explosion/spaCy
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Merge remote-tracking branch 'upstream/master' into chore/update-v4-from-master-4

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Adriane Boyd 2022-11-03 09:42:36 +01:00
commit 68b8fa2df2
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70
.github/azure-steps.yml vendored
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@ -1,9 +1,7 @@
parameters:
python_version: ''
architecture: ''
prefix: ''
gpu: false
num_build_jobs: 1
architecture: 'x64'
num_build_jobs: 2
steps:
- task: UsePythonVersion@0
@ -17,16 +15,16 @@ steps:
displayName: 'Set variables'
- script: |
${{ parameters.prefix }} python -m pip install -U pip setuptools
${{ parameters.prefix }} python -m pip install -U -r requirements.txt
python -m pip install -U build pip setuptools
python -m pip install -U -r requirements.txt
displayName: "Install dependencies"
- script: |
${{ parameters.prefix }} python setup.py build_ext --inplace -j ${{ parameters.num_build_jobs }}
${{ parameters.prefix }} python setup.py sdist --formats=gztar
displayName: "Compile and build sdist"
python -m build --sdist
displayName: "Build sdist"
- script: python -m mypy spacy
- script: |
python -m mypy spacy
displayName: 'Run mypy'
condition: ne(variables['python_version'], '3.6')
@ -35,35 +33,24 @@ steps:
contents: "spacy"
displayName: "Delete source directory"
- task: DeleteFiles@1
inputs:
contents: "*.egg-info"
displayName: "Delete egg-info directory"
- script: |
${{ parameters.prefix }} python -m pip freeze --exclude torch --exclude cupy-cuda110 > installed.txt
${{ parameters.prefix }} python -m pip uninstall -y -r installed.txt
python -m pip freeze > installed.txt
python -m pip uninstall -y -r installed.txt
displayName: "Uninstall all packages"
- bash: |
${{ parameters.prefix }} SDIST=$(python -c "import os;print(os.listdir('./dist')[-1])" 2>&1)
${{ parameters.prefix }} SPACY_NUM_BUILD_JOBS=2 python -m pip install dist/$SDIST
SDIST=$(python -c "import os;print(os.listdir('./dist')[-1])" 2>&1)
SPACY_NUM_BUILD_JOBS=${{ parameters.num_build_jobs }} python -m pip install dist/$SDIST
displayName: "Install from sdist"
- script: |
${{ parameters.prefix }} python -m pip install -U -r requirements.txt
displayName: "Install test requirements"
- script: |
${{ parameters.prefix }} python -m pip install -U cupy-cuda110 -f https://github.com/cupy/cupy/releases/v9.0.0
${{ parameters.prefix }} python -m pip install "torch==1.7.1+cu110" -f https://download.pytorch.org/whl/torch_stable.html
displayName: "Install GPU requirements"
condition: eq(${{ parameters.gpu }}, true)
- script: |
${{ parameters.prefix }} python -m pytest --pyargs spacy -W error
displayName: "Run CPU tests"
condition: eq(${{ parameters.gpu }}, false)
- script: |
${{ parameters.prefix }} python -m pytest --pyargs spacy -W error -p spacy.tests.enable_gpu
displayName: "Run GPU tests"
condition: eq(${{ parameters.gpu }}, true)
python -W error -c "import spacy"
displayName: "Test import"
- script: |
python -m spacy download ca_core_news_sm
@ -106,13 +93,22 @@ steps:
displayName: 'Test assemble CLI vectors warning'
condition: eq(variables['python_version'], '3.8')
- script: |
python -m pip install -U -r requirements.txt
displayName: "Install test requirements"
- script: |
python -m pytest --pyargs spacy -W error
displayName: "Run CPU tests"
- script: |
python -m pip install --pre thinc-apple-ops
python -m pytest --pyargs spacy
displayName: "Run CPU tests with thinc-apple-ops"
condition: and(startsWith(variables['imageName'], 'macos'), eq(variables['python.version'], '3.11'))
- script: |
python .github/validate_universe_json.py website/meta/universe.json
displayName: 'Test website/meta/universe.json'
condition: eq(variables['python_version'], '3.8')
- script: |
${{ parameters.prefix }} python -m pip install --pre thinc-apple-ops
${{ parameters.prefix }} python -m pytest --pyargs spacy
displayName: "Run CPU tests with thinc-apple-ops"
condition: and(startsWith(variables['imageName'], 'macos'), eq(variables['python.version'], '3.10'))

9
.github/workflows/autoblack.yml vendored
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@ -12,10 +12,10 @@ jobs:
if: github.repository_owner == 'explosion'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v3
with:
ref: ${{ github.head_ref }}
- uses: actions/setup-python@v2
- uses: actions/setup-python@v4
- run: pip install black
- name: Auto-format code if needed
run: black spacy
@ -23,10 +23,11 @@ jobs:
# code and makes GitHub think the action failed
- name: Check for modified files
id: git-check
run: echo ::set-output name=modified::$(if git diff-index --quiet HEAD --; then echo "false"; else echo "true"; fi)
run: echo modified=$(if git diff-index --quiet HEAD --; then echo "false"; else echo "true"; fi) >> $GITHUB_OUTPUT
- name: Create Pull Request
if: steps.git-check.outputs.modified == 'true'
uses: peter-evans/create-pull-request@v3
uses: peter-evans/create-pull-request@v4
with:
title: Auto-format code with black
labels: meta

6
.github/workflows/explosionbot.yml vendored
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@ -8,14 +8,14 @@ on:
jobs:
explosion-bot:
runs-on: ubuntu-18.04
runs-on: ubuntu-latest
steps:
- name: Dump GitHub context
env:
GITHUB_CONTEXT: ${{ toJson(github) }}
run: echo "$GITHUB_CONTEXT"
- uses: actions/checkout@v1
- uses: actions/setup-python@v1
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
- name: Install and run explosion-bot
run: |
pip install git+https://${{ secrets.EXPLOSIONBOT_TOKEN }}@github.com/explosion/explosion-bot

6
.github/workflows/slowtests.yml vendored
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@ -14,7 +14,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v1
uses: actions/checkout@v3
with:
ref: ${{ matrix.branch }}
- name: Get commits from past 24 hours
@ -23,9 +23,9 @@ jobs:
today=$(date '+%Y-%m-%d %H:%M:%S')
yesterday=$(date -d "yesterday" '+%Y-%m-%d %H:%M:%S')
if git log --after="$yesterday" --before="$today" | grep commit ; then
echo "::set-output name=run_tests::true"
echo run_tests=true >> $GITHUB_OUTPUT
else
echo "::set-output name=run_tests::false"
echo run_tests=false >> $GITHUB_OUTPUT
fi
- name: Trigger buildkite build

4
.github/workflows/spacy_universe_alert.yml vendored
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@ -17,8 +17,8 @@ jobs:
run: |
echo "$GITHUB_CONTEXT"
- uses: actions/checkout@v1
- uses: actions/setup-python@v1
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
- name: Install Bernadette app dependency and send an alert
env:
SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}

6
README.md
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@ -8,7 +8,7 @@ be used in real products.
spaCy comes with
[pretrained pipelines](https://spacy.io/models) and
currently supports tokenization and training for **60+ languages**. It features
currently supports tokenization and training for **70+ languages**. It features
state-of-the-art speed and **neural network models** for tagging,
parsing, **named entity recognition**, **text classification** and more,
multi-task learning with pretrained **transformers** like BERT, as well as a
@ -16,7 +16,7 @@ production-ready [**training system**](https://spacy.io/usage/training) and easy
model packaging, deployment and workflow management. spaCy is commercial
open-source software, released under the MIT license.
💫 **Version 3.4.0 out now!**
💫 **Version 3.4 out now!**
[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
[![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
@ -79,7 +79,7 @@ more people can benefit from it.
## Features
- Support for **60+ languages**
- Support for **70+ languages**
- **Trained pipelines** for different languages and tasks
- Multi-task learning with pretrained **transformers** like BERT
- Support for pretrained **word vectors** and embeddings

35
azure-pipelines.yml
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@ -76,24 +76,24 @@ jobs:
# Python39Mac:
# imageName: "macos-latest"
# python.version: "3.9"
Python310Linux:
imageName: "ubuntu-latest"
python.version: "3.10"
# Python310Linux:
# imageName: "ubuntu-latest"
# python.version: "3.10"
Python310Windows:
imageName: "windows-latest"
python.version: "3.10"
Python310Mac:
imageName: "macos-latest"
python.version: "3.10"
# Python310Mac:
# imageName: "macos-latest"
# python.version: "3.10"
Python311Linux:
imageName: 'ubuntu-latest'
python.version: '3.11.0-rc.2'
python.version: '3.11.0'
Python311Windows:
imageName: 'windows-latest'
python.version: '3.11.0-rc.2'
python.version: '3.11.0'
Python311Mac:
imageName: 'macos-latest'
python.version: '3.11.0-rc.2'
python.version: '3.11.0'
maxParallel: 4
pool:
vmImage: $(imageName)
@ -101,20 +101,3 @@ jobs:
- template: .github/azure-steps.yml
parameters:
python_version: '$(python.version)'
architecture: 'x64'
# - job: "TestGPU"
# dependsOn: "Validate"
# strategy:
# matrix:
# Python38LinuxX64_GPU:
# python.version: '3.8'
# pool:
# name: "LinuxX64_GPU"
# steps:
# - template: .github/azure-steps.yml
# parameters:
# python_version: '$(python.version)'
# architecture: 'x64'
# gpu: true
# num_build_jobs: 24

7
spacy/ml/models/entity_linker.py
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@ -71,11 +71,10 @@ def span_maker_forward(model, docs: List[Doc], is_train) -> Tuple[Ragged, Callab
cands.append((start_token, end_token))
candidates.append(ops.asarray2i(cands))
candlens = ops.asarray1i([len(cands) for cands in candidates])
candidates = ops.xp.concatenate(candidates)
outputs = Ragged(candidates, candlens)
lengths = model.ops.asarray1i([len(cands) for cands in candidates])
out = Ragged(model.ops.flatten(candidates), lengths)
# because this is just rearranging docs, the backprop does nothing
return outputs, lambda x: []
return out, lambda x: []
@registry.misc("spacy.KBFromFile.v1")

11
spacy/pipeline/textcat.py
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@ -27,8 +27,8 @@ single_label_default_config = """
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = 64
rows = [2000, 2000, 1000, 1000, 1000, 1000]
attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
rows = [2000, 2000, 500, 1000, 500]
attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@ -75,7 +75,7 @@ subword_features = true
"textcat",
assigns=["doc.cats"],
default_config={
"threshold": 0.5,
"threshold": 0.0,
"model": DEFAULT_SINGLE_TEXTCAT_MODEL,
"scorer": {"@scorers": "spacy.textcat_scorer.v1"},
"save_activations": False,
@ -158,7 +158,8 @@ class TextCategorizer(TrainablePipe):
model (thinc.api.Model): The Thinc Model powering the pipeline component.
name (str): The component instance name, used to add entries to the
losses during training.
threshold (float): Cutoff to consider a prediction "positive".
threshold (float): Unused, not needed for single-label (exclusive
classes) classification.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_cats for the attribute "cats".
@ -168,7 +169,7 @@ class TextCategorizer(TrainablePipe):
self.model = model
self.name = name
self._rehearsal_model = None
cfg = {"labels": [], "threshold": threshold, "positive_label": None}
cfg: Dict[str, Any] = {"labels": [], "threshold": threshold, "positive_label": None}
self.cfg = dict(cfg)
self.scorer = scorer
self.save_activations = save_activations

4
spacy/pipeline/textcat_multilabel.py
View File

@ -24,8 +24,8 @@ multi_label_default_config = """
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = 64
rows = [2000, 2000, 1000, 1000, 1000, 1000]
attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
rows = [2000, 2000, 500, 1000, 500]
attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]

12
spacy/scorer.py
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@ -446,7 +446,7 @@ class Scorer:
labels (Iterable[str]): The set of possible labels. Defaults to [].
multi_label (bool): Whether the attribute allows multiple labels.
Defaults to True. When set to False (exclusive labels), missing
gold labels are interpreted as 0.0.
gold labels are interpreted as 0.0 and the threshold is set to 0.0.
positive_label (str): The positive label for a binary task with
exclusive classes. Defaults to None.
threshold (float): Cutoff to consider a prediction "positive". Defaults
@ -471,6 +471,8 @@ class Scorer:
"""
if threshold is None:
threshold = 0.5 if multi_label else 0.0
if not multi_label:
threshold = 0.0
f_per_type = {label: PRFScore() for label in labels}
auc_per_type = {label: ROCAUCScore() for label in labels}
labels = set(labels)
@ -505,20 +507,18 @@ class Scorer:
# Get the highest-scoring for each.
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
gold_label, gold_score = max(gold_cats.items(), key=lambda it: it[1])
if pred_label == gold_label and pred_score >= threshold:
if pred_label == gold_label:
f_per_type[pred_label].tp += 1
else:
f_per_type[gold_label].fn += 1
if pred_score >= threshold:
f_per_type[pred_label].fp += 1
f_per_type[pred_label].fp += 1
elif gold_cats:
gold_label, gold_score = max(gold_cats, key=lambda it: it[1])
if gold_score > 0:
f_per_type[gold_label].fn += 1
elif pred_cats:
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
if pred_score >= threshold:
f_per_type[pred_label].fp += 1
f_per_type[pred_label].fp += 1
micro_prf = PRFScore()
for label_prf in f_per_type.values():
micro_prf.tp += label_prf.tp

22
spacy/tests/pipeline/test_entity_linker.py
View File

@ -10,6 +10,7 @@ from spacy.compat import pickle
from spacy.kb import Candidate, InMemoryLookupKB, get_candidates, KnowledgeBase
from spacy.lang.en import English
from spacy.ml import load_kb
from spacy.ml.models.entity_linker import build_span_maker
from spacy.pipeline import EntityLinker, TrainablePipe
from spacy.pipeline.legacy import EntityLinker_v1
from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
@ -716,7 +717,11 @@ TRAIN_DATA = [
("Russ Cochran was a member of University of Kentucky's golf team.",
{"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}},
"entities": [(0, 12, "PERSON"), (43, 51, "LOC")],
"sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]})
"sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}),
# having a blank instance shouldn't break things
("The weather is nice today.",
{"links": {}, "entities": [],
"sent_starts": [1, -1, 0, 0, 0, 0]})
]
GOLD_entities = ["Q2146908", "Q7381115", "Q7381115", "Q2146908"]
# fmt: on
@ -1260,3 +1265,18 @@ def test_save_activations():
assert scores.data.shape == (2, 1)
assert scores.data.dtype == "float32"
assert scores.lengths.shape == (1,)
def test_span_maker_forward_with_empty():
"""The forward pass of the span maker may have a doc with no entities."""
nlp = English()
doc1 = nlp("a b c")
ent = doc1[0:1]
ent.label_ = "X"
doc1.ents = [ent]
# no entities
doc2 = nlp("x y z")
# just to get a model
span_maker = build_span_maker()
span_maker([doc1, doc2], False)

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

@ -824,10 +824,10 @@ def test_textcat_loss(multi_label: bool, expected_loss: float):
assert loss == expected_loss
def test_textcat_threshold():
def test_textcat_multilabel_threshold():
# Ensure the scorer can be called with a different threshold
nlp = English()
nlp.add_pipe("textcat")
nlp.add_pipe("textcat_multilabel")
train_examples = []
for text, annotations in TRAIN_DATA_SINGLE_LABEL:
@ -850,7 +850,7 @@ def test_textcat_threshold():
)
pos_f = scores["cats_score"]
assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 1.0
assert pos_f > macro_f
assert pos_f >= macro_f
def test_textcat_multi_threshold():

22
spacy/tests/pipeline/test_tok2vec.py
View File

@ -231,7 +231,7 @@ def test_tok2vec_listener_callback():
def test_tok2vec_listener_overfitting():
""" Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components """
"""Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
@ -264,7 +264,7 @@ def test_tok2vec_listener_overfitting():
def test_tok2vec_frozen_not_annotating():
""" Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating """
"""Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
@ -274,12 +274,16 @@ def test_tok2vec_frozen_not_annotating():
for i in range(2):
losses = {}
with pytest.raises(ValueError, match=r"the tok2vec embedding layer is not updated"):
nlp.update(train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"])
with pytest.raises(
ValueError, match=r"the tok2vec embedding layer is not updated"
):
nlp.update(
train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"]
)
def test_tok2vec_frozen_overfitting():
""" Test that a pipeline with a frozen & annotating tok2vec can still overfit """
"""Test that a pipeline with a frozen & annotating tok2vec can still overfit"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
@ -289,7 +293,13 @@ def test_tok2vec_frozen_overfitting():
for i in range(100):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"], annotates=["tok2vec"])
nlp.update(
train_examples,
sgd=optimizer,
losses=losses,
exclude=["tok2vec"],
annotates=["tok2vec"],
)
assert losses["tagger"] < 0.0001
# test the trained model

4
spacy/tests/test_models.py
View File

@ -23,7 +23,7 @@ def get_textcat_bow_kwargs():
def get_textcat_cnn_kwargs():
return {"tok2vec": test_tok2vec(), "exclusive_classes": False, "nO": 13}
return {"tok2vec": make_test_tok2vec(), "exclusive_classes": False, "nO": 13}
def get_all_params(model):
@ -65,7 +65,7 @@ def get_tok2vec_kwargs():
}
def test_tok2vec():
def make_test_tok2vec():
return build_Tok2Vec_model(**get_tok2vec_kwargs())

47
spacy/tests/test_scorer.py
View File

@ -474,3 +474,50 @@ def test_prf_score():
assert (a.precision, a.recall, a.fscore) == approx(
(c.precision, c.recall, c.fscore)
)
def test_score_cats(en_tokenizer):
text = "some text"
gold_doc = en_tokenizer(text)
gold_doc.cats = {"POSITIVE": 1.0, "NEGATIVE": 0.0}
pred_doc = en_tokenizer(text)
pred_doc.cats = {"POSITIVE": 0.75, "NEGATIVE": 0.25}
example = Example(pred_doc, gold_doc)
# threshold is ignored for multi_label=False
scores1 = Scorer.score_cats(
[example],
"cats",
labels=list(gold_doc.cats.keys()),
multi_label=False,
positive_label="POSITIVE",
threshold=0.1,
)
scores2 = Scorer.score_cats(
[example],
"cats",
labels=list(gold_doc.cats.keys()),
multi_label=False,
positive_label="POSITIVE",
threshold=0.9,
)
assert scores1["cats_score"] == 1.0
assert scores2["cats_score"] == 1.0
assert scores1 == scores2
# threshold is relevant for multi_label=True
scores = Scorer.score_cats(
[example],
"cats",
labels=list(gold_doc.cats.keys()),
multi_label=True,
threshold=0.9,
)
assert scores["cats_macro_f"] == 0.0
# threshold is relevant for multi_label=True
scores = Scorer.score_cats(
[example],
"cats",
labels=list(gold_doc.cats.keys()),
multi_label=True,
threshold=0.1,
)
assert scores["cats_macro_f"] == 0.5

21
website/docs/api/scorer.md
View File

@ -229,16 +229,17 @@ The reported `{attr}_score` depends on the classification properties:
> print(scores["cats_macro_auc"])
> ```
| Name | Description |
| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
| `attr` | The attribute to score. ~~str~~ |
| _keyword-only_ | |
| `getter` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. ~~Callable[[Doc, str], Dict[str, float]]~~ |
| labels | The set of possible labels. Defaults to `[]`. ~~Iterable[str]~~ |
| `multi_label` | Whether the attribute allows multiple labels. Defaults to `True`. ~~bool~~ |
| `positive_label` | The positive label for a binary task with exclusive classes. Defaults to `None`. ~~Optional[str]~~ |
| **RETURNS** | A dictionary containing the scores, with inapplicable scores as `None`. ~~Dict[str, Optional[float]]~~ |
| Name | Description |
| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
| `attr` | The attribute to score. ~~str~~ |
| _keyword-only_ | |
| `getter` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. ~~Callable[[Doc, str], Dict[str, float]]~~ |
| labels | The set of possible labels. Defaults to `[]`. ~~Iterable[str]~~ |
| `multi_label` | Whether the attribute allows multiple labels. Defaults to `True`. When set to `False` (exclusive labels), missing gold labels are interpreted as `0.0` and the threshold is set to `0.0`. ~~bool~~ |
| `positive_label` | The positive label for a binary task with exclusive classes. Defaults to `None`. ~~Optional[str]~~ |
| `threshold` | Cutoff to consider a prediction "positive". Defaults to `0.5` for multi-label, and `0.0` (i.e. whatever's highest scoring) otherwise. ~~float~~ |
| **RETURNS** | A dictionary containing the scores, with inapplicable scores as `None`. ~~Dict[str, Optional[float]]~~ |
## Scorer.score_links {#score_links tag="staticmethod" new="3"}

5
website/docs/api/textcategorizer.md
View File

@ -63,7 +63,6 @@ architectures and their arguments and hyperparameters.
> ```python
> from spacy.pipeline.textcat import DEFAULT_SINGLE_TEXTCAT_MODEL
> config = {
> "threshold": 0.5,
> "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
> }
> nlp.add_pipe("textcat", config=config)
@ -82,7 +81,7 @@ architectures and their arguments and hyperparameters.
| Setting | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~ |
| `threshold` | Cutoff to consider a prediction "positive", relevant for `textcat_multilabel` when calculating accuracy scores. ~~float~~ |
| `model` | A model instance that predicts scores for each category. Defaults to [TextCatEnsemble](/api/architectures#TextCatEnsemble). ~~Model[List[Doc], List[Floats2d]]~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_cats`](/api/scorer#score_cats) for the attribute `"cats"`. ~~Optional[Callable]~~ |
@ -123,7 +122,7 @@ shortcut for this and instantiate the component using its string name and
| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | |
| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~ |
| `threshold` | Cutoff to consider a prediction "positive", relevant for `textcat_multilabel` when calculating accuracy scores. ~~float~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_cats`](/api/scorer#score_cats) for the attribute `"cats"`. ~~Optional[Callable]~~ |
| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. The supported activations is `"probabilities"`. ~~Union[bool, list[str]]~~ |

2
website/docs/usage/rule-based-matching.md
View File

@ -1791,7 +1791,7 @@ the entity `Span` – for example `._.orgs` or `._.prev_orgs` and
> [`Doc.retokenize`](/api/doc#retokenize) context manager:
>
> ```python
> with doc.retokenize() as retokenize:
> with doc.retokenize() as retokenizer:
> for ent in doc.ents:
> retokenizer.merge(ent)
> ```

28
website/meta/languages.json
View File

@ -4,12 +4,22 @@
"code": "af",
"name": "Afrikaans"
},
{
"code": "am",
"name": "Amharic",
"has_examples": true
},
{
"code": "ar",
"name": "Arabic",
"example": "هذه جملة",
"has_examples": true
},
{
"code": "az",
"name": "Azerbaijani",
"has_examples": true
},
{
"code": "bg",
"name": "Bulgarian",
@ -65,7 +75,7 @@
{
"code": "dsb",
"name": "Lower Sorbian",
"has_examples": true
"has_examples": true
},
{
"code": "el",
@ -142,6 +152,11 @@
"code": "ga",
"name": "Irish"
},
{
"code": "grc",
"name": "Ancient Greek",
"has_examples": true
},
{
"code": "gu",
"name": "Gujarati",
@ -172,7 +187,7 @@
{
"code": "hsb",
"name": "Upper Sorbian",
"has_examples": true
"has_examples": true
},
{
"code": "hu",
@ -260,6 +275,10 @@
"example": "Адамга эң кыйыны — күн сайын адам болуу",
"has_examples": true
},
{
"code": "la",
"name": "Latin"
},
{
"code": "lb",
"name": "Luxembourgish",
@ -448,6 +467,11 @@
"example": "นี่คือประโยค",
"has_examples": true
},
{
"code": "ti",
"name": "Tigrinya",
"has_examples": true
},
{
"code": "tl",
"name": "Tagalog"

3
website/src/styles/quickstart.module.sass
View File

@ -149,6 +149,9 @@
& > span
display: block
a
text-decoration: underline
.small
font-size: var(--font-size-code)
line-height: 1.65

11
website/src/widgets/quickstart-install.js
View File

@ -159,6 +159,9 @@ const QuickstartInstall = ({ id, title }) => {
setters={setters}
showDropdown={showDropdown}
>
<QS os="mac" hardware="gpu" platform="arm">
# Note M1 GPU support is experimental, see <a href="https://github.com/explosion/thinc/issues/792">Thinc issue #792</a>
</QS>
<QS package="pip" config="venv">
python -m venv .env
</QS>
@ -198,7 +201,13 @@ const QuickstartInstall = ({ id, title }) => {
{nightly ? ' --pre' : ''}
</QS>
<QS package="conda">conda install -c conda-forge spacy</QS>
<QS package="conda" hardware="gpu">
<QS package="conda" hardware="gpu" os="windows">
conda install -c conda-forge cupy
</QS>
<QS package="conda" hardware="gpu" os="linux">
conda install -c conda-forge cupy
</QS>
<QS package="conda" hardware="gpu" os="mac" platform="x86">
conda install -c conda-forge cupy
</QS>
<QS package="conda" config="train">