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Merge remote-tracking branch 'upstream/master' into store-activations

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Daniël de Kok 2022-06-27 14:14:44 +02:00
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34
.github/azure-steps.yml vendored
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@ -64,12 +64,12 @@ steps:
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 .
@ -93,17 +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

106
.github/contributors/Lucaterre.md vendored Normal file
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@ -0,0 +1,106 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- |---------------|
| Name | Lucas Terriel |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2022-06-20 |
| GitHub username | Lucaterre |
| Website (optional) | |

4
extra/DEVELOPER_DOCS/Code Conventions.md
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@ -455,6 +455,10 @@ Regression tests are tests that refer to bugs reported in specific issues. They
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.
### Testing Cython Code
If you're developing Cython code (`.pyx` files), those extensions will need to be built before the test runner can test that code - otherwise it's going to run the tests with stale code from the last time the extension was built. You can build the extensions locally with `python setup.py build_ext -i`.
### Constructing objects and state
Test functions usually follow the same simple structure: they set up some state, perform the operation you want to test and `assert` conditions that you expect to be true, usually before and after the operation.

2
pyproject.toml
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@ -5,7 +5,7 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc>=8.1.0.dev2,<8.2.0",
"thinc>=8.1.0.dev3,<8.2.0",
"pathy",
"numpy>=1.15.0",
]

2
requirements.txt
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@ -3,7 +3,7 @@ spacy-legacy>=3.0.9,<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
thinc>=8.1.0.dev2,<8.2.0
thinc>=8.1.0.dev3,<8.2.0
ml_datasets>=0.2.0,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.9.1,<1.1.0

4
setup.cfg
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@ -38,7 +38,7 @@ setup_requires =
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc>=8.1.0.dev2,<8.2.0
thinc>=8.1.0.dev3,<8.2.0
install_requires =
# Our libraries
spacy-legacy>=3.0.9,<3.1.0
@ -46,7 +46,7 @@ install_requires =
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.0.dev2,<8.2.0
thinc>=8.1.0.dev3,<8.2.0
wasabi>=0.9.1,<1.1.0
srsly>=2.4.3,<3.0.0
catalogue>=2.0.6,<2.1.0

2
spacy/about.py
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@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy"
__version__ = "3.3.0"
__version__ = "3.4.0"
__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"

11
spacy/tests/parser/test_ner.py
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@ -158,13 +158,18 @@ def test_issue3209():
def test_labels_from_BILUO():
"""Test that labels are inferred correctly when there's a - in label.
"""
"""Test that labels are inferred correctly when there's a - in label."""
nlp = English()
ner = nlp.add_pipe("ner")
ner.add_label("LARGE-ANIMAL")
nlp.initialize()
move_names = ["O", "B-LARGE-ANIMAL", "I-LARGE-ANIMAL", "L-LARGE-ANIMAL", "U-LARGE-ANIMAL"]
move_names = [
"O",
"B-LARGE-ANIMAL",
"I-LARGE-ANIMAL",
"L-LARGE-ANIMAL",
"U-LARGE-ANIMAL",
]
labels = {"LARGE-ANIMAL"}
assert ner.move_names == move_names
assert set(ner.labels) == labels

2
spacy/tests/test_cli.py
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@ -589,6 +589,7 @@ 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
@ -599,6 +600,7 @@ 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

3
spacy/tests/training/test_readers.py
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@ -60,11 +60,12 @@ def test_readers():
assert isinstance(extra_corpus, Callable)
# TODO: enable IMDB test once Stanford servers are back up and running
@pytest.mark.slow
@pytest.mark.parametrize(
"reader,additional_config",
[
("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 10}),
# ("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 10}),
("ml_datasets.dbpedia.v1", {"train_limit": 10, "dev_limit": 10}),
("ml_datasets.cmu_movies.v1", {"limit": 10, "freq_cutoff": 200, "split": 0.8}),
],

20
spacy/training/alignment_array.pyx
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@ -1,33 +1,39 @@
from typing import List
from ..errors import Errors
import numpy
from libc.stdint cimport int32_t
cdef class AlignmentArray:
"""AlignmentArray is similar to Thinc's Ragged with two simplfications:
indexing returns numpy arrays and this type can only be used for CPU arrays.
However, these changes make AlginmentArray more efficient for indexing in a
However, these changes make AlignmentArray more efficient for indexing in a
tight loop."""
__slots__ = []
def __init__(self, alignment: List[List[int]]):
self._lengths = None
self._starts_ends = numpy.zeros(len(alignment) + 1, dtype="i")
cdef int data_len = 0
cdef int outer_len
cdef int idx
self._starts_ends = numpy.zeros(len(alignment) + 1, dtype='int32')
cdef int32_t* starts_ends_ptr = <int32_t*>self._starts_ends.data
for idx, outer in enumerate(alignment):
outer_len = len(outer)
self._starts_ends[idx + 1] = self._starts_ends[idx] + outer_len
starts_ends_ptr[idx + 1] = starts_ends_ptr[idx] + outer_len
data_len += outer_len
self._data = numpy.empty(data_len, dtype="i")
self._lengths = None
self._data = numpy.empty(data_len, dtype="int32")
idx = 0
cdef int32_t* data_ptr = <int32_t*>self._data.data
for outer in alignment:
for inner in outer:
self._data[idx] = inner
data_ptr[idx] = inner
idx += 1
def __getitem__(self, idx):

129
spacy/training/example.pyx
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@ -13,7 +13,7 @@ from .iob_utils import biluo_tags_to_spans, remove_bilu_prefix
from ..errors import Errors, Warnings
from ..pipeline._parser_internals import nonproj
from ..tokens.token cimport MISSING_DEP
from ..util import logger, to_ternary_int
from ..util import logger, to_ternary_int, all_equal
cpdef Doc annotations_to_doc(vocab, tok_annot, doc_annot):
@ -151,50 +151,127 @@ cdef class Example:
self._y_sig = y_sig
return self._cached_alignment
def _get_aligned_vectorized(self, align, gold_values):
# Fast path for Doc attributes/fields that are predominantly a single value,
# i.e., TAG, POS, MORPH.
x2y_single_toks = []
x2y_single_toks_i = []
x2y_multiple_toks = []
x2y_multiple_toks_i = []
# Gather indices of gold tokens aligned to the candidate tokens into two buckets.
# Bucket 1: All tokens that have a one-to-one alignment.
# Bucket 2: All tokens that have a one-to-many alignment.
for idx, token in enumerate(self.predicted):
aligned_gold_i = align[token.i]
aligned_gold_len = len(aligned_gold_i)
if aligned_gold_len == 1:
x2y_single_toks.append(aligned_gold_i.item())
x2y_single_toks_i.append(idx)
elif aligned_gold_len > 1:
x2y_multiple_toks.append(aligned_gold_i)
x2y_multiple_toks_i.append(idx)
# Map elements of the first bucket directly to the output array.
output = numpy.full(len(self.predicted), None)
output[x2y_single_toks_i] = gold_values[x2y_single_toks].squeeze()
# Collapse many-to-one alignments into one-to-one alignments if they
# share the same value. Map to None in all other cases.
for i in range(len(x2y_multiple_toks)):
aligned_gold_values = gold_values[x2y_multiple_toks[i]]
# If all aligned tokens have the same value, use it.
if all_equal(aligned_gold_values):
x2y_multiple_toks[i] = aligned_gold_values[0].item()
else:
x2y_multiple_toks[i] = None
output[x2y_multiple_toks_i] = x2y_multiple_toks
return output.tolist()
def _get_aligned_non_vectorized(self, align, gold_values):
# Slower path for fields that return multiple values (resulting
# in ragged arrays that cannot be vectorized trivially).
output = [None] * len(self.predicted)
for token in self.predicted:
aligned_gold_i = align[token.i]
values = gold_values[aligned_gold_i].ravel()
if len(values) == 1:
output[token.i] = values.item()
elif all_equal(values):
# If all aligned tokens have the same value, use it.
output[token.i] = values[0].item()
return output
def get_aligned(self, field, as_string=False):
"""Return an aligned array for a token attribute."""
align = self.alignment.x2y
gold_values = self.reference.to_array([field])
if len(gold_values.shape) == 1:
output = self._get_aligned_vectorized(align, gold_values)
else:
output = self._get_aligned_non_vectorized(align, gold_values)
vocab = self.reference.vocab
gold_values = self.reference.to_array([field])
output = [None] * len(self.predicted)
for token in self.predicted:
values = gold_values[align[token.i]]
values = values.ravel()
if len(values) == 0:
output[token.i] = None
elif len(values) == 1:
output[token.i] = values[0]
elif len(set(list(values))) == 1:
# If all aligned tokens have the same value, use it.
output[token.i] = values[0]
else:
output[token.i] = None
if as_string and field not in ["ENT_IOB", "SENT_START"]:
output = [vocab.strings[o] if o is not None else o for o in output]
return output
def get_aligned_parse(self, projectivize=True):
cand_to_gold = self.alignment.x2y
gold_to_cand = self.alignment.y2x
aligned_heads = [None] * self.x.length
aligned_deps = [None] * self.x.length
has_deps = [token.has_dep() for token in self.y]
has_heads = [token.has_head() for token in self.y]
heads = [token.head.i for token in self.y]
deps = [token.dep_ for token in self.y]
if projectivize:
proj_heads, proj_deps = nonproj.projectivize(heads, deps)
has_deps = [token.has_dep() for token in self.y]
has_heads = [token.has_head() for token in self.y]
# ensure that missing data remains missing
heads = [h if has_heads[i] else heads[i] for i, h in enumerate(proj_heads)]
deps = [d if has_deps[i] else deps[i] for i, d in enumerate(proj_deps)]
for cand_i in range(self.x.length):
if cand_to_gold.lengths[cand_i] == 1:
gold_i = cand_to_gold[cand_i][0]
if gold_to_cand.lengths[heads[gold_i]] == 1:
aligned_heads[cand_i] = int(gold_to_cand[heads[gold_i]][0])
aligned_deps[cand_i] = deps[gold_i]
return aligned_heads, aligned_deps
# Select all candidate tokens that are aligned to a single gold token.
c2g_single_toks = numpy.where(cand_to_gold.lengths == 1)[0]
# Fetch all aligned gold token incides.
if c2g_single_toks.shape == cand_to_gold.lengths.shape:
# This the most likely case.
gold_i = cand_to_gold[:].squeeze()
else:
gold_i = numpy.vectorize(lambda x: cand_to_gold[int(x)][0])(c2g_single_toks).squeeze()
# Fetch indices of all gold heads for the aligned gold tokens.
heads = numpy.asarray(heads, dtype='i')
gold_head_i = heads[gold_i]
# Select all gold tokens that are heads of the previously selected
# gold tokens (and are aligned to a single candidate token).
g2c_len_heads = gold_to_cand.lengths[gold_head_i]
g2c_len_heads = numpy.where(g2c_len_heads == 1)[0]
g2c_i = numpy.vectorize(lambda x: gold_to_cand[int(x)][0])(gold_head_i[g2c_len_heads]).squeeze()
# Update head/dep alignments with the above.
aligned_heads = numpy.full((self.x.length), None)
aligned_heads[c2g_single_toks[g2c_len_heads]] = g2c_i
deps = numpy.asarray(deps)
aligned_deps = numpy.full((self.x.length), None)
aligned_deps[c2g_single_toks] = deps[gold_i]
return aligned_heads.tolist(), aligned_deps.tolist()
def get_aligned_sent_starts(self):
"""Get list of SENT_START attributes aligned to the predicted tokenization.

7
spacy/util.py
View File

@ -1716,3 +1716,10 @@ def packages_distributions() -> Dict[str, List[str]]:
for pkg in (dist.read_text("top_level.txt") or "").split():
pkg_to_dist[pkg].append(dist.metadata["Name"])
return dict(pkg_to_dist)
def all_equal(iterable):
"""Return True if all the elements are equal to each other
(or if the input is an empty sequence), False otherwise."""
g = itertools.groupby(iterable)
return next(g, True) and not next(g, False)

6
website/docs/api/architectures.md
View File

@ -587,7 +587,7 @@ 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 simply a matter of summing the component features and
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
@ -628,7 +628,7 @@ 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 simply adds a linear layer with softmax activation to predict scores given
model adds a linear layer with softmax activation to predict scores given
the token vectors.
| Name | Description |
@ -920,5 +920,5 @@ 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` simply uses the text of a mention to find its potential
`CandidateGenerator` uses the text of a mention to find its potential
aliases in the `KnowledgeBase`. Note that this function is case-dependent.

78
website/docs/api/attributes.md Normal file
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@ -0,0 +1,78 @@
---
title: Attributes
teaser: Token attributes
source: spacy/attrs.pyx
---
[Token](/api/token) attributes are specified using internal IDs in many places
including:
- [`Matcher` patterns](/api/matcher#patterns),
- [`Doc.to_array`](/api/doc#to_array) and
[`Doc.from_array`](/api/doc#from_array)
- [`Doc.has_annotation`](/api/doc#has_annotation)
- [`MultiHashEmbed`](/api/architectures#MultiHashEmbed) Tok2Vec architecture
`attrs`
> ```python
> import spacy
> from spacy.attrs import DEP
>
> nlp = spacy.blank("en")
> doc = nlp("There are many attributes.")
>
> # DEP always has the same internal value
> assert DEP == 76
>
> # "DEP" is automatically converted to DEP
> assert DEP == nlp.vocab.strings["DEP"]
> assert doc.has_annotation(DEP) == doc.has_annotation("DEP")
>
> # look up IDs in spacy.attrs.IDS
> from spacy.attrs import IDS
> assert IDS["DEP"] == DEP
> ```
All methods automatically convert between the string version of an ID (`"DEP"`)
and the internal integer symbols (`DEP`). The internal IDs can be imported from
`spacy.attrs` or retrieved from the [`StringStore`](/api/stringstore). A map
from string attribute names to internal attribute IDs is stored in
`spacy.attrs.IDS`.
The corresponding [`Token` object attributes](/api/token#attributes) can be
accessed using the same names in lowercase, e.g. `token.orth` or `token.length`.
For attributes that represent string values, the internal integer ID is
accessed as `Token.attr`, e.g. `token.dep`, while the string value can be
retrieved by appending `_` as in `token.dep_`.
| Attribute | Description |
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `DEP` | The token's dependency label. ~~str~~ |
| `ENT_ID` | The token's entity ID (`ent_id`). ~~str~~ |
| `ENT_IOB` | The IOB part of the token's entity tag. Uses custom integer vaues rather than the string store: unset is `0`, `I` is `1`, `O` is `2`, and `B` is `3`. ~~str~~ |
| `ENT_KB_ID` | The token's entity knowledge base ID. ~~str~~ |
| `ENT_TYPE` | The token's entity label. ~~str~~ |
| `IS_ALPHA` | Token text consists of alphabetic characters. ~~bool~~ |
| `IS_ASCII` | Token text consists of ASCII characters. ~~bool~~ |
| `IS_DIGIT` | Token text consists of digits. ~~bool~~ |
| `IS_LOWER` | Token text is in lowercase. ~~bool~~ |
| `IS_PUNCT` | Token is punctuation. ~~bool~~ |
| `IS_SPACE` | Token is whitespace. ~~bool~~ |
| `IS_STOP` | Token is a stop word. ~~bool~~ |
| `IS_TITLE` | Token text is in titlecase. ~~bool~~ |
| `IS_UPPER` | Token text is in uppercase. ~~bool~~ |
| `LEMMA` | The token's lemma. ~~str~~ |
| `LENGTH` | The length of the token text. ~~int~~ |
| `LIKE_EMAIL` | Token text resembles an email address. ~~bool~~ |
| `LIKE_NUM` | Token text resembles a number. ~~bool~~ |
| `LIKE_URL` | Token text resembles a URL. ~~bool~~ |
| `LOWER` | The lowercase form of the token text. ~~str~~ |
| `MORPH` | The token's morphological analysis. ~~MorphAnalysis~~ |
| `NORM` | The normalized form of the token text. ~~str~~ |
| `ORTH` | The exact verbatim text of a token. ~~str~~ |
| `POS` | The token's universal part of speech (UPOS). ~~str~~ |
| `SENT_START` | Token is start of sentence. ~~bool~~ |
| `SHAPE` | The token's shape. ~~str~~ |
| `SPACY` | Token has a trailing space. ~~bool~~ |
| `TAG` | The token's fine-grained part of speech. ~~str~~ |

2
website/docs/api/spanruler.md
View File

@ -2,7 +2,7 @@
title: SpanRuler
tag: class
source: spacy/pipeline/span_ruler.py
new: 3.3.1
new: 3.3
teaser: 'Pipeline component for rule-based span and named entity recognition'
api_string_name: span_ruler
api_trainable: false

7
website/docs/usage/saving-loading.md
View File

@ -203,11 +203,14 @@ the data to and from a JSON file.
```python
### {highlight="16-23,25-30"}
import json
from spacy import Language
from spacy.util import ensure_path
@Language.factory("my_component")
class CustomComponent:
def __init__(self):
def __init__(self, nlp: Language, name: str = "my_component"):
self.name = name
self.data = []
def __call__(self, doc):
@ -231,7 +234,7 @@ class CustomComponent:
# This will receive the directory path + /my_component
data_path = path / "data.json"
with data_path.open("r", encoding="utf8") as f:
self.data = json.loads(f)
self.data = json.load(f)
return self
```

1
website/meta/sidebars.json
View File

@ -124,6 +124,7 @@
{
"label": "Other",
"items": [
{ "text": "Attributes", "url": "/api/attributes" },
{ "text": "Corpus", "url": "/api/corpus" },
{ "text": "KnowledgeBase", "url": "/api/kb" },
{ "text": "Lookups", "url": "/api/lookups" },

45
website/meta/universe.json
View File

@ -1,5 +1,34 @@
{
"resources": [
{
"id": "spacyfishing",
"title": "spaCy fishing",
"slogan": "Named entity disambiguation and linking on Wikidata in spaCy with Entity-Fishing.",
"description": "A spaCy wrapper of Entity-Fishing for named entity disambiguation and linking against a Wikidata knowledge base.",
"github": "Lucaterre/spacyfishing",
"pip": "spacyfishing",
"code_example": [
"import spacy",
"text = 'Victor Hugo and Honoré de Balzac are French writers who lived in Paris.'",
"nlp = spacy.load('en_core_web_sm')",
"nlp.add_pipe('entityfishing')",
"doc = nlp(text)",
"for span in doc.ents:",
" print((ent.text, ent.label_, ent._.kb_qid, ent._.url_wikidata, ent._.nerd_score))",
"# ('Victor Hugo', 'PERSON', 'Q535', 'https://www.wikidata.org/wiki/Q535', 0.972)",
"# ('Honoré de Balzac', 'PERSON', 'Q9711', 'https://www.wikidata.org/wiki/Q9711', 0.9724)",
"# ('French', 'NORP', 'Q121842', 'https://www.wikidata.org/wiki/Q121842', 0.3739)",
"# ('Paris', 'GPE', 'Q90', 'https://www.wikidata.org/wiki/Q90', 0.5652)",
"## Set parameter `extra_info` to `True` and check also span._.description, span._.src_description, span._.normal_term, span._.other_ids"
],
"category": ["models", "pipeline"],
"tags": ["NER", "NEL"],
"author": "Lucas Terriel",
"author_links": {
"twitter": "TerreLuca",
"github": "Lucaterre"
}
},
{
"id": "aim-spacy",
"title": "Aim-spaCy",
@ -55,7 +84,7 @@
"code_language": "python",
"author": "Leap Beyond",
"author_links": {
"github": "https://github.com/LeapBeyond",
"github": "LeapBeyond",
"website": "https://leapbeyond.ai"
},
"code_example": [
@ -78,8 +107,8 @@
"code_language": "python",
"author": "Peter Baumgartner",
"author_links": {
"twitter" : "https://twitter.com/pmbaumgartner",
"github": "https://github.com/pmbaumgartner",
"twitter" : "pmbaumgartner",
"github": "pmbaumgartner",
"website": "https://www.peterbaumgartner.com/"
},
"code_example": [
@ -98,8 +127,8 @@
"code_language": "python",
"author": "Explosion",
"author_links": {
"twitter" : "https://twitter.com/explosion_ai",
"github": "https://github.com/explosion",
"twitter" : "explosion_ai",
"github": "explosion",
"website": "https://explosion.ai/"
},
"code_example": [
@ -571,8 +600,8 @@
"code_language": "python",
"author": "Keith Rozario",
"author_links": {
"twitter" : "https://twitter.com/keithrozario",
"github": "https://github.com/keithrozario",
"twitter" : "keithrozario",
"github": "keithrozario",
"website": "https://www.keithrozario.com"
},
"code_example": [
@ -2295,7 +2324,7 @@
"author": "Daniel Whitenack & Chris Benson",
"author_links": {
"website": "https://changelog.com/practicalai",
"twitter": "https://twitter.com/PracticalAIFM"
"twitter": "PracticalAIFM"
},
"category": ["podcasts"]
},

1
website/src/widgets/quickstart-install.js
View File

@ -24,7 +24,6 @@ const CUDA = {
'11.3': 'cuda113',
'11.4': 'cuda114',
'11.5': 'cuda115',
'11.6': 'cuda116',
}
const LANG_EXTRAS = ['ja'] // only for languages with models