rest of the pipeline is run.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
|
|
@ -172,7 +176,7 @@ contain arbitrary whitespace. Alignment into the original string is preserved.
|
|||
|
||||
| Name | Description |
|
||||
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `text` | The text to be processed. ~~str~~ |
|
||||
| `text` | The text to be processed, or a Doc. ~~Union[str, Doc]~~ |
|
||||
| _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]]]~~ |
|
||||
|
|
@ -183,6 +187,9 @@ contain arbitrary whitespace. Alignment into the original string is preserved.
|
|||
Process texts as a stream, and yield `Doc` objects in order. This is usually
|
||||
more efficient than processing texts one-by-one.
|
||||
|
||||
Instead of text, a `Doc` object can be passed as input. In this case
|
||||
tokenization is skipped but the rest of the pipeline is run.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
|
|
@ -193,7 +200,7 @@ more efficient than processing texts one-by-one.
|
|||
|
||||
| Name | Description |
|
||||
| ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `texts` | A sequence of strings. ~~Iterable[str]~~ |
|
||||
| `texts` | A sequence of strings (or `Doc` objects). ~~Iterable[Union[str, Doc]]~~ |
|
||||
| _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. ~~Optional[int]~~ |
|
||||
|
|
@ -695,8 +702,8 @@ As of spaCy v3.0, the `disable_pipes` method has been renamed to `select_pipes`:
|
|||
| Name | Description |
|
||||
| -------------- | ------------------------------------------------------------------------------------------------------ |
|
||||
| _keyword-only_ | |
|
||||
| `disable` | Name(s) of pipeline components to disable. ~~Optional[Union[str, Iterable[str]]]~~ |
|
||||
| `enable` | Name(s) of pipeline components that will not be disabled. ~~Optional[Union[str, Iterable[str]]]~~ |
|
||||
| `disable` | Name(s) of pipeline component(s) to disable. ~~Optional[Union[str, Iterable[str]]]~~ |
|
||||
| `enable` | Name(s) of pipeline component(s) 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"}
|
||||
|
|
|
|||
|
|
@ -248,6 +248,59 @@ added to an existing vectors table. See more details in
|
|||
|
||||
## Loggers {#loggers}
|
||||
|
||||
These functions are available from `@spacy.registry.loggers`.
|
||||
|
||||
### spacy.ConsoleLogger.v1 {#ConsoleLogger_v1}
|
||||
|
||||
> #### Example config
|
||||
>
|
||||
> ```ini
|
||||
> [training.logger]
|
||||
> @loggers = "spacy.ConsoleLogger.v1"
|
||||
> progress_bar = true
|
||||
> ```
|
||||
|
||||
Writes the results of a training step to the console in a tabular format.
|
||||
|
||||
<Accordion title="Example console output" spaced>
|
||||
|
||||
```cli
|
||||
$ python -m spacy train config.cfg
|
||||
```
|
||||
|
||||
```
|
||||
ℹ Using CPU
|
||||
ℹ Loading config and nlp from: config.cfg
|
||||
ℹ Pipeline: ['tok2vec', 'tagger']
|
||||
ℹ Start training
|
||||
ℹ Training. Initial learn rate: 0.0
|
||||
|
||||
E # LOSS TOK2VEC LOSS TAGGER TAG_ACC SCORE
|
||||
--- ------ ------------ ----------- ------- ------
|
||||
0 0 0.00 86.20 0.22 0.00
|
||||
0 200 3.08 18968.78 34.00 0.34
|
||||
0 400 31.81 22539.06 33.64 0.34
|
||||
0 600 92.13 22794.91 43.80 0.44
|
||||
0 800 183.62 21541.39 56.05 0.56
|
||||
0 1000 352.49 25461.82 65.15 0.65
|
||||
0 1200 422.87 23708.82 71.84 0.72
|
||||
0 1400 601.92 24994.79 76.57 0.77
|
||||
0 1600 662.57 22268.02 80.20 0.80
|
||||
0 1800 1101.50 28413.77 82.56 0.83
|
||||
0 2000 1253.43 28736.36 85.00 0.85
|
||||
0 2200 1411.02 28237.53 87.42 0.87
|
||||
0 2400 1605.35 28439.95 88.70 0.89
|
||||
```
|
||||
|
||||
Note that the cumulative loss keeps increasing within one epoch, but should
|
||||
start decreasing across epochs.
|
||||
|
||||
</Accordion>
|
||||
|
||||
| Name | Description |
|
||||
| -------------- | --------------------------------------------------------- |
|
||||
| `progress_bar` | Whether the logger should print the progress bar ~~bool~~ |
|
||||
|
||||
Logging utilities for spaCy are implemented in the
|
||||
[`spacy-loggers`](https://github.com/explosion/spacy-loggers) repo, and the
|
||||
functions are typically available from `@spacy.registry.loggers`.
|
||||
|
|
|
|||
|
|
@ -199,7 +199,7 @@ will be overwritten.
|
|||
> [{"LOWER": "hello"}, {"LOWER": "world"}],
|
||||
> [{"ORTH": "Google"}, {"ORTH": "Maps"}]
|
||||
> ]
|
||||
> matcher.add("TEST_PATTERNS", patterns)
|
||||
> matcher.add("TEST_PATTERNS", patterns, on_match=on_match)
|
||||
> doc = nlp("HELLO WORLD on Google Maps.")
|
||||
> matches = matcher(doc)
|
||||
> ```
|
||||
|
|
|
|||
|
|
@ -147,10 +147,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/morphologizer#call) and
|
|||
## Morphologizer.initialize {#initialize tag="method"}
|
||||
|
||||
Initialize the component for training. `get_examples` should be a function that
|
||||
returns an iterable of [`Example`](/api/example) objects. The data examples are
|
||||
used to **initialize the model** of the component and can either be the full
|
||||
training data or a representative sample. Initialization includes validating the
|
||||
network,
|
||||
returns an iterable of [`Example`](/api/example) objects. **At least one example
|
||||
should be supplied.** The data examples are used to **initialize the model** of
|
||||
the component and can either be the full training data or a representative
|
||||
sample. Initialization includes validating the network,
|
||||
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
||||
setting up the label scheme based on the data. This method is typically called
|
||||
by [`Language.initialize`](/api/language#initialize) and lets you customize
|
||||
|
|
@ -162,7 +162,7 @@ config.
|
|||
>
|
||||
> ```python
|
||||
> morphologizer = nlp.add_pipe("morphologizer")
|
||||
> morphologizer.initialize(lambda: [], nlp=nlp)
|
||||
> morphologizer.initialize(lambda: examples, nlp=nlp)
|
||||
> ```
|
||||
>
|
||||
> ```ini
|
||||
|
|
@ -176,7 +176,7 @@ config.
|
|||
|
||||
| Name | Description |
|
||||
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
|
||||
| _keyword-only_ | |
|
||||
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
||||
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[dict]~~ |
|
||||
|
|
|
|||
|
|
@ -153,3 +153,36 @@ whole pipeline has run.
|
|||
| `attrs` | A dict of the `Doc` attributes and the values to set them to. Defaults to `{"tensor": None, "_.trf_data": None}` to clean up after `tok2vec` and `transformer` components. ~~dict~~ |
|
||||
| `silent` | If `False`, show warnings if attributes aren't found or can't be set. Defaults to `True`. ~~bool~~ |
|
||||
| **RETURNS** | The modified `Doc` with the modified attributes. ~~Doc~~ |
|
||||
|
||||
## span_cleaner {#span_cleaner tag="function,experimental"}
|
||||
|
||||
Remove `SpanGroup`s from `doc.spans` based on a key prefix. This is used to
|
||||
clean up after the [`CoreferenceResolver`](/api/coref) when it's paired with a
|
||||
[`SpanResolver`](/api/span-resolver).
|
||||
|
||||
<Infobox title="Important note" variant="warning">
|
||||
|
||||
This pipeline function is not yet integrated into spaCy core, and is available
|
||||
via the extension package
|
||||
[`spacy-experimental`](https://github.com/explosion/spacy-experimental) starting
|
||||
in version 0.6.0. It exposes the component via
|
||||
[entry points](/usage/saving-loading/#entry-points), so if you have the package
|
||||
installed, using `factory = "span_cleaner"` in your
|
||||
[training config](/usage/training#config) or `nlp.add_pipe("span_cleaner")` will
|
||||
work out-of-the-box.
|
||||
|
||||
</Infobox>
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> config = {"prefix": "coref_head_clusters"}
|
||||
> nlp.add_pipe("span_cleaner", config=config)
|
||||
> doc = nlp("text")
|
||||
> assert "coref_head_clusters_1" not in doc.spans
|
||||
> ```
|
||||
|
||||
| Setting | Description |
|
||||
| ----------- | ------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `prefix` | A prefix to check `SpanGroup` keys for. Any matching groups will be removed. Defaults to `"coref_head_clusters"`. ~~str~~ |
|
||||
| **RETURNS** | The modified `Doc` with any matching spans removed. ~~Doc~~ |
|
||||
|
|
|
|||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user