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Merge remote-tracking branch 'upstream/v4' into feature/pipe-as-tuples

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Adriane Boyd 2023-01-31 09:56:48 +01:00
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2
.github/ISSUE_TEMPLATE/01_bugs.md vendored
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@ -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:

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.github/azure-steps.yml vendored
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@ -1,74 +1,68 @@
parameters:
python_version: ''
architecture: ''
prefix: ''
gpu: false
num_build_jobs: 1
architecture: 'x64'
num_build_jobs: 2
steps:
- task: UsePythonVersion@0
inputs:
versionSpec: ${{ parameters.python_version }}
architecture: ${{ parameters.architecture }}
allowUnstable: true
- bash: |
echo "##vso[task.setvariable variable=python_version]${{ parameters.python_version }}"
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')
- task: DeleteFiles@1
inputs:
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"
python -W error -c "import spacy"
displayName: "Test import"
- 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: |
${{ parameters.prefix }} python -m pytest --pyargs spacy
displayName: "Run CPU tests"
condition: eq(${{ parameters.gpu }}, false)
- script: |
${{ parameters.prefix }} python -m pytest --pyargs spacy -p spacy.tests.enable_gpu
displayName: "Run GPU tests"
condition: eq(${{ 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 -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')"
# displayName: 'Test no warnings on load (#11713)'
# condition: eq(variables['python_version'], '3.8')
- script: |
python -m spacy convert extra/example_data/ner_example_data/ner-token-per-line-conll2003.json .
@ -92,25 +86,34 @@ 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_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_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')
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 'spacy[apple]'
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'))

13
.github/no-response.yml vendored
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@ -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.

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

8
.github/workflows/issue-manager.yml vendored
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@ -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
}
}

2
.github/workflows/lock.yml vendored
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@ -15,7 +15,7 @@ jobs:
action:
runs-on: ubuntu-latest
steps:
- uses: dessant/lock-threads@v3
- uses: dessant/lock-threads@v4
with:
process-only: 'issues'
issue-inactive-days: '30'

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

6
.github/workflows/spacy_universe_alert.yml vendored
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@ -17,8 +17,10 @@ jobs:
run: |
echo "$GITHUB_CONTEXT"
- uses: actions/checkout@v1
- uses: actions/setup-python@v1
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install Bernadette app dependency and send an alert
env:
SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}

11
.gitignore vendored
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@ -10,20 +10,11 @@ spacy/tests/package/setup.cfg
spacy/tests/package/pyproject.toml
spacy/tests/package/requirements.txt
# Website
website/.cache/
website/public/
website/node_modules
website/.npm
website/logs
*.log
npm-debug.log*
quickstart-training-generator.js
# Cython / C extensions
cythonize.json
spacy/*.html
*.cpp
*.c
*.so
# Vim / VSCode / editors

6
.pre-commit-config.yaml
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@ -3,10 +3,10 @@ repos:
rev: 22.3.0
hooks:
- id: black
language_version: python3.7
language_version: python3.8
additional_dependencies: ['click==8.0.4']
- repo: https://gitlab.com/pycqa/flake8
rev: 3.9.2
- repo: https://github.com/pycqa/flake8
rev: 5.0.4
hooks:
- id: flake8
args:

2
CONTRIBUTING.md
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@ -271,7 +271,7 @@ except: # noqa: E722
### Python conventions
All Python code must be written **compatible with Python 3.6+**. More detailed
All Python code must be written **compatible with Python 3.8+**. 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

2
Makefile
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@ -5,7 +5,7 @@ override SPACY_EXTRAS = spacy-lookups-data==1.0.2 jieba spacy-pkuseg==0.0.28 sud
endif
ifndef PYVER
override PYVER = 3.6
override PYVER = 3.8
endif
VENV := ./env$(PYVER)

12
README.md
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@ -8,15 +8,15 @@ 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
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.
open-source software, released under the [MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
💫 **Version 3.4.0 out now!**
💫 **Version 3.5 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)
@ -46,6 +46,7 @@ open-source software, released under the MIT license.
| 🛠 **[Changelog]** | Changes and version history. |
| 💝 **[Contribute]** | How to contribute to the spaCy project and code base. |
| <a href="https://explosion.ai/spacy-tailored-pipelines"><img src="https://user-images.githubusercontent.com/13643239/152853098-1c761611-ccb0-4ec6-9066-b234552831fe.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-pipelines)** |
| <a href="https://explosion.ai/spacy-tailored-analysis"><img src="https://user-images.githubusercontent.com/1019791/206151300-b00cd189-e503-4797-aa1e-1bb6344062c5.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-analysis)** |
[spacy 101]: https://spacy.io/usage/spacy-101
[new in v3.0]: https://spacy.io/usage/v3
@ -59,6 +60,7 @@ open-source software, released under the MIT license.
[changelog]: https://spacy.io/usage#changelog
[contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md
## 💬 Where to ask questions
The spaCy project is maintained by the [spaCy team](https://explosion.ai/about).
@ -79,7 +81,7 @@ 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
@ -103,7 +105,7 @@ For detailed installation instructions, see the
- **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
Studio)
- **Python version**: Python 3.6+ (only 64 bit)
- **Python version**: Python 3.8+ (only 64 bit)
- **Package managers**: [pip] · [conda] (via `conda-forge`)
[pip]: https://pypi.org/project/spacy/

80
azure-pipelines.yml
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@ -11,28 +11,42 @@ trigger:
exclude:
- "website/*"
- "*.md"
- "*.mdx"
- ".github/workflows/*"
pr:
paths:
exclude:
- "*.md"
- "*.mdx"
- "website/docs/*"
- "website/src/*"
- "website/meta/*.tsx"
- "website/meta/*.mjs"
- "website/meta/languages.json"
- "website/meta/site.json"
- "website/meta/sidebars.json"
- "website/meta/type-annotations.json"
- "website/pages/*"
- ".github/workflows/*"
jobs:
# Perform basic checks for most important errors (syntax etc.) Uses the config
# defined in .flake8 and overwrites the selected codes.
# Check formatting and linting. Perform basic checks for most important errors
# (syntax etc.) Uses the config defined in setup.cfg and overwrites the
# selected codes.
- job: "Validate"
pool:
vmImage: "ubuntu-latest"
steps:
- task: UsePythonVersion@0
inputs:
versionSpec: "3.7"
versionSpec: "3.8"
- script: |
pip install flake8==3.9.2
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823 --show-source --statistics
pip install black==22.3.0
python -m black spacy --check
displayName: "black"
- script: |
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"
@ -40,24 +54,6 @@ jobs:
strategy:
matrix:
# We're only running one platform per Python version to speed up builds
Python36Linux:
imageName: "ubuntu-latest"
python.version: "3.6"
# Python36Windows:
# imageName: "windows-latest"
# python.version: "3.6"
# Python36Mac:
# imageName: "macos-latest"
# python.version: "3.6"
# Python37Linux:
# imageName: "ubuntu-latest"
# python.version: "3.7"
Python37Windows:
imageName: "windows-latest"
python.version: "3.7"
# Python37Mac:
# imageName: "macos-latest"
# python.version: "3.7"
# Python38Linux:
# imageName: "ubuntu-latest"
# python.version: "3.8"
@ -76,15 +72,24 @@ 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'
Python311Windows:
imageName: 'windows-latest'
python.version: '3.11'
Python311Mac:
imageName: 'macos-latest'
python.version: '3.11'
maxParallel: 4
pool:
vmImage: $(imageName)
@ -92,20 +97,3 @@ jobs:
- template: .github/azure-steps.yml
parameters:
python_version: '$(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

3
build-constraints.txt
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@ -5,4 +5,5 @@ numpy==1.17.3; python_version=='3.8' and platform_machine!='aarch64'
numpy==1.19.2; python_version=='3.8' and platform_machine=='aarch64'
numpy==1.19.3; python_version=='3.9'
numpy==1.21.3; python_version=='3.10'
numpy; python_version>='3.11'
numpy==1.23.2; python_version=='3.11'
numpy; python_version>='3.12'

23
extra/DEVELOPER_DOCS/Code Conventions.md
View File

@ -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.

82
extra/DEVELOPER_DOCS/Satellite Packages.md Normal file
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@ -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&#39;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&#39;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&#39;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&#39;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&#39;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&#39;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&#39;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&#39;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&#39;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.

31
licenses/3rd_party_licenses.txt
View File

@ -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.

3
pyproject.toml
View File

@ -5,8 +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,<8.2.0",
"pathy",
"thinc>=9.0.0.dev2,<9.1.0",
"numpy>=1.15.0",
]
build-backend = "setuptools.build_meta"

21
requirements.txt
View File

@ -1,37 +1,38 @@
# Our libraries
spacy-legacy>=3.0.9,<3.1.0
spacy-legacy>=4.0.0.dev0,<4.1.0
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,<8.2.0
thinc>=9.0.0.dev2,<9.1.0
ml_datasets>=0.2.0,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.9.1,<1.1.0
wasabi>=0.9.1,<1.2.0
srsly>=2.4.3,<3.0.0
catalogue>=2.0.6,<2.1.0
typer>=0.3.0,<0.5.0
pathy>=0.3.5
typer>=0.3.0,<0.8.0
pathy>=0.10.0
smart-open>=5.2.1,<7.0.0
# Third party dependencies
numpy>=1.15.0
requests>=2.13.0,<3.0.0
tqdm>=4.38.0,<5.0.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.10.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
jinja2
langcodes>=3.2.0,<4.0.0
# Official Python utilities
setuptools
packaging>=20.0
typing_extensions>=3.7.4.1,<4.2.0; python_version < "3.8"
# Development dependencies
pre-commit>=2.13.0
cython>=0.25,<3.0
pytest>=5.2.0,!=7.1.0
pytest-timeout>=1.3.0,<2.0.0
mock>=2.0.0,<3.0.0
flake8>=3.8.0,<3.10.0
flake8>=3.8.0,<6.0.0
hypothesis>=3.27.0,<7.0.0
mypy>=0.910,<0.970; platform_machine!='aarch64'
types-dataclasses>=0.1.3; python_version < "3.7"
mypy>=0.990,<0.1000; platform_machine != "aarch64"
types-mock>=0.1.1
types-setuptools>=57.0.0
types-requests
types-setuptools>=57.0.0
black>=22.0,<23.0

67
setup.cfg
View File

@ -17,11 +17,10 @@ classifiers =
Operating System :: Microsoft :: Windows
Programming Language :: Cython
Programming Language :: Python :: 3
Programming Language :: Python :: 3.6
Programming Language :: Python :: 3.7
Programming Language :: Python :: 3.8
Programming Language :: Python :: 3.9
Programming Language :: Python :: 3.10
Programming Language :: Python :: 3.11
Topic :: Scientific/Engineering
project_urls =
Release notes = https://github.com/explosion/spaCy/releases
@ -30,38 +29,30 @@ project_urls =
[options]
zip_safe = false
include_package_data = true
python_requires = >=3.6
setup_requires =
cython>=0.25,<3.0
numpy>=1.15.0
# We also need our Cython packages here to compile against
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,<8.2.0
python_requires = >=3.8
install_requires =
# Our libraries
spacy-legacy>=3.0.9,<3.1.0
spacy-legacy>=4.0.0.dev0,<4.1.0
spacy-loggers>=1.0.0,<2.0.0
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,<8.2.0
wasabi>=0.9.1,<1.1.0
thinc>=9.0.0.dev2,<9.1.0
wasabi>=0.9.1,<1.2.0
srsly>=2.4.3,<3.0.0
catalogue>=2.0.6,<2.1.0
typer>=0.3.0,<0.5.0
pathy>=0.3.5
# Third-party dependencies
typer>=0.3.0,<0.8.0
pathy>=0.10.0
smart-open>=5.2.1,<7.0.0
tqdm>=4.38.0,<5.0.0
numpy>=1.15.0
requests>=2.13.0,<3.0.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.10.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
jinja2
# Official Python utilities
setuptools
packaging>=20.0
typing_extensions>=3.7.4,<4.2.0; python_version < "3.8"
langcodes>=3.2.0,<4.0.0
[options.entry_points]
@ -72,41 +63,45 @@ console_scripts =
lookups =
spacy_lookups_data>=1.0.3,<1.1.0
transformers =
spacy_transformers>=1.1.2,<1.2.0
spacy_transformers>=1.1.2,<1.3.0
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 +109,7 @@ ja =
sudachipy>=0.5.2,!=0.6.1
sudachidict_core>=20211220
ko =
natto-py>=0.9.0
mecab-ko>=1.0.0
th =
pythainlp>=2.0

26
setup.py
View File

@ -30,14 +30,13 @@ MOD_NAMES = [
"spacy.lexeme",
"spacy.vocab",
"spacy.attrs",
"spacy.kb",
"spacy.ml.parser_model",
"spacy.kb.candidate",
"spacy.kb.kb",
"spacy.kb.kb_in_memory",
"spacy.ml.tb_framework",
"spacy.morphology",
"spacy.pipeline.dep_parser",
"spacy.pipeline._edit_tree_internals.edit_trees",
"spacy.pipeline.morphologizer",
"spacy.pipeline.multitask",
"spacy.pipeline.ner",
"spacy.pipeline.pipe",
"spacy.pipeline.trainable_pipe",
"spacy.pipeline.sentencizer",
@ -45,12 +44,15 @@ MOD_NAMES = [
"spacy.pipeline.tagger",
"spacy.pipeline.transition_parser",
"spacy.pipeline._parser_internals.arc_eager",
"spacy.pipeline._parser_internals.batch",
"spacy.pipeline._parser_internals.ner",
"spacy.pipeline._parser_internals.nonproj",
"spacy.pipeline._parser_internals.search",
"spacy.pipeline._parser_internals._state",
"spacy.pipeline._parser_internals.stateclass",
"spacy.pipeline._parser_internals.transition_system",
"spacy.pipeline._parser_internals._beam_utils",
"spacy.pipeline._parser_internals._parser_utils",
"spacy.tokenizer",
"spacy.training.align",
"spacy.training.gold_io",
@ -60,12 +62,13 @@ MOD_NAMES = [
"spacy.tokens.span_group",
"spacy.tokens.graph",
"spacy.tokens.morphanalysis",
"spacy.tokens._retokenize",
"spacy.tokens.retokenizer",
"spacy.matcher.matcher",
"spacy.matcher.phrasematcher",
"spacy.matcher.dependencymatcher",
"spacy.symbols",
"spacy.vectors",
"spacy.tests.parser._search",
]
COMPILE_OPTIONS = {
"msvc": ["/Ox", "/EHsc"],
@ -205,6 +208,17 @@ 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(

12
spacy/__init__.py
View File

@ -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.

2
spacy/about.py
View File

@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy"
__version__ = "3.4.1"
__version__ = "4.0.0.dev0"
__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"

129
spacy/attrs.pxd
View File

@ -1,98 +1,49 @@
# Reserve 64 values for flag features
from . cimport symbols
cdef enum attr_id_t:
NULL_ATTR
IS_ALPHA
IS_ASCII
IS_DIGIT
IS_LOWER
IS_PUNCT
IS_SPACE
IS_TITLE
IS_UPPER
LIKE_URL
LIKE_NUM
LIKE_EMAIL
IS_STOP
IS_OOV_DEPRECATED
IS_BRACKET
IS_QUOTE
IS_LEFT_PUNCT
IS_RIGHT_PUNCT
IS_CURRENCY
NULL_ATTR = 0
IS_ALPHA = symbols.IS_ALPHA
IS_ASCII = symbols.IS_ASCII
IS_DIGIT = symbols.IS_DIGIT
IS_LOWER = symbols.IS_LOWER
IS_PUNCT = symbols.IS_PUNCT
IS_SPACE = symbols.IS_SPACE
IS_TITLE = symbols.IS_TITLE
IS_UPPER = symbols.IS_UPPER
LIKE_URL = symbols.LIKE_URL
LIKE_NUM = symbols.LIKE_NUM
LIKE_EMAIL = symbols.LIKE_EMAIL
IS_STOP = symbols.IS_STOP
IS_BRACKET = symbols.IS_BRACKET
IS_QUOTE = symbols.IS_QUOTE
IS_LEFT_PUNCT = symbols.IS_LEFT_PUNCT
IS_RIGHT_PUNCT = symbols.IS_RIGHT_PUNCT
IS_CURRENCY = symbols.IS_CURRENCY
FLAG19 = 19
FLAG20
FLAG21
FLAG22
FLAG23
FLAG24
FLAG25
FLAG26
FLAG27
FLAG28
FLAG29
FLAG30
FLAG31
FLAG32
FLAG33
FLAG34
FLAG35
FLAG36
FLAG37
FLAG38
FLAG39
FLAG40
FLAG41
FLAG42
FLAG43
FLAG44
FLAG45
FLAG46
FLAG47
FLAG48
FLAG49
FLAG50
FLAG51
FLAG52
FLAG53
FLAG54
FLAG55
FLAG56
FLAG57
FLAG58
FLAG59
FLAG60
FLAG61
FLAG62
FLAG63
ID = symbols.ID
ORTH = symbols.ORTH
LOWER = symbols.LOWER
NORM = symbols.NORM
SHAPE = symbols.SHAPE
PREFIX = symbols.PREFIX
SUFFIX = symbols.SUFFIX
ID
ORTH
LOWER
NORM
SHAPE
PREFIX
SUFFIX
LENGTH = symbols.LENGTH
CLUSTER = symbols.CLUSTER
LEMMA = symbols.LEMMA
POS = symbols.POS
TAG = symbols.TAG
DEP = symbols.DEP
ENT_IOB = symbols.ENT_IOB
ENT_TYPE = symbols.ENT_TYPE
HEAD = symbols.HEAD
SENT_START = symbols.SENT_START
SPACY = symbols.SPACY
PROB = symbols.PROB
LENGTH
CLUSTER
LEMMA
POS
TAG
DEP
ENT_IOB
ENT_TYPE
HEAD
SENT_START
SPACY
PROB
LANG
LANG = symbols.LANG
ENT_KB_ID = symbols.ENT_KB_ID
MORPH
MORPH = symbols.MORPH
ENT_ID = symbols.ENT_ID
IDX
SENT_END
IDX = symbols.IDX

120
spacy/attrs.pyx
View File

@ -16,57 +16,11 @@ IDS = {
"LIKE_NUM": LIKE_NUM,
"LIKE_EMAIL": LIKE_EMAIL,
"IS_STOP": IS_STOP,
"IS_OOV_DEPRECATED": IS_OOV_DEPRECATED,
"IS_BRACKET": IS_BRACKET,
"IS_QUOTE": IS_QUOTE,
"IS_LEFT_PUNCT": IS_LEFT_PUNCT,
"IS_RIGHT_PUNCT": IS_RIGHT_PUNCT,
"IS_CURRENCY": IS_CURRENCY,
"FLAG19": FLAG19,
"FLAG20": FLAG20,
"FLAG21": FLAG21,
"FLAG22": FLAG22,
"FLAG23": FLAG23,
"FLAG24": FLAG24,
"FLAG25": FLAG25,
"FLAG26": FLAG26,
"FLAG27": FLAG27,
"FLAG28": FLAG28,
"FLAG29": FLAG29,
"FLAG30": FLAG30,
"FLAG31": FLAG31,
"FLAG32": FLAG32,
"FLAG33": FLAG33,
"FLAG34": FLAG34,
"FLAG35": FLAG35,
"FLAG36": FLAG36,
"FLAG37": FLAG37,
"FLAG38": FLAG38,
"FLAG39": FLAG39,
"FLAG40": FLAG40,
"FLAG41": FLAG41,
"FLAG42": FLAG42,
"FLAG43": FLAG43,
"FLAG44": FLAG44,
"FLAG45": FLAG45,
"FLAG46": FLAG46,
"FLAG47": FLAG47,
"FLAG48": FLAG48,
"FLAG49": FLAG49,
"FLAG50": FLAG50,
"FLAG51": FLAG51,
"FLAG52": FLAG52,
"FLAG53": FLAG53,
"FLAG54": FLAG54,
"FLAG55": FLAG55,
"FLAG56": FLAG56,
"FLAG57": FLAG57,
"FLAG58": FLAG58,
"FLAG59": FLAG59,
"FLAG60": FLAG60,
"FLAG61": FLAG61,
"FLAG62": FLAG62,
"FLAG63": FLAG63,
"ID": ID,
"ORTH": ORTH,
"LOWER": LOWER,
@ -92,12 +46,11 @@ IDS = {
}
# ATTR IDs, in order of the symbol
NAMES = [key for key, value in sorted(IDS.items(), key=lambda item: item[1])]
NAMES = {v: k for k, v in IDS.items()}
locals().update(IDS)
def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):
def intify_attrs(stringy_attrs, strings_map=None):
"""
Normalize a dictionary of attributes, converting them to ints.
@ -109,75 +62,6 @@ def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):
converted to ints.
"""
inty_attrs = {}
if _do_deprecated:
if "F" in stringy_attrs:
stringy_attrs["ORTH"] = stringy_attrs.pop("F")
if "L" in stringy_attrs:
stringy_attrs["LEMMA"] = stringy_attrs.pop("L")
if "pos" in stringy_attrs:
stringy_attrs["TAG"] = stringy_attrs.pop("pos")
if "morph" in stringy_attrs:
morphs = stringy_attrs.pop("morph")
if "number" in stringy_attrs:
stringy_attrs.pop("number")
if "tenspect" in stringy_attrs:
stringy_attrs.pop("tenspect")
morph_keys = [
"PunctType",
"PunctSide",
"Other",
"Degree",
"AdvType",
"Number",
"VerbForm",
"PronType",
"Aspect",
"Tense",
"PartType",
"Poss",
"Hyph",
"ConjType",
"NumType",
"Foreign",
"VerbType",
"NounType",
"Gender",
"Mood",
"Negative",
"Tense",
"Voice",
"Abbr",
"Derivation",
"Echo",
"Foreign",
"NameType",
"NounType",
"NumForm",
"NumValue",
"PartType",
"Polite",
"StyleVariant",
"PronType",
"AdjType",
"Person",
"Variant",
"AdpType",
"Reflex",
"Negative",
"Mood",
"Aspect",
"Case",
"Polarity",
"PrepCase",
"Animacy", # U20
]
for key in morph_keys:
if key in stringy_attrs:
stringy_attrs.pop(key)
elif key.lower() in stringy_attrs:
stringy_attrs.pop(key.lower())
elif key.upper() in stringy_attrs:
stringy_attrs.pop(key.upper())
for name, value in stringy_attrs.items():
int_key = intify_attr(name)
if int_key is not None:

3
spacy/cli/__init__.py
View File

@ -4,6 +4,7 @@ from ._util import app, setup_cli # noqa: F401
# These are the actual functions, NOT the wrapped CLI commands. The CLI commands
# are registered automatically and won't have to be imported here.
from .benchmark_speed import benchmark_speed_cli # noqa: F401
from .download import download # noqa: F401
from .info import info # noqa: F401
from .package import package # noqa: F401
@ -16,6 +17,7 @@ from .debug_config import debug_config # noqa: F401
from .debug_model import debug_model # noqa: F401
from .debug_diff import debug_diff # noqa: F401
from .evaluate import evaluate # noqa: F401
from .apply import apply # noqa: F401
from .convert import convert # noqa: F401
from .init_pipeline import init_pipeline_cli # noqa: F401
from .init_config import init_config, fill_config # noqa: F401
@ -27,6 +29,7 @@ from .project.dvc import project_update_dvc # noqa: F401
from .project.push import project_push # noqa: F401
from .project.pull import project_pull # noqa: F401
from .project.document import project_document # noqa: F401
from .find_threshold import find_threshold # noqa: F401
@app.command("link", no_args_is_help=True, deprecated=True, hidden=True)

67
spacy/cli/_util.py
View File

@ -1,4 +1,4 @@
from typing import Dict, Any, Union, List, Optional, Tuple, Iterable
from typing import Dict, Any, Union, List, Optional, Tuple, Iterable, Literal
from typing import TYPE_CHECKING, overload
import sys
import shutil
@ -16,14 +16,13 @@ from thinc.util import gpu_is_available
from configparser import InterpolationError
import os
from ..compat import Literal
from ..schemas import ProjectConfigSchema, validate
from ..util import import_file, run_command, make_tempdir, registry, logger
from ..util import is_compatible_version, SimpleFrozenDict, ENV_VARS
from .. import about
if TYPE_CHECKING:
from pathy import Pathy # noqa: F401
from pathy import FluidPath # noqa: F401
SDIST_SUFFIX = ".tar.gz"
@ -46,6 +45,7 @@ DEBUG_HELP = """Suite of helpful commands for debugging and profiling. Includes
commands to check and validate your config files, training and evaluation data,
and custom model implementations.
"""
BENCHMARK_HELP = """Commands for benchmarking pipelines."""
INIT_HELP = """Commands for initializing configs and pipeline packages."""
# Wrappers for Typer's annotations. Initially created to set defaults and to
@ -54,12 +54,14 @@ Arg = typer.Argument
Opt = typer.Option
app = typer.Typer(name=NAME, help=HELP)
benchmark_cli = typer.Typer(name="benchmark", help=BENCHMARK_HELP, no_args_is_help=True)
project_cli = typer.Typer(name="project", help=PROJECT_HELP, no_args_is_help=True)
debug_cli = typer.Typer(name="debug", help=DEBUG_HELP, no_args_is_help=True)
init_cli = typer.Typer(name="init", help=INIT_HELP, no_args_is_help=True)
app.add_typer(project_cli)
app.add_typer(debug_cli)
app.add_typer(benchmark_cli)
app.add_typer(init_cli)
@ -158,15 +160,15 @@ def load_project_config(
sys.exit(1)
validate_project_version(config)
validate_project_commands(config)
if interpolate:
err = f"{PROJECT_FILE} validation error"
with show_validation_error(title=err, hint_fill=False):
config = substitute_project_variables(config, overrides)
# Make sure directories defined in config exist
for subdir in config.get("directories", []):
dir_path = path / subdir
if not dir_path.exists():
dir_path.mkdir(parents=True)
if interpolate:
err = f"{PROJECT_FILE} validation error"
with show_validation_error(title=err, hint_fill=False):
config = substitute_project_variables(config, overrides)
return config
@ -331,7 +333,7 @@ def import_code(code_path: Optional[Union[Path, str]]) -> None:
msg.fail(f"Couldn't load Python code: {code_path}", e, exits=1)
def upload_file(src: Path, dest: Union[str, "Pathy"]) -> None:
def upload_file(src: Path, dest: Union[str, "FluidPath"]) -> None:
"""Upload a file.
src (Path): The source path.
@ -339,13 +341,20 @@ def upload_file(src: Path, dest: Union[str, "Pathy"]) -> None:
"""
import smart_open
# Create parent directories for local paths
if isinstance(dest, Path):
if not dest.parent.exists():
dest.parent.mkdir(parents=True)
dest = str(dest)
with smart_open.open(dest, mode="wb") as output_file:
with src.open(mode="rb") as input_file:
output_file.write(input_file.read())
def download_file(src: Union[str, "Pathy"], dest: Path, *, force: bool = False) -> None:
def download_file(
src: Union[str, "FluidPath"], dest: Path, *, force: bool = False
) -> None:
"""Download a file using smart_open.
url (str): The URL of the file.
@ -358,7 +367,7 @@ def download_file(src: Union[str, "Pathy"], dest: Path, *, force: bool = False)
if dest.exists() and not force:
return None
src = str(src)
with smart_open.open(src, mode="rb", ignore_ext=True) as input_file:
with smart_open.open(src, mode="rb", compression="disable") as input_file:
with dest.open(mode="wb") as output_file:
shutil.copyfileobj(input_file, output_file)
@ -368,7 +377,7 @@ def ensure_pathy(path):
slow and annoying Google Cloud warning)."""
from pathy import Pathy # noqa: F811
return Pathy(path)
return Pathy.fluid(path)
def git_checkout(
@ -573,3 +582,39 @@ def setup_gpu(use_gpu: int, silent=None) -> None:
local_msg.info("Using CPU")
if gpu_is_available():
local_msg.info("To switch to GPU 0, use the option: --gpu-id 0")
def walk_directory(path: Path, suffix: Optional[str] = None) -> List[Path]:
"""Given a directory and a suffix, recursively find all files matching the suffix.
Directories or files with names beginning with a . are ignored, but hidden flags on
filesystems are not checked.
When provided with a suffix `None`, there is no suffix-based filtering."""
if not path.is_dir():
return [path]
paths = [path]
locs = []
seen = set()
for path in paths:
if str(path) in seen:
continue
seen.add(str(path))
if path.parts[-1].startswith("."):
continue
elif path.is_dir():
paths.extend(path.iterdir())
elif suffix is not None and not path.parts[-1].endswith(suffix):
continue
else:
locs.append(path)
# It's good to sort these, in case the ordering messes up cache.
locs.sort()
return locs
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)

143
spacy/cli/apply.py Normal file
View File

@ -0,0 +1,143 @@
import tqdm
import srsly
from itertools import chain
from pathlib import Path
from typing import Optional, List, Iterable, cast, Union
from wasabi import msg
from ._util import app, Arg, Opt, setup_gpu, import_code, walk_directory
from ..tokens import Doc, DocBin
from ..vocab import Vocab
from ..util import ensure_path, load_model
path_help = """Location of the documents to predict on.
Can be a single file in .spacy format or a .jsonl file.
Files with other extensions are treated as single plain text documents.
If a directory is provided it is traversed recursively to grab
all files to be processed.
The files can be a mixture of .spacy, .jsonl and text files.
If .jsonl is provided the specified field is going
to be grabbed ("text" by default)."""
out_help = "Path to save the resulting .spacy file"
code_help = (
"Path to Python file with additional " "code (registered functions) to be imported"
)
gold_help = "Use gold preprocessing provided in the .spacy files"
force_msg = (
"The provided output file already exists. "
"To force overwriting the output file, set the --force or -F flag."
)
DocOrStrStream = Union[Iterable[str], Iterable[Doc]]
def _stream_docbin(path: Path, vocab: Vocab) -> Iterable[Doc]:
"""
Stream Doc objects from DocBin.
"""
docbin = DocBin().from_disk(path)
for doc in docbin.get_docs(vocab):
yield doc
def _stream_jsonl(path: Path, field: str) -> Iterable[str]:
"""
Stream "text" field from JSONL. If the field "text" is
not found it raises error.
"""
for entry in srsly.read_jsonl(path):
if field not in entry:
msg.fail(f"{path} does not contain the required '{field}' field.", exits=1)
else:
yield entry[field]
def _stream_texts(paths: Iterable[Path]) -> Iterable[str]:
"""
Yields strings from text files in paths.
"""
for path in paths:
with open(path, "r") as fin:
text = fin.read()
yield text
@app.command("apply")
def apply_cli(
# fmt: off
model: str = Arg(..., help="Model name or path"),
data_path: Path = Arg(..., help=path_help, exists=True),
output_file: Path = Arg(..., help=out_help, dir_okay=False),
code_path: Optional[Path] = Opt(None, "--code", "-c", help=code_help),
text_key: str = Opt("text", "--text-key", "-tk", help="Key containing text string for JSONL"),
force_overwrite: bool = Opt(False, "--force", "-F", help="Force overwriting the output file"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU."),
batch_size: int = Opt(1, "--batch-size", "-b", help="Batch size."),
n_process: int = Opt(1, "--n-process", "-n", help="number of processors to use.")
):
"""
Apply a trained pipeline to documents to get predictions.
Expects a loadable spaCy pipeline and path to the data, which
can be a directory or a file.
The data files can be provided in multiple formats:
1. .spacy files
2. .jsonl files with a specified "field" to read the text from.
3. Files with any other extension are assumed to be containing
a single document.
DOCS: https://spacy.io/api/cli#apply
"""
data_path = ensure_path(data_path)
output_file = ensure_path(output_file)
code_path = ensure_path(code_path)
if output_file.exists() and not force_overwrite:
msg.fail(force_msg, exits=1)
if not data_path.exists():
msg.fail(f"Couldn't find data path: {data_path}", exits=1)
import_code(code_path)
setup_gpu(use_gpu)
apply(data_path, output_file, model, text_key, batch_size, n_process)
def apply(
data_path: Path,
output_file: Path,
model: str,
json_field: str,
batch_size: int,
n_process: int,
):
docbin = DocBin(store_user_data=True)
paths = walk_directory(data_path)
if len(paths) == 0:
docbin.to_disk(output_file)
msg.warn(
"Did not find data to process,"
f" {data_path} seems to be an empty directory."
)
return
nlp = load_model(model)
msg.good(f"Loaded model {model}")
vocab = nlp.vocab
streams: List[DocOrStrStream] = []
text_files = []
for path in paths:
if path.suffix == ".spacy":
streams.append(_stream_docbin(path, vocab))
elif path.suffix == ".jsonl":
streams.append(_stream_jsonl(path, json_field))
else:
text_files.append(path)
if len(text_files) > 0:
streams.append(_stream_texts(text_files))
datagen = cast(DocOrStrStream, chain(*streams))
for doc in tqdm.tqdm(nlp.pipe(datagen, batch_size=batch_size, n_process=n_process)):
docbin.add(doc)
if output_file.suffix == "":
output_file = output_file.with_suffix(".spacy")
docbin.to_disk(output_file)

174
spacy/cli/benchmark_speed.py Normal file
View File

@ -0,0 +1,174 @@
from typing import Iterable, List, Optional
import random
from itertools import islice
import numpy
from pathlib import Path
import time
from tqdm import tqdm
import typer
from wasabi import msg
from .. import util
from ..language import Language
from ..tokens import Doc
from ..training import Corpus
from ._util import Arg, Opt, benchmark_cli, setup_gpu
@benchmark_cli.command(
"speed",
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
)
def benchmark_speed_cli(
# fmt: off
ctx: typer.Context,
model: str = Arg(..., help="Model name or path"),
data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True),
batch_size: Optional[int] = Opt(None, "--batch-size", "-b", min=1, help="Override the pipeline batch size"),
no_shuffle: bool = Opt(False, "--no-shuffle", help="Do not shuffle benchmark data"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
n_batches: int = Opt(50, "--batches", help="Minimum number of batches to benchmark", min=30,),
warmup_epochs: int = Opt(3, "--warmup", "-w", min=0, help="Number of iterations over the data for warmup"),
# fmt: on
):
"""
Benchmark a pipeline. Expects a loadable spaCy pipeline and benchmark
data in the binary .spacy format.
"""
setup_gpu(use_gpu=use_gpu, silent=False)
nlp = util.load_model(model)
batch_size = batch_size if batch_size is not None else nlp.batch_size
corpus = Corpus(data_path)
docs = [eg.predicted for eg in corpus(nlp)]
if len(docs) == 0:
msg.fail("Cannot benchmark speed using an empty corpus.", exits=1)
print(f"Warming up for {warmup_epochs} epochs...")
warmup(nlp, docs, warmup_epochs, batch_size)
print()
print(f"Benchmarking {n_batches} batches...")
wps = benchmark(nlp, docs, n_batches, batch_size, not no_shuffle)
print()
print_outliers(wps)
print_mean_with_ci(wps)
# Lowercased, behaves as a context manager function.
class time_context:
"""Register the running time of a context."""
def __enter__(self):
self.start = time.perf_counter()
return self
def __exit__(self, type, value, traceback):
self.elapsed = time.perf_counter() - self.start
class Quartiles:
"""Calculate the q1, q2, q3 quartiles and the inter-quartile range (iqr)
of a sample."""
q1: float
q2: float
q3: float
iqr: float
def __init__(self, sample: numpy.ndarray) -> None:
self.q1 = numpy.quantile(sample, 0.25)
self.q2 = numpy.quantile(sample, 0.5)
self.q3 = numpy.quantile(sample, 0.75)
self.iqr = self.q3 - self.q1
def annotate(
nlp: Language, docs: List[Doc], batch_size: Optional[int]
) -> numpy.ndarray:
docs = nlp.pipe(tqdm(docs, unit="doc"), batch_size=batch_size)
wps = []
while True:
with time_context() as elapsed:
batch_docs = list(
islice(docs, batch_size if batch_size else nlp.batch_size)
)
if len(batch_docs) == 0:
break
n_tokens = count_tokens(batch_docs)
wps.append(n_tokens / elapsed.elapsed)
return numpy.array(wps)
def benchmark(
nlp: Language,
docs: List[Doc],
n_batches: int,
batch_size: int,
shuffle: bool,
) -> numpy.ndarray:
if shuffle:
bench_docs = [
nlp.make_doc(random.choice(docs).text)
for _ in range(n_batches * batch_size)
]
else:
bench_docs = [
nlp.make_doc(docs[i % len(docs)].text)
for i in range(n_batches * batch_size)
]
return annotate(nlp, bench_docs, batch_size)
def bootstrap(x, statistic=numpy.mean, iterations=10000) -> numpy.ndarray:
"""Apply a statistic to repeated random samples of an array."""
return numpy.fromiter(
(
statistic(numpy.random.choice(x, len(x), replace=True))
for _ in range(iterations)
),
numpy.float64,
)
def count_tokens(docs: Iterable[Doc]) -> int:
return sum(len(doc) for doc in docs)
def print_mean_with_ci(sample: numpy.ndarray):
mean = numpy.mean(sample)
bootstrap_means = bootstrap(sample)
bootstrap_means.sort()
# 95% confidence interval
low = bootstrap_means[int(len(bootstrap_means) * 0.025)]
high = bootstrap_means[int(len(bootstrap_means) * 0.975)]
print(f"Mean: {mean:.1f} words/s (95% CI: {low-mean:.1f} +{high-mean:.1f})")
def print_outliers(sample: numpy.ndarray):
quartiles = Quartiles(sample)
n_outliers = numpy.sum(
(sample < (quartiles.q1 - 1.5 * quartiles.iqr))
| (sample > (quartiles.q3 + 1.5 * quartiles.iqr))
)
n_extreme_outliers = numpy.sum(
(sample < (quartiles.q1 - 3.0 * quartiles.iqr))
| (sample > (quartiles.q3 + 3.0 * quartiles.iqr))
)
print(
f"Outliers: {(100 * n_outliers) / len(sample):.1f}%, extreme outliers: {(100 * n_extreme_outliers) / len(sample)}%"
)
def warmup(
nlp: Language, docs: List[Doc], warmup_epochs: int, batch_size: Optional[int]
) -> numpy.ndarray:
docs = warmup_epochs * docs
return annotate(nlp, docs, batch_size)

57
spacy/cli/convert.py
View File

@ -1,4 +1,4 @@
from typing import Callable, Iterable, Mapping, Optional, Any, List, Union
from typing import Callable, Iterable, Mapping, Optional, Any, Union
from enum import Enum
from pathlib import Path
from wasabi import Printer
@ -7,7 +7,7 @@ import re
import sys
import itertools
from ._util import app, Arg, Opt
from ._util import app, Arg, Opt, walk_directory
from ..training import docs_to_json
from ..tokens import Doc, DocBin
from ..training.converters import iob_to_docs, conll_ner_to_docs, json_to_docs
@ -28,6 +28,8 @@ CONVERTERS: Mapping[str, Callable[..., Iterable[Doc]]] = {
"json": json_to_docs,
}
AUTO = "auto"
# File types that can be written to stdout
FILE_TYPES_STDOUT = ("json",)
@ -49,7 +51,7 @@ def convert_cli(
model: Optional[str] = Opt(None, "--model", "--base", "-b", help="Trained spaCy pipeline for sentence segmentation to use as base (for --seg-sents)"),
morphology: bool = Opt(False, "--morphology", "-m", help="Enable appending morphology to tags"),
merge_subtokens: bool = Opt(False, "--merge-subtokens", "-T", help="Merge CoNLL-U subtokens"),
converter: str = Opt("auto", "--converter", "-c", help=f"Converter: {tuple(CONVERTERS.keys())}"),
converter: str = Opt(AUTO, "--converter", "-c", help=f"Converter: {tuple(CONVERTERS.keys())}"),
ner_map: Optional[Path] = Opt(None, "--ner-map", "-nm", help="NER tag mapping (as JSON-encoded dict of entity types)", exists=True),
lang: Optional[str] = Opt(None, "--lang", "-l", help="Language (if tokenizer required)"),
concatenate: bool = Opt(None, "--concatenate", "-C", help="Concatenate output to a single file"),
@ -70,8 +72,8 @@ def convert_cli(
output_dir: Union[str, Path] = "-" if output_dir == Path("-") else output_dir
silent = output_dir == "-"
msg = Printer(no_print=silent)
verify_cli_args(msg, input_path, output_dir, file_type.value, converter, ner_map)
converter = _get_converter(msg, converter, input_path)
verify_cli_args(msg, input_path, output_dir, file_type.value, converter, ner_map)
convert(
input_path,
output_dir,
@ -100,7 +102,7 @@ def convert(
model: Optional[str] = None,
morphology: bool = False,
merge_subtokens: bool = False,
converter: str = "auto",
converter: str,
ner_map: Optional[Path] = None,
lang: Optional[str] = None,
concatenate: bool = False,
@ -189,33 +191,6 @@ def autodetect_ner_format(input_data: str) -> Optional[str]:
return None
def walk_directory(path: Path, converter: str) -> List[Path]:
if not path.is_dir():
return [path]
paths = [path]
locs = []
seen = set()
for path in paths:
if str(path) in seen:
continue
seen.add(str(path))
if path.parts[-1].startswith("."):
continue
elif path.is_dir():
paths.extend(path.iterdir())
elif converter == "json" and not path.parts[-1].endswith("json"):
continue
elif converter == "conll" and not path.parts[-1].endswith("conll"):
continue
elif converter == "iob" and not path.parts[-1].endswith("iob"):
continue
else:
locs.append(path)
# It's good to sort these, in case the ordering messes up cache.
locs.sort()
return locs
def verify_cli_args(
msg: Printer,
input_path: Path,
@ -239,18 +214,22 @@ def verify_cli_args(
input_locs = walk_directory(input_path, converter)
if len(input_locs) == 0:
msg.fail("No input files in directory", input_path, exits=1)
file_types = list(set([loc.suffix[1:] for loc in input_locs]))
if converter == "auto" and len(file_types) >= 2:
file_types_str = ",".join(file_types)
msg.fail("All input files must be same type", file_types_str, exits=1)
if converter != "auto" and converter not in CONVERTERS:
if converter not in CONVERTERS:
msg.fail(f"Can't find converter for {converter}", exits=1)
def _get_converter(msg, converter, input_path: Path):
if input_path.is_dir():
input_path = walk_directory(input_path, converter)[0]
if converter == "auto":
if converter == AUTO:
input_locs = walk_directory(input_path, suffix=None)
file_types = list(set([loc.suffix[1:] for loc in input_locs]))
if len(file_types) >= 2:
file_types_str = ",".join(file_types)
msg.fail("All input files must be same type", file_types_str, exits=1)
input_path = input_locs[0]
else:
input_path = walk_directory(input_path, suffix=converter)[0]
if converter == AUTO:
converter = input_path.suffix[1:]
if converter == "ner" or converter == "iob":
with input_path.open(encoding="utf8") as file_:

113
spacy/cli/debug_data.py
View File

@ -1,5 +1,5 @@
from typing import Any, Dict, Iterable, List, Optional, Sequence, Set, Tuple, Union
from typing import cast, overload
from typing import Literal, cast, overload
from pathlib import Path
from collections import Counter
import sys
@ -9,17 +9,18 @@ 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
from ..pipeline import TrainablePipe
from ..pipeline._parser_internals import nonproj
from ..pipeline._parser_internals.nonproj import DELIMITER
from ..pipeline import Morphologizer, SpanCategorizer
from ..pipeline._edit_tree_internals.edit_trees import EditTrees
from ..morphology import Morphology
from ..language import Language
from ..util import registry, resolve_dot_names
from ..compat import Literal
from ..vectors import Mode as VectorsMode
from .. import util
@ -670,6 +671,59 @@ def debug_data(
f"Found {gold_train_data['n_cycles']} projectivized train sentence(s) with cycles"
)
if "trainable_lemmatizer" in factory_names:
msg.divider("Trainable Lemmatizer")
trees_train: Set[str] = gold_train_data["lemmatizer_trees"]
trees_dev: Set[str] = gold_dev_data["lemmatizer_trees"]
# This is necessary context when someone is attempting to interpret whether the
# number of trees exclusively in the dev set is meaningful.
msg.info(f"{len(trees_train)} lemmatizer trees generated from training data")
msg.info(f"{len(trees_dev)} lemmatizer trees generated from dev data")
dev_not_train = trees_dev - trees_train
if len(dev_not_train) != 0:
pct = len(dev_not_train) / len(trees_dev)
msg.info(
f"{len(dev_not_train)} lemmatizer trees ({pct*100:.1f}% of dev trees)"
" were found exclusively in the dev data."
)
else:
# Would we ever expect this case? It seems like it would be pretty rare,
# and we might actually want a warning?
msg.info("All trees in dev data present in training data.")
if gold_train_data["n_low_cardinality_lemmas"] > 0:
n = gold_train_data["n_low_cardinality_lemmas"]
msg.warn(f"{n} training docs with 0 or 1 unique lemmas.")
if gold_dev_data["n_low_cardinality_lemmas"] > 0:
n = gold_dev_data["n_low_cardinality_lemmas"]
msg.warn(f"{n} dev docs with 0 or 1 unique lemmas.")
if gold_train_data["no_lemma_annotations"] > 0:
n = gold_train_data["no_lemma_annotations"]
msg.warn(f"{n} training docs with no lemma annotations.")
else:
msg.good("All training docs have lemma annotations.")
if gold_dev_data["no_lemma_annotations"] > 0:
n = gold_dev_data["no_lemma_annotations"]
msg.warn(f"{n} dev docs with no lemma annotations.")
else:
msg.good("All dev docs have lemma annotations.")
if gold_train_data["partial_lemma_annotations"] > 0:
n = gold_train_data["partial_lemma_annotations"]
msg.info(f"{n} training docs with partial lemma annotations.")
else:
msg.good("All training docs have complete lemma annotations.")
if gold_dev_data["partial_lemma_annotations"] > 0:
n = gold_dev_data["partial_lemma_annotations"]
msg.info(f"{n} dev docs with partial lemma annotations.")
else:
msg.good("All dev docs have complete lemma annotations.")
msg.divider("Summary")
good_counts = msg.counts[MESSAGES.GOOD]
warn_counts = msg.counts[MESSAGES.WARN]
@ -731,7 +785,13 @@ def _compile_gold(
"n_cats_multilabel": 0,
"n_cats_bad_values": 0,
"texts": set(),
"lemmatizer_trees": set(),
"no_lemma_annotations": 0,
"partial_lemma_annotations": 0,
"n_low_cardinality_lemmas": 0,
}
if "trainable_lemmatizer" in factory_names:
trees = EditTrees(nlp.vocab.strings)
for eg in examples:
gold = eg.reference
doc = eg.predicted
@ -861,6 +921,25 @@ def _compile_gold(
data["n_nonproj"] += 1
if nonproj.contains_cycle(aligned_heads):
data["n_cycles"] += 1
if "trainable_lemmatizer" in factory_names:
# from EditTreeLemmatizer._labels_from_data
if all(token.lemma == 0 for token in gold):
data["no_lemma_annotations"] += 1
continue
if any(token.lemma == 0 for token in gold):
data["partial_lemma_annotations"] += 1
lemma_set = set()
for token in gold:
if token.lemma != 0:
lemma_set.add(token.lemma)
tree_id = trees.add(token.text, token.lemma_)
tree_str = trees.tree_to_str(tree_id)
data["lemmatizer_trees"].add(tree_str)
# We want to identify cases where lemmas aren't assigned
# or are all assigned the same value, as this would indicate
# an issue since we're expecting a large set of lemmas
if len(lemma_set) < 2 and len(gold) > 1:
data["n_low_cardinality_lemmas"] += 1
return data
@ -934,6 +1013,7 @@ def _get_labels_from_model(nlp: Language, factory_name: str) -> Set[str]:
labels: Set[str] = set()
for pipe_name in pipe_names:
pipe = nlp.get_pipe(pipe_name)
assert isinstance(pipe, TrainablePipe)
labels.update(pipe.labels)
return labels
@ -989,7 +1069,8 @@ 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 +1085,10 @@ 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 +1116,7 @@ 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 +1131,15 @@ 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 +1150,18 @@ 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(

43
spacy/cli/download.py
View File

@ -7,7 +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 ..errors import OLD_MODEL_SHORTCUTS
from ..util import is_prerelease_version
@app.command(
@ -19,7 +19,7 @@ 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 +35,12 @@ 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
@ -49,31 +54,38 @@ 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:
msg.warn(
f"As of spaCy v3.0, shortcuts like '{model}' are deprecated. Please "
f"use the full pipeline package name '{OLD_MODEL_SHORTCUTS[model]}' instead."
)
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:
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:
@ -101,6 +113,11 @@ 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:

7
spacy/cli/evaluate.py
View File

@ -7,12 +7,15 @@ from thinc.api import fix_random_seed
from ..training import Corpus
from ..tokens import Doc
from ._util import app, Arg, Opt, setup_gpu, import_code
from ._util import app, Arg, Opt, setup_gpu, import_code, benchmark_cli
from ..scorer import Scorer
from .. import util
from .. import displacy
@benchmark_cli.command(
"accuracy",
)
@app.command("evaluate")
def evaluate_cli(
# fmt: off
@ -36,7 +39,7 @@ def evaluate_cli(
dependency parses in a HTML file, set as output directory as the
displacy_path argument.
DOCS: https://spacy.io/api/cli#evaluate
DOCS: https://spacy.io/api/cli#benchmark-accuracy
"""
import_code(code_path)
evaluate(

233
spacy/cli/find_threshold.py Normal file
View File

@ -0,0 +1,233 @@
import functools
import operator
from pathlib import Path
import logging
from typing import Optional, Tuple, Any, Dict, List
import numpy
import wasabi.tables
from ..pipeline import TextCategorizer, MultiLabel_TextCategorizer
from ..errors import Errors
from ..training import Corpus
from ._util import app, Arg, Opt, import_code, setup_gpu
from .. import util
_DEFAULTS = {
"n_trials": 11,
"use_gpu": -1,
"gold_preproc": False,
}
@app.command(
"find-threshold",
context_settings={"allow_extra_args": False, "ignore_unknown_options": True},
)
def find_threshold_cli(
# fmt: off
model: str = Arg(..., help="Model name or path"),
data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True),
pipe_name: str = Arg(..., help="Name of pipe to examine thresholds for"),
threshold_key: str = Arg(..., help="Key of threshold attribute in component's configuration"),
scores_key: str = Arg(..., help="Metric to optimize"),
n_trials: int = Opt(_DEFAULTS["n_trials"], "--n_trials", "-n", help="Number of trials to determine optimal thresholds"),
code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
use_gpu: int = Opt(_DEFAULTS["use_gpu"], "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
gold_preproc: bool = Opt(_DEFAULTS["gold_preproc"], "--gold-preproc", "-G", help="Use gold preprocessing"),
verbose: bool = Opt(False, "--silent", "-V", "-VV", help="Display more information for debugging purposes"),
# fmt: on
):
"""
Runs prediction trials for a trained model with varying tresholds to maximize
the specified metric. The search space for the threshold is traversed linearly
from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout`
(the corresponding API call to `spacy.cli.find_threshold.find_threshold()`
returns all results).
This is applicable only for components whose predictions are influenced by
thresholds - e.g. `textcat_multilabel` and `spancat`, but not `textcat`. Note
that the full path to the corresponding threshold attribute in the config has to
be provided.
DOCS: https://spacy.io/api/cli#find-threshold
"""
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
import_code(code_path)
find_threshold(
model=model,
data_path=data_path,
pipe_name=pipe_name,
threshold_key=threshold_key,
scores_key=scores_key,
n_trials=n_trials,
use_gpu=use_gpu,
gold_preproc=gold_preproc,
silent=False,
)
def find_threshold(
model: str,
data_path: Path,
pipe_name: str,
threshold_key: str,
scores_key: str,
*,
n_trials: int = _DEFAULTS["n_trials"], # type: ignore
use_gpu: int = _DEFAULTS["use_gpu"], # type: ignore
gold_preproc: bool = _DEFAULTS["gold_preproc"], # type: ignore
silent: bool = True,
) -> Tuple[float, float, Dict[float, float]]:
"""
Runs prediction trials for models with varying tresholds to maximize the specified metric.
model (Union[str, Path]): Pipeline to evaluate. Can be a package or a path to a data directory.
data_path (Path): Path to file with DocBin with docs to use for threshold search.
pipe_name (str): Name of pipe to examine thresholds for.
threshold_key (str): Key of threshold attribute in component's configuration.
scores_key (str): Name of score to metric to optimize.
n_trials (int): Number of trials to determine optimal thresholds.
use_gpu (int): GPU ID or -1 for CPU.
gold_preproc (bool): Whether to use gold preprocessing. Gold preprocessing helps the annotations align to the
tokenization, and may result in sequences of more consistent length. However, it may reduce runtime accuracy due
to train/test skew.
silent (bool): Whether to print non-error-related output to stdout.
RETURNS (Tuple[float, float, Dict[float, float]]): Best found threshold, the corresponding score, scores for all
evaluated thresholds.
"""
setup_gpu(use_gpu, silent=silent)
data_path = util.ensure_path(data_path)
if not data_path.exists():
wasabi.msg.fail("Evaluation data not found", data_path, exits=1)
nlp = util.load_model(model)
if pipe_name not in nlp.component_names:
raise AttributeError(
Errors.E001.format(name=pipe_name, opts=nlp.component_names)
)
pipe = nlp.get_pipe(pipe_name)
if not hasattr(pipe, "scorer"):
raise AttributeError(Errors.E1045)
if type(pipe) == TextCategorizer:
wasabi.msg.warn(
"The `textcat` component doesn't use a threshold as it's not applicable to the concept of "
"exclusive classes. All thresholds will yield the same results."
)
if not silent:
wasabi.msg.info(
title=f"Optimizing for {scores_key} for component '{pipe_name}' with {n_trials} "
f"trials."
)
# Load evaluation corpus.
corpus = Corpus(data_path, gold_preproc=gold_preproc)
dev_dataset = list(corpus(nlp))
config_keys = threshold_key.split(".")
def set_nested_item(
config: Dict[str, Any], keys: List[str], value: float
) -> Dict[str, Any]:
"""Set item in nested dictionary. Adapted from https://stackoverflow.com/a/54138200.
config (Dict[str, Any]): Configuration dictionary.
keys (List[Any]): Path to value to set.
value (float): Value to set.
RETURNS (Dict[str, Any]): Updated dictionary.
"""
functools.reduce(operator.getitem, keys[:-1], config)[keys[-1]] = value
return config
def filter_config(
config: Dict[str, Any], keys: List[str], full_key: str
) -> Dict[str, Any]:
"""Filters provided config dictionary so that only the specified keys path remains.
config (Dict[str, Any]): Configuration dictionary.
keys (List[Any]): Path to value to set.
full_key (str): Full user-specified key.
RETURNS (Dict[str, Any]): Filtered dictionary.
"""
if keys[0] not in config:
wasabi.msg.fail(
title=f"Failed to look up `{full_key}` in config: sub-key {[keys[0]]} not found.",
text=f"Make sure you specified {[keys[0]]} correctly. The following sub-keys are available instead: "
f"{list(config.keys())}",
exits=1,
)
return {
keys[0]: filter_config(config[keys[0]], keys[1:], full_key)
if len(keys) > 1
else config[keys[0]]
}
# Evaluate with varying threshold values.
scores: Dict[float, float] = {}
config_keys_full = ["components", pipe_name, *config_keys]
table_col_widths = (10, 10)
thresholds = numpy.linspace(0, 1, n_trials)
print(wasabi.tables.row(["Threshold", f"{scores_key}"], widths=table_col_widths))
for threshold in thresholds:
# Reload pipeline with overrides specifying the new threshold.
nlp = util.load_model(
model,
config=set_nested_item(
filter_config(
nlp.config, config_keys_full, ".".join(config_keys_full)
).copy(),
config_keys_full,
threshold,
),
)
if hasattr(pipe, "cfg"):
setattr(
nlp.get_pipe(pipe_name),
"cfg",
set_nested_item(getattr(pipe, "cfg"), config_keys, threshold),
)
eval_scores = nlp.evaluate(dev_dataset)
if scores_key not in eval_scores:
wasabi.msg.fail(
title=f"Failed to look up score `{scores_key}` in evaluation results.",
text=f"Make sure you specified the correct value for `scores_key`. The following scores are "
f"available: {list(eval_scores.keys())}",
exits=1,
)
scores[threshold] = eval_scores[scores_key]
if not isinstance(scores[threshold], (float, int)):
wasabi.msg.fail(
f"Returned score for key '{scores_key}' is not numeric. Threshold optimization only works for numeric "
f"scores.",
exits=1,
)
print(
wasabi.row(
[round(threshold, 3), round(scores[threshold], 3)],
widths=table_col_widths,
)
)
best_threshold = max(scores.keys(), key=(lambda key: scores[key]))
# If all scores are identical, emit warning.
if len(set(scores.values())) == 1:
wasabi.msg.warn(
title="All scores are identical. Verify that all settings are correct.",
text=""
if (
not isinstance(pipe, MultiLabel_TextCategorizer)
or scores_key in ("cats_macro_f", "cats_micro_f")
)
else "Use `cats_macro_f` or `cats_micro_f` when optimizing the threshold for `textcat_multilabel`.",
)
else:
if not silent:
print(
f"\nBest threshold: {round(best_threshold, ndigits=4)} with {scores_key} value of {scores[best_threshold]}."
)
return best_threshold, scores[best_threshold], scores

59
spacy/cli/info.py
View File

@ -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,

2
spacy/cli/package.py
View File

@ -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),

6
spacy/cli/project/assets.py
View File

@ -189,7 +189,11 @@ def convert_asset_url(url: str) -> str:
RETURNS (str): The converted URL.
"""
# If the asset URL is a regular GitHub URL it's likely a mistake
if re.match(r"(http(s?)):\/\/github.com", url) and "releases/download" not in url:
if (
re.match(r"(http(s?)):\/\/github.com", url)
and "releases/download" not in url
and "/raw/" not in url
):
converted = url.replace("github.com", "raw.githubusercontent.com")
converted = re.sub(r"/(tree|blob)/", "/", converted)
msg.warn(

57
spacy/cli/project/dvc.py
View File

@ -25,6 +25,7 @@ def project_update_dvc_cli(
project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
workflow: Optional[str] = Arg(None, help=f"Name of workflow defined in {PROJECT_FILE}. Defaults to first workflow if not set."),
verbose: bool = Opt(False, "--verbose", "-V", help="Print more info"),
quiet: bool = Opt(False, "--quiet", "-q", help="Print less info"),
force: bool = Opt(False, "--force", "-F", help="Force update DVC config"),
# fmt: on
):
@ -36,7 +37,7 @@ def project_update_dvc_cli(
DOCS: https://spacy.io/api/cli#project-dvc
"""
project_update_dvc(project_dir, workflow, verbose=verbose, force=force)
project_update_dvc(project_dir, workflow, verbose=verbose, quiet=quiet, force=force)
def project_update_dvc(
@ -44,6 +45,7 @@ def project_update_dvc(
workflow: Optional[str] = None,
*,
verbose: bool = False,
quiet: bool = False,
force: bool = False,
) -> None:
"""Update the auto-generated Data Version Control (DVC) config file. A DVC
@ -54,11 +56,12 @@ def project_update_dvc(
workflow (Optional[str]): Optional name of workflow defined in project.yml.
If not set, the first workflow will be used.
verbose (bool): Print more info.
quiet (bool): Print less info.
force (bool): Force update DVC config.
"""
config = load_project_config(project_dir)
updated = update_dvc_config(
project_dir, config, workflow, verbose=verbose, force=force
project_dir, config, workflow, verbose=verbose, quiet=quiet, force=force
)
help_msg = "To execute the workflow with DVC, run: dvc repro"
if updated:
@ -72,7 +75,7 @@ def update_dvc_config(
config: Dict[str, Any],
workflow: Optional[str] = None,
verbose: bool = False,
silent: bool = False,
quiet: bool = False,
force: bool = False,
) -> bool:
"""Re-run the DVC commands in dry mode and update dvc.yaml file in the
@ -83,7 +86,7 @@ def update_dvc_config(
path (Path): The path to the project directory.
config (Dict[str, Any]): The loaded project.yml.
verbose (bool): Whether to print additional info (via DVC).
silent (bool): Don't output anything (via DVC).
quiet (bool): Don't output anything (via DVC).
force (bool): Force update, even if hashes match.
RETURNS (bool): Whether the DVC config file was updated.
"""
@ -105,6 +108,14 @@ def update_dvc_config(
dvc_config_path.unlink()
dvc_commands = []
config_commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
# some flags that apply to every command
flags = []
if verbose:
flags.append("--verbose")
if quiet:
flags.append("--quiet")
for name in workflows[workflow]:
command = config_commands[name]
deps = command.get("deps", [])
@ -118,14 +129,26 @@ def update_dvc_config(
deps_cmd = [c for cl in [["-d", p] for p in deps] for c in cl]
outputs_cmd = [c for cl in [["-o", p] for p in outputs] for c in cl]
outputs_nc_cmd = [c for cl in [["-O", p] for p in outputs_no_cache] for c in cl]
dvc_cmd = ["run", "-n", name, "-w", str(path), "--no-exec"]
dvc_cmd = ["run", *flags, "-n", name, "-w", str(path), "--no-exec"]
if command.get("no_skip"):
dvc_cmd.append("--always-changed")
full_cmd = [*dvc_cmd, *deps_cmd, *outputs_cmd, *outputs_nc_cmd, *project_cmd]
dvc_commands.append(join_command(full_cmd))
if not dvc_commands:
# If we don't check for this, then there will be an error when reading the
# config, since DVC wouldn't create it.
msg.fail(
"No usable commands for DVC found. This can happen if none of your "
"commands have dependencies or outputs.",
exits=1,
)
with working_dir(path):
dvc_flags = {"--verbose": verbose, "--quiet": silent}
run_dvc_commands(dvc_commands, flags=dvc_flags)
for c in dvc_commands:
dvc_command = "dvc " + c
run_command(dvc_command)
with dvc_config_path.open("r+", encoding="utf8") as f:
content = f.read()
f.seek(0, 0)
@ -133,26 +156,6 @@ def update_dvc_config(
return True
def run_dvc_commands(
commands: Iterable[str] = SimpleFrozenList(), flags: Dict[str, bool] = {}
) -> None:
"""Run a sequence of DVC commands in a subprocess, in order.
commands (List[str]): The string commands without the leading "dvc".
flags (Dict[str, bool]): Conditional flags to be added to command. Makes it
easier to pass flags like --quiet that depend on a variable or
command-line setting while avoiding lots of nested conditionals.
"""
for c in commands:
command = split_command(c)
dvc_command = ["dvc", *command]
# Add the flags if they are set to True
for flag, is_active in flags.items():
if is_active:
dvc_command.append(flag)
run_command(dvc_command)
def check_workflows(workflows: List[str], workflow: Optional[str] = None) -> None:
"""Validate workflows provided in project.yml and check that a given
workflow can be used to generate a DVC config.

55
spacy/cli/project/remote_storage.py
View File

@ -5,14 +5,17 @@ import hashlib
import urllib.parse
import tarfile
from pathlib import Path
from wasabi import msg
from .._util import get_hash, get_checksum, download_file, ensure_pathy
from ...util import make_tempdir, get_minor_version, ENV_VARS, check_bool_env_var
from .._util import get_hash, get_checksum, upload_file, download_file
from .._util import ensure_pathy, make_tempdir
from ...util import get_minor_version, ENV_VARS, check_bool_env_var
from ...git_info import GIT_VERSION
from ... import about
from ...errors import Errors
if TYPE_CHECKING:
from pathy import Pathy # noqa: F401
from pathy import FluidPath # noqa: F401
class RemoteStorage:
@ -27,7 +30,7 @@ class RemoteStorage:
self.url = ensure_pathy(url)
self.compression = compression
def push(self, path: Path, command_hash: str, content_hash: str) -> "Pathy":
def push(self, path: Path, command_hash: str, content_hash: str) -> "FluidPath":
"""Compress a file or directory within a project and upload it to a remote
storage. If an object exists at the full URL, nothing is done.
@ -48,9 +51,7 @@ class RemoteStorage:
mode_string = f"w:{self.compression}" if self.compression else "w"
with tarfile.open(tar_loc, mode=mode_string) as tar_file:
tar_file.add(str(loc), arcname=str(path))
with tar_loc.open(mode="rb") as input_file:
with url.open(mode="wb") as output_file:
output_file.write(input_file.read())
upload_file(tar_loc, url)
return url
def pull(
@ -59,7 +60,7 @@ class RemoteStorage:
*,
command_hash: Optional[str] = None,
content_hash: Optional[str] = None,
) -> Optional["Pathy"]:
) -> Optional["FluidPath"]:
"""Retrieve a file from the remote cache. If the file already exists,
nothing is done.
@ -84,7 +85,23 @@ class RemoteStorage:
with tarfile.open(tar_loc, mode=mode_string) as tar_file:
# This requires that the path is added correctly, relative
# to root. This is how we set things up in push()
tar_file.extractall(self.root)
# Disallow paths outside the current directory for the tar
# file (CVE-2007-4559, directory traversal vulnerability)
def is_within_directory(directory, target):
abs_directory = os.path.abspath(directory)
abs_target = os.path.abspath(target)
prefix = os.path.commonprefix([abs_directory, abs_target])
return prefix == abs_directory
def safe_extract(tar, path):
for member in tar.getmembers():
member_path = os.path.join(path, member.name)
if not is_within_directory(path, member_path):
raise ValueError(Errors.E852)
tar.extractall(path)
safe_extract(tar_file, self.root)
return url
def find(
@ -93,25 +110,37 @@ class RemoteStorage:
*,
command_hash: Optional[str] = None,
content_hash: Optional[str] = None,
) -> Optional["Pathy"]:
) -> Optional["FluidPath"]:
"""Find the best matching version of a file within the storage,
or `None` if no match can be found. If both the creation and content hash
are specified, only exact matches will be returned. Otherwise, the most
recent matching file is preferred.
"""
name = self.encode_name(str(path))
urls = []
if command_hash is not None and content_hash is not None:
url = self.make_url(path, command_hash, content_hash)
url = self.url / name / command_hash / content_hash
urls = [url] if url.exists() else []
elif command_hash is not None:
if (self.url / name / command_hash).exists():
urls = list((self.url / name / command_hash).iterdir())
else:
urls = list((self.url / name).iterdir())
if (self.url / name).exists():
for sub_dir in (self.url / name).iterdir():
urls.extend(sub_dir.iterdir())
if content_hash is not None:
urls = [url for url in urls if url.parts[-1] == content_hash]
if len(urls) >= 2:
try:
urls.sort(key=lambda x: x.stat().last_modified) # type: ignore
except Exception:
msg.warn(
"Unable to sort remote files by last modified. The file(s) "
"pulled from the cache may not be the most recent."
)
return urls[-1] if urls else None
def make_url(self, path: Path, command_hash: str, content_hash: str) -> "Pathy":
def make_url(self, path: Path, command_hash: str, content_hash: str) -> "FluidPath":
"""Construct a URL from a subpath, a creation hash and a content hash."""
return self.url / self.encode_name(str(path)) / command_hash / content_hash

56
spacy/cli/project/run.py
View File

@ -1,5 +1,8 @@
from typing import Optional, List, Dict, Sequence, Any, Iterable
from typing import Optional, List, Dict, Sequence, Any, Iterable, Tuple
import os.path
from pathlib import Path
import pkg_resources
from wasabi import msg
from wasabi.util import locale_escape
import sys
@ -50,6 +53,7 @@ def project_run(
force: bool = False,
dry: bool = False,
capture: bool = False,
skip_requirements_check: bool = False,
) -> None:
"""Run a named script defined in the project.yml. If the script is part
of the default pipeline (defined in the "run" section), DVC is used to
@ -66,11 +70,19 @@ def project_run(
sys.exit will be called with the return code. You should use capture=False
when you want to turn over execution to the command, and capture=True
when you want to run the command more like a function.
skip_requirements_check (bool): Whether to skip the requirements check.
"""
config = load_project_config(project_dir, overrides=overrides)
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 not skip_requirements_check:
if config.get("check_requirements", True) and os.path.exists(req_path):
with req_path.open() as requirements_file:
_check_requirements([req.strip() for req in requirements_file])
if subcommand in workflows:
msg.info(f"Running workflow '{subcommand}'")
for cmd in workflows[subcommand]:
@ -81,6 +93,7 @@ def project_run(
force=force,
dry=dry,
capture=capture,
skip_requirements_check=True,
)
else:
cmd = commands[subcommand]
@ -88,8 +101,8 @@ def project_run(
if not (project_dir / dep).exists():
err = f"Missing dependency specified by command '{subcommand}': {dep}"
err_help = "Maybe you forgot to run the 'project assets' command or a previous step?"
err_kwargs = {"exits": 1} if not dry else {}
msg.fail(err, err_help, **err_kwargs)
err_exits = 1 if not dry else None
msg.fail(err, err_help, exits=err_exits)
check_spacy_commit = check_bool_env_var(ENV_VARS.PROJECT_USE_GIT_VERSION)
with working_dir(project_dir) as current_dir:
msg.divider(subcommand)
@ -195,6 +208,8 @@ 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:
@ -308,3 +323,38 @@ def get_fileinfo(project_dir: Path, paths: List[str]) -> List[Dict[str, Optional
md5 = get_checksum(file_path) if file_path.exists() else None
data.append({"path": path, "md5": md5})
return data
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())
except Exception:
msg.warn(
f"Unable to check requirement: {req} "
"Checks are currently limited to requirement specifiers "
"(PEP 508)"
)
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

21
spacy/cli/templates/quickstart_training.jinja
View File

@ -1,7 +1,7 @@
{# This is a template for training configs used for the quickstart widget in
the docs and the init config command. It encodes various best practices and
can help generate the best possible configuration, given a user's requirements. #}
{%- set use_transformer = hardware != "cpu" -%}
{%- set use_transformer = hardware != "cpu" and transformer_data -%}
{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "trainable_lemmatizer"] -%}
[paths]
@ -87,12 +87,11 @@ grad_factor = 1.0
factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v2"
@architectures = "spacy.TransitionBasedParser.v3"
state_type = "parser"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = false
nO = null
[components.parser.model.tok2vec]
@ -108,12 +107,11 @@ grad_factor = 1.0
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
@architectures = "spacy.TransitionBasedParser.v3"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = false
nO = null
[components.ner.model.tok2vec]
@ -271,13 +269,8 @@ 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]
@ -319,12 +312,11 @@ width = ${components.tok2vec.model.encode.width}
factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v2"
@architectures = "spacy.TransitionBasedParser.v3"
state_type = "parser"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = true
nO = null
[components.parser.model.tok2vec]
@ -337,12 +329,11 @@ width = ${components.tok2vec.model.encode.width}
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
@architectures = "spacy.TransitionBasedParser.v3"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = true
nO = null
[components.ner.model.tok2vec]

10
spacy/cli/templates/quickstart_training_recommendations.yml
View File

@ -37,6 +37,15 @@ bn:
accuracy:
name: sagorsarker/bangla-bert-base
size_factor: 3
ca:
word_vectors: null
transformer:
efficiency:
name: projecte-aina/roberta-base-ca-v2
size_factor: 3
accuracy:
name: projecte-aina/roberta-base-ca-v2
size_factor: 3
da:
word_vectors: da_core_news_lg
transformer:
@ -271,4 +280,3 @@ zh:
accuracy:
name: bert-base-chinese
size_factor: 3
has_letters: false

13
spacy/compat.py
View File

@ -22,19 +22,6 @@ try:
except ImportError:
cupy = None
if sys.version_info[:2] >= (3, 8): # Python 3.8+
from typing import Literal, Protocol, runtime_checkable
else:
from typing_extensions import Literal, Protocol, runtime_checkable # noqa: F401
# Important note: The importlib_metadata "backport" includes functionality
# that's not part of the built-in importlib.metadata. We should treat this
# import like the built-in and only use what's available there.
try: # Python 3.8+
import importlib.metadata as importlib_metadata
except ImportError:
from catalogue import _importlib_metadata as importlib_metadata # type: ignore[no-redef] # noqa: F401
from thinc.api import Optimizer # noqa: F401
pickle = pickle

2
spacy/default_config.cfg
View File

@ -90,6 +90,8 @@ dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
# Optional callback before nlp object is saved to disk after training
before_to_disk = null
# Optional callback that is invoked at the start of each training step
before_update = null
[training.logger]
@loggers = "spacy.ConsoleLogger.v1"

17
spacy/displacy/__init__.py
View File

@ -11,6 +11,7 @@ from .render import DependencyRenderer, EntityRenderer, SpanRenderer
from ..tokens import Doc, Span
from ..errors import Errors, Warnings
from ..util import is_in_jupyter
from ..util import find_available_port
_html = {}
@ -36,7 +37,7 @@ def render(
jupyter (bool): Override Jupyter auto-detection.
options (dict): Visualiser-specific options, e.g. colors.
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
RETURNS (str): Rendered HTML markup.
RETURNS (str): Rendered SVG or HTML markup.
DOCS: https://spacy.io/api/top-level#displacy.render
USAGE: https://spacy.io/usage/visualizers
@ -82,6 +83,7 @@ def serve(
manual: bool = False,
port: int = 5000,
host: str = "0.0.0.0",
auto_select_port: bool = False,
) -> None:
"""Serve displaCy visualisation.
@ -93,12 +95,15 @@ def serve(
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
port (int): Port to serve visualisation.
host (str): Host to serve visualisation.
auto_select_port (bool): Automatically select a port if the specified port is in use.
DOCS: https://spacy.io/api/top-level#displacy.serve
USAGE: https://spacy.io/usage/visualizers
"""
from wsgiref import simple_server
port = find_available_port(port, host, auto_select_port)
if is_in_jupyter():
warnings.warn(Warnings.W011)
render(docs, style=style, page=page, minify=minify, options=options, manual=manual)
@ -123,7 +128,8 @@ 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 +215,7 @@ 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.
@ -227,12 +233,13 @@ def parse_spans(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
"kb_id": span.kb_id_ if span.kb_id_ else "",
"kb_url": kb_url_template.format(span.kb_id_) if kb_url_template else "#",
}
for span in doc.spans[spans_key]
for span in doc.spans.get(spans_key, [])
]
tokens = [token.text for token in doc]
if not spans:
warnings.warn(Warnings.W117.format(spans_key=spans_key))
keys = list(doc.spans.keys())
warnings.warn(Warnings.W117.format(spans_key=spans_key, keys=keys))
title = doc.user_data.get("title", None) if hasattr(doc, "user_data") else None
settings = get_doc_settings(doc)
return {

4
spacy/displacy/render.py
View File

@ -94,7 +94,7 @@ class SpanRenderer:
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (str): Rendered HTML markup.
RETURNS (str): Rendered SVG or HTML markup.
"""
rendered = []
for i, p in enumerate(parsed):
@ -510,7 +510,7 @@ class EntityRenderer:
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (str): Rendered HTML markup.
RETURNS (str): Rendered SVG or HTML markup.
"""
rendered = []
for i, p in enumerate(parsed):

105
spacy/errors.py
View File

@ -1,5 +1,5 @@
from typing import Literal
import warnings
from .compat import Literal
class ErrorsWithCodes(type):
@ -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
@ -131,13 +131,6 @@ class Warnings(metaclass=ErrorsWithCodes):
"and make it independent. For example, `replace_listeners = "
"[\"model.tok2vec\"]` See the documentation for details: "
"https://spacy.io/usage/training#config-components-listeners")
W088 = ("The pipeline component {name} implements a `begin_training` "
"method, which won't be called by spaCy. As of v3.0, `begin_training` "
"has been renamed to `initialize`, so you likely want to rename the "
"component method. See the documentation for details: "
"https://spacy.io/api/language#initialize")
W089 = ("As of spaCy v3.0, the `nlp.begin_training` method has been renamed "
"to `nlp.initialize`.")
W090 = ("Could not locate any {format} files in path '{path}'.")
W091 = ("Could not clean/remove the temp directory at {dir}: {msg}.")
W092 = ("Ignoring annotations for sentence starts, as dependency heads are set.")
@ -199,7 +192,7 @@ class Warnings(metaclass=ErrorsWithCodes):
W117 = ("No spans to visualize found in Doc object with spans_key: '{spans_key}'. If this is "
"surprising to you, make sure the Doc was processed using a model "
"that supports span categorization, and check the `doc.spans[spans_key]` "
"property manually if necessary.")
"property manually if necessary.\n\nAvailable keys: {keys}")
W118 = ("Term '{term}' not found in glossary. It may however be explained in documentation "
"for the corpora used to train the language. Please check "
"`nlp.meta[\"sources\"]` for any relevant links.")
@ -212,6 +205,11 @@ 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 `enable` with value {enable} does not contain all values specified in the config option "
"`enabled` ({enabled}). Be aware that this might affect other components in your pipeline.")
W124 = ("{host}:{port} is already in use, using the nearest available port {serve_port} as an alternative.")
W400 = ("`use_upper=False` is ignored, the upper layer is always enabled")
class Errors(metaclass=ErrorsWithCodes):
@ -230,8 +228,9 @@ 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). "
@ -247,9 +246,7 @@ class Errors(metaclass=ErrorsWithCodes):
"https://spacy.io/usage/models")
E011 = ("Unknown operator: '{op}'. Options: {opts}")
E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
E016 = ("MultitaskObjective target should be function or one of: dep, "
"tag, ent, dep_tag_offset, ent_tag.")
E017 = ("Can only add unicode or bytes. Got type: {value_type}")
E017 = ("Can only add 'str' inputs to StringStore. Got type: {value_type}")
E018 = ("Can't retrieve string for hash '{hash_value}'. This usually "
"refers to an issue with the `Vocab` or `StringStore`.")
E019 = ("Can't create transition with unknown action ID: {action}. Action "
@ -342,6 +339,11 @@ class Errors(metaclass=ErrorsWithCodes):
"clear the existing vectors and resize the table.")
E074 = ("Error interpreting compiled match pattern: patterns are expected "
"to end with the attribute {attr}. Got: {bad_attr}.")
E079 = ("Error computing states in beam: number of predicted beams "
"({pbeams}) does not equal number of gold beams ({gbeams}).")
E080 = ("Duplicate state found in beam: {key}.")
E081 = ("Error getting gradient in beam: number of histories ({n_hist}) "
"does not equal number of losses ({losses}).")
E082 = ("Error deprojectivizing parse: number of heads ({n_heads}), "
"projective heads ({n_proj_heads}) and labels ({n_labels}) do not "
"match.")
@ -389,7 +391,7 @@ 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}")
@ -457,13 +459,13 @@ class Errors(metaclass=ErrorsWithCodes):
"same, but found '{nlp}' and '{vocab}' respectively.")
E152 = ("The attribute {attr} is not supported for token patterns. "
"Please use the option `validate=True` with the Matcher, PhraseMatcher, "
"EntityRuler or AttributeRuler for more details.")
"SpanRuler or AttributeRuler for more details.")
E153 = ("The value type {vtype} is not supported for token patterns. "
"Please use the option validate=True with Matcher, PhraseMatcher, "
"EntityRuler or AttributeRuler for more details.")
"SpanRuler or AttributeRuler for more details.")
E154 = ("One of the attributes or values is not supported for token "
"patterns. Please use the option `validate=True` with the Matcher, "
"PhraseMatcher, or EntityRuler for more details.")
"PhraseMatcher, or SpanRuler for more details.")
E155 = ("The pipeline needs to include a {pipe} in order to use "
"Matcher or PhraseMatcher with the attribute {attr}. "
"Try using `nlp()` instead of `nlp.make_doc()` or `list(nlp.pipe())` "
@ -487,7 +489,7 @@ class Errors(metaclass=ErrorsWithCodes):
"Current DocBin: {current}\nOther DocBin: {other}")
E169 = ("Can't find module: {module}")
E170 = ("Cannot apply transition {name}: invalid for the current state.")
E171 = ("Matcher.add received invalid 'on_match' callback argument: expected "
E171 = ("{name}.add received invalid 'on_match' callback argument: expected "
"callable or None, but got: {arg_type}")
E175 = ("Can't remove rule for unknown match pattern ID: {key}")
E176 = ("Alias '{alias}' is not defined in the Knowledge Base.")
@ -535,15 +537,22 @@ 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}.")
E203 = ("If the {name} embedding layer is not updated "
"during training, make sure to include it in 'annotating components'")
# New errors added in v4.x
E300 = ("nlp.pipe(text_tuples, as_tuples=True) has been replaced with:\n"
"nlp.pipe_as_tuples(text_tuples)")
# New errors added in v3.x
E851 = ("The 'textcat' component labels should only have values of 0 or 1, "
"but found value of '{val}'.")
E852 = ("The tar file pulled from the remote attempted an unsafe path "
"traversal.")
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.")
@ -709,11 +718,11 @@ 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, "
E928 = ("An InMemoryLookupKB can only be serialized to/from from a directory, "
"but the provided argument {loc} points to a file.")
E929 = ("Couldn't read KnowledgeBase from {loc}. The path does not seem to exist.")
E929 = ("Couldn't read InMemoryLookupKB from {loc}. The path does not seem to exist.")
E930 = ("Received invalid get_examples callback in `{method}`. "
"Expected function that returns an iterable of Example objects but "
"got: {obj}")
@ -721,13 +730,6 @@ class Errors(metaclass=ErrorsWithCodes):
"method in component '{name}'. If you want to use this "
"method, make sure it's overwritten on the subclass.")
E940 = ("Found NaN values in scores.")
E941 = ("Can't find model '{name}'. It looks like you're trying to load a "
"model from a shortcut, which is obsolete as of spaCy v3.0. To "
"load the model, use its full name instead:\n\n"
"nlp = spacy.load(\"{full}\")\n\nFor more details on the available "
"models, see the models directory: https://spacy.io/models. If you "
"want to create a blank model, use spacy.blank: "
"nlp = spacy.blank(\"{name}\")")
E942 = ("Executing `after_{name}` callback failed. Expected the function to "
"return an initialized nlp object but got: {value}. Maybe "
"you forgot to return the modified object in your function?")
@ -741,7 +743,7 @@ class Errors(metaclass=ErrorsWithCodes):
"loaded nlp object, but got: {source}")
E947 = ("`Matcher.add` received invalid `greedy` argument: expected "
"a string value from {expected} but got: '{arg}'")
E948 = ("`Matcher.add` received invalid 'patterns' argument: expected "
E948 = ("`{name}.add` received invalid 'patterns' argument: expected "
"a list, but got: {arg_type}")
E949 = ("Unable to align tokens for the predicted and reference docs. It "
"is only possible to align the docs when both texts are the same "
@ -915,8 +917,6 @@ class Errors(metaclass=ErrorsWithCodes):
E1021 = ("`pos` value \"{pp}\" is not a valid Universal Dependencies tag. "
"Non-UD tags should use the `tag` property.")
E1022 = ("Words must be of type str or int, but input is of type '{wtype}'")
E1023 = ("Couldn't read EntityRuler from the {path}. This file doesn't "
"exist.")
E1024 = ("A pattern with {attr_type} '{label}' is not present in "
"'{component}' patterns.")
E1025 = ("Cannot intify the value '{value}' as an IOB string. The only "
@ -939,19 +939,32 @@ 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 = ("Expected `candidates_batch_size` to be >= 1, but got: {value}")
E1045 = ("Encountered {parent} subclass without `{parent}.{method}` "
"method in '{name}'. If you want to use this method, make "
"sure it's overwritten on the subclass.")
E1046 = ("{cls_name} is an abstract class and cannot be instantiated. If you are looking for spaCy's default "
"knowledge base, use `InMemoryLookupKB`.")
E1047 = ("`find_threshold()` only supports components with a `scorer` attribute.")
E1048 = ("Got '{unexpected}' as console progress bar type, but expected one of the following: {expected}")
E1049 = ("No available port found for displaCy on host {host}. Please specify an available port "
"with `displacy.serve(doc, port=port)`")
E1050 = ("Port {port} is already in use. Please specify an available port with `displacy.serve(doc, port=port)` "
"or use `auto_switch_port=True` to pick an available port automatically.")
# Deprecated model shortcuts, only used in errors and warnings
OLD_MODEL_SHORTCUTS = {
"en": "en_core_web_sm", "de": "de_core_news_sm", "es": "es_core_news_sm",
"pt": "pt_core_news_sm", "fr": "fr_core_news_sm", "it": "it_core_news_sm",
"nl": "nl_core_news_sm", "el": "el_core_news_sm", "nb": "nb_core_news_sm",
"lt": "lt_core_news_sm", "xx": "xx_ent_wiki_sm"
}
# v4 error strings
E4000 = ("Expected a Doc as input, but got: '{type}'")
E4001 = ("Expected input to be one of the following types: ({expected_types}), "
"but got '{received_type}'")
E4002 = ("Pipe '{name}' requires a teacher pipe for distillation.")
E4003 = ("Training examples for distillation must have the exact same tokens in the "
"reference and predicted docs.")
E4004 = ("Backprop is not supported when is_train is not set.")
# fmt: on

3
spacy/kb/__init__.py Normal file
View File

@ -0,0 +1,3 @@
from .kb import KnowledgeBase
from .kb_in_memory import InMemoryLookupKB
from .candidate import Candidate, get_candidates, get_candidates_batch

12
spacy/kb/candidate.pxd Normal file
View File

@ -0,0 +1,12 @@
from .kb cimport KnowledgeBase
from libcpp.vector cimport vector
from ..typedefs cimport hash_t
# Object used by the Entity Linker that summarizes one entity-alias candidate combination.
cdef class Candidate:
cdef readonly KnowledgeBase kb
cdef hash_t entity_hash
cdef float entity_freq
cdef vector[float] entity_vector
cdef hash_t alias_hash
cdef float prior_prob

74
spacy/kb/candidate.pyx Normal file
View File

@ -0,0 +1,74 @@
# cython: infer_types=True, profile=True
from typing import Iterable
from .kb cimport KnowledgeBase
from ..tokens import Span
cdef class Candidate:
"""A `Candidate` object refers to a textual mention (`alias`) that may or may not be resolved
to a specific `entity` from a Knowledge Base. This will be used as input for the entity linking
algorithm which will disambiguate the various candidates to the correct one.
Each candidate (alias, entity) pair is assigned a certain prior probability.
DOCS: https://spacy.io/api/kb/#candidate-init
"""
def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
self.kb = kb
self.entity_hash = entity_hash
self.entity_freq = entity_freq
self.entity_vector = entity_vector
self.alias_hash = alias_hash
self.prior_prob = prior_prob
@property
def entity(self) -> int:
"""RETURNS (uint64): hash of the entity's KB ID/name"""
return self.entity_hash
@property
def entity_(self) -> str:
"""RETURNS (str): ID/name of this entity in the KB"""
return self.kb.vocab.strings[self.entity_hash]
@property
def alias(self) -> int:
"""RETURNS (uint64): hash of the alias"""
return self.alias_hash
@property
def alias_(self) -> str:
"""RETURNS (str): ID of the original alias"""
return self.kb.vocab.strings[self.alias_hash]
@property
def entity_freq(self) -> float:
return self.entity_freq
@property
def entity_vector(self) -> Iterable[float]:
return self.entity_vector
@property
def prior_prob(self) -> float:
return self.prior_prob
def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
"""
Return candidate entities for a given mention and fetching appropriate entries from the index.
kb (KnowledgeBase): Knowledge base to query.
mention (Span): Entity mention for which to identify candidates.
RETURNS (Iterable[Candidate]): Identified candidates.
"""
return kb.get_candidates(mention)
def get_candidates_batch(kb: KnowledgeBase, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]:
"""
Return candidate entities for the given mentions and fetching appropriate entries from the index.
kb (KnowledgeBase): Knowledge base to query.
mention (Iterable[Span]): Entity mentions for which to identify candidates.
RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
"""
return kb.get_candidates_batch(mentions)

10
spacy/kb/kb.pxd Normal file
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@ -0,0 +1,10 @@
"""Knowledge-base for entity or concept linking."""
from cymem.cymem cimport Pool
from libc.stdint cimport int64_t
from ..vocab cimport Vocab
cdef class KnowledgeBase:
cdef Pool mem
cdef readonly Vocab vocab
cdef readonly int64_t entity_vector_length

108
spacy/kb/kb.pyx Normal file
View File

@ -0,0 +1,108 @@
# cython: infer_types=True, profile=True
from pathlib import Path
from typing import Iterable, Tuple, Union
from cymem.cymem cimport Pool
from .candidate import Candidate
from ..tokens import Span
from ..util import SimpleFrozenList
from ..errors import Errors
cdef class KnowledgeBase:
"""A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
This is an abstract class and requires its operations to be implemented.
DOCS: https://spacy.io/api/kb
"""
def __init__(self, vocab: Vocab, entity_vector_length: int):
"""Create a KnowledgeBase."""
# Make sure abstract KB is not instantiated.
if self.__class__ == KnowledgeBase:
raise TypeError(
Errors.E1046.format(cls_name=self.__class__.__name__)
)
self.vocab = vocab
self.entity_vector_length = entity_vector_length
self.mem = Pool()
def get_candidates_batch(self, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]:
"""
Return candidate entities for specified texts. Each candidate defines the entity, the original alias,
and the prior probability of that alias resolving to that entity.
If no candidate is found for a given text, an empty list is returned.
mentions (Iterable[Span]): Mentions for which to get candidates.
RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
"""
return [self.get_candidates(span) for span in mentions]
def get_candidates(self, mention: Span) -> Iterable[Candidate]:
"""
Return candidate entities for specified text. Each candidate defines the entity, the original alias,
and the prior probability of that alias resolving to that entity.
If the no candidate is found for a given text, an empty list is returned.
mention (Span): Mention for which to get candidates.
RETURNS (Iterable[Candidate]): Identified candidates.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="get_candidates", name=self.__name__)
)
def get_vectors(self, entities: Iterable[str]) -> Iterable[Iterable[float]]:
"""
Return vectors for entities.
entity (str): Entity name/ID.
RETURNS (Iterable[Iterable[float]]): Vectors for specified entities.
"""
return [self.get_vector(entity) for entity in entities]
def get_vector(self, str entity) -> Iterable[float]:
"""
Return vector for entity.
entity (str): Entity name/ID.
RETURNS (Iterable[float]): Vector for specified entity.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="get_vector", name=self.__name__)
)
def to_bytes(self, **kwargs) -> bytes:
"""Serialize the current state to a binary string.
RETURNS (bytes): Current state as binary string.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="to_bytes", name=self.__name__)
)
def from_bytes(self, bytes_data: bytes, *, exclude: Tuple[str] = tuple()):
"""Load state from a binary string.
bytes_data (bytes): KB state.
exclude (Tuple[str]): Properties to exclude when restoring KB.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="from_bytes", name=self.__name__)
)
def to_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
"""
Write KnowledgeBase content to disk.
path (Union[str, Path]): Target file path.
exclude (Iterable[str]): List of components to exclude.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="to_disk", name=self.__name__)
)
def from_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
"""
Load KnowledgeBase content from disk.
path (Union[str, Path]): Target file path.
exclude (Iterable[str]): List of components to exclude.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="from_disk", name=self.__name__)
)

24
spacy/kb.pxd → spacy/kb/kb_in_memory.pxd
View File

@ -1,14 +1,12 @@
"""Knowledge-base for entity or concept linking."""
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from libcpp.vector cimport vector
from libc.stdint cimport int32_t, int64_t
from libc.stdio cimport FILE
from .vocab cimport Vocab
from .typedefs cimport hash_t
from .structs cimport KBEntryC, AliasC
from ..typedefs cimport hash_t
from ..structs cimport KBEntryC, AliasC
from .kb cimport KnowledgeBase
ctypedef vector[KBEntryC] entry_vec
ctypedef vector[AliasC] alias_vec
@ -16,21 +14,7 @@ ctypedef vector[float] float_vec
ctypedef vector[float_vec] float_matrix
# Object used by the Entity Linker that summarizes one entity-alias candidate combination.
cdef class Candidate:
cdef readonly KnowledgeBase kb
cdef hash_t entity_hash
cdef float entity_freq
cdef vector[float] entity_vector
cdef hash_t alias_hash
cdef float prior_prob
cdef class KnowledgeBase:
cdef Pool mem
cdef readonly Vocab vocab
cdef int64_t entity_vector_length
cdef class InMemoryLookupKB(KnowledgeBase):
# This maps 64bit keys (hash of unique entity string)
# to 64bit values (position of the _KBEntryC struct in the _entries vector).
# The PreshMap is pretty space efficient, as it uses open addressing. So

96
spacy/kb.pyx → spacy/kb/kb_in_memory.pyx
View File

@ -1,8 +1,7 @@
# cython: infer_types=True, profile=True
from typing import Iterator, Iterable, Callable, Dict, Any
from typing import Iterable, Callable, Dict, Any, Union
import srsly
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from cpython.exc cimport PyErr_SetFromErrno
from libc.stdio cimport fopen, fclose, fread, fwrite, feof, fseek
@ -12,85 +11,28 @@ from libcpp.vector cimport vector
from pathlib import Path
import warnings
from .typedefs cimport hash_t
from .errors import Errors, Warnings
from . import util
from .util import SimpleFrozenList, ensure_path
cdef class Candidate:
"""A `Candidate` object refers to a textual mention (`alias`) that may or may not be resolved
to a specific `entity` from a Knowledge Base. This will be used as input for the entity linking
algorithm which will disambiguate the various candidates to the correct one.
Each candidate (alias, entity) pair is assigned to a certain prior probability.
DOCS: https://spacy.io/api/kb/#candidate_init
"""
def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
self.kb = kb
self.entity_hash = entity_hash
self.entity_freq = entity_freq
self.entity_vector = entity_vector
self.alias_hash = alias_hash
self.prior_prob = prior_prob
@property
def entity(self):
"""RETURNS (uint64): hash of the entity's KB ID/name"""
return self.entity_hash
@property
def entity_(self):
"""RETURNS (str): ID/name of this entity in the KB"""
return self.kb.vocab.strings[self.entity_hash]
@property
def alias(self):
"""RETURNS (uint64): hash of the alias"""
return self.alias_hash
@property
def alias_(self):
"""RETURNS (str): ID of the original alias"""
return self.kb.vocab.strings[self.alias_hash]
@property
def entity_freq(self):
return self.entity_freq
@property
def entity_vector(self):
return self.entity_vector
@property
def prior_prob(self):
return self.prior_prob
from ..tokens import Span
from ..typedefs cimport hash_t
from ..errors import Errors, Warnings
from .. import util
from ..util import SimpleFrozenList, ensure_path
from ..vocab cimport Vocab
from .kb cimport KnowledgeBase
from .candidate import Candidate as Candidate
def get_candidates(KnowledgeBase kb, span) -> Iterator[Candidate]:
"""
Return candidate entities for a given span by using the text of the span as the alias
and fetching appropriate entries from the index.
This particular function is optimized to work with the built-in KB functionality,
but any other custom candidate generation method can be used in combination with the KB as well.
"""
return kb.get_alias_candidates(span.text)
cdef class KnowledgeBase:
"""A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
cdef class InMemoryLookupKB(KnowledgeBase):
"""An `InMemoryLookupKB` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
DOCS: https://spacy.io/api/kb
DOCS: https://spacy.io/api/inmemorylookupkb
"""
def __init__(self, Vocab vocab, entity_vector_length):
"""Create a KnowledgeBase."""
self.mem = Pool()
self.entity_vector_length = entity_vector_length
"""Create an InMemoryLookupKB."""
super().__init__(vocab, entity_vector_length)
self._entry_index = PreshMap()
self._alias_index = PreshMap()
self.vocab = vocab
self._create_empty_vectors(dummy_hash=self.vocab.strings[""])
def _initialize_entities(self, int64_t nr_entities):
@ -104,11 +46,6 @@ cdef class KnowledgeBase:
self._alias_index = PreshMap(nr_aliases + 1)
self._aliases_table = alias_vec(nr_aliases + 1)
@property
def entity_vector_length(self):
"""RETURNS (uint64): length of the entity vectors"""
return self.entity_vector_length
def __len__(self):
return self.get_size_entities()
@ -286,7 +223,10 @@ cdef class KnowledgeBase:
alias_entry.probs = probs
self._aliases_table[alias_index] = alias_entry
def get_alias_candidates(self, str alias) -> Iterator[Candidate]:
def get_candidates(self, mention: Span) -> Iterable[Candidate]:
return self.get_alias_candidates(mention.text) # type: ignore
def get_alias_candidates(self, str alias) -> Iterable[Candidate]:
"""
Return candidate entities for an alias. Each candidate defines the entity, the original alias,
and the prior probability of that alias resolving to that entity.

6
spacy/lang/ca/lemmatizer.py
View File

@ -72,10 +72,10 @@ class CatalanLemmatizer(Lemmatizer):
oov_forms.append(form)
if not forms:
forms.extend(oov_forms)
if not forms and string in lookup_table.keys():
forms.append(self.lookup_lemmatize(token)[0])
# use lookups, and fall back to the token itself
if not forms:
forms.append(string)
forms.append(lookup_table.get(string, [string])[0])
forms = list(dict.fromkeys(forms))
self.cache[cache_key] = forms
return forms

2
spacy/lang/char_classes.py
View File

@ -280,7 +280,7 @@ _currency = (
_punct = (
r"… …… , : ; \! \? ¿ ؟ ¡ \( \) \[ \] \{ \} < > _ # \* & 。 ？ ！ ， 、 ； ： ～ · । ، ۔ ؛ ٪"
)
_quotes = r'\' " ” “ ` ‘ ´ ’ ‚ , „ » « 「 」 『 』 （ ） 〔 〕 【 】 《 》 〈 〉'
_quotes = r'\' " ” “ ` ‘ ´ ’ ‚ , „ » « 「 」 『 』 （ ） 〔 〕 【 】 《 》 〈 〉 〈 〉 ⟦ ⟧'
_hyphens = "- – — -- --- —— ~"
# Various symbols like dingbats, but also emoji

13
spacy/lang/fr/lemmatizer.py
View File

@ -53,11 +53,16 @@ class FrenchLemmatizer(Lemmatizer):
rules = rules_table.get(univ_pos, [])
string = string.lower()
forms = []
# first try lookup in table based on upos
if string in index:
forms.append(string)
self.cache[cache_key] = forms
return forms
# then add anything in the exceptions table
forms.extend(exceptions.get(string, []))
# if nothing found yet, use the rules
oov_forms = []
if not forms:
for old, new in rules:
@ -69,12 +74,14 @@ class FrenchLemmatizer(Lemmatizer):
forms.append(form)
else:
oov_forms.append(form)
# if still nothing, add the oov forms from rules
if not forms:
forms.extend(oov_forms)
if not forms and string in lookup_table.keys():
forms.append(self.lookup_lemmatize(token)[0])
# use lookups, which fall back to the token itself
if not forms:
forms.append(string)
forms.append(lookup_table.get(string, [string])[0])
forms = list(dict.fromkeys(forms))
self.cache[cache_key] = forms
return forms

4
spacy/lang/grc/__init__.py
View File

@ -1,11 +1,15 @@
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
from ...language import Language, BaseDefaults
class AncientGreekDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
prefixes = TOKENIZER_PREFIXES
suffixes = TOKENIZER_SUFFIXES
infixes = TOKENIZER_INFIXES
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS

46
spacy/lang/grc/punctuation.py Normal file
View File

@ -0,0 +1,46 @@
from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES, LIST_CURRENCY
from ..char_classes import LIST_ICONS, ALPHA_LOWER, ALPHA_UPPER, ALPHA, HYPHENS
from ..char_classes import CONCAT_QUOTES
_prefixes = (
[
"†",
"⸏",
]
+ LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ LIST_CURRENCY
+ LIST_ICONS
)
_suffixes = (
LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ LIST_ICONS
+ [
"†",
"⸎",
r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])[\-\.⸏]",
]
)
_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),
r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])—",
]
)
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_SUFFIXES = _suffixes
TOKENIZER_INFIXES = _infixes

107
spacy/lang/ko/__init__.py
View File

@ -18,21 +18,100 @@ DEFAULT_CONFIG = """
[nlp.tokenizer]
@tokenizers = "spacy.ko.KoreanTokenizer"
mecab_args = ""
"""
@registry.tokenizers("spacy.ko.KoreanTokenizer")
def create_tokenizer():
def create_tokenizer(mecab_args: str):
def korean_tokenizer_factory(nlp):
return KoreanTokenizer(nlp.vocab)
return KoreanTokenizer(nlp.vocab, mecab_args=mecab_args)
return korean_tokenizer_factory
class KoreanTokenizer(DummyTokenizer):
def __init__(self, vocab: Vocab, *, mecab_args: str = ""):
self.vocab = vocab
mecab = try_mecab_import()
self.mecab_tokenizer = mecab.Tagger(mecab_args)
def __reduce__(self):
return KoreanTokenizer, (self.vocab,)
def __call__(self, text: str) -> Doc:
dtokens = list(self.detailed_tokens(text))
surfaces = [dt["surface"] for dt in dtokens]
doc = Doc(self.vocab, words=surfaces, spaces=list(check_spaces(text, surfaces)))
for token, dtoken in zip(doc, dtokens):
first_tag, sep, eomi_tags = dtoken["tag"].partition("+")
token.tag_ = first_tag # stem(어간) or pre-final(선어말 어미)
if token.tag_ in TAG_MAP:
token.pos = TAG_MAP[token.tag_][POS]
else:
token.pos = X
token.lemma_ = dtoken["lemma"]
doc.user_data["full_tags"] = [dt["tag"] for dt in dtokens]
return doc
def detailed_tokens(self, text: str) -> Iterator[Dict[str, Any]]:
# 품사 태그(POS)[0], 의미 부류(semantic class)[1], 종성 유무(jongseong)[2], 읽기(reading)[3],
# 타입(type)[4], 첫번째 품사(start pos)[5], 마지막 품사(end pos)[6], 표현(expression)[7], *
for line in self.mecab_tokenizer.parse(text).split("\n"):
if line == "EOS":
break
surface, _, expr = line.partition("\t")
features = expr.split("/")[0].split(",")
tag = features[0]
lemma = "*"
if len(features) >= 8:
lemma = features[7]
if lemma == "*":
lemma = surface
yield {"surface": surface, "lemma": lemma, "tag": tag}
def score(self, examples):
validate_examples(examples, "KoreanTokenizer.score")
return Scorer.score_tokenization(examples)
class KoreanDefaults(BaseDefaults):
config = load_config_from_str(DEFAULT_CONFIG)
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
infixes = TOKENIZER_INFIXES
class Korean(Language):
lang = "ko"
Defaults = KoreanDefaults
def try_mecab_import():
try:
import mecab_ko as MeCab
return MeCab
except ImportError:
raise ImportError(
'The Korean tokenizer ("spacy.ko.KoreanTokenizer") requires '
"the python package `mecab-ko`: pip install mecab-ko"
) from None
@registry.tokenizers("spacy.KoreanNattoTokenizer.v1")
def create_natto_tokenizer():
def korean_natto_tokenizer_factory(nlp):
return KoreanNattoTokenizer(nlp.vocab)
return korean_natto_tokenizer_factory
class KoreanNattoTokenizer(DummyTokenizer):
def __init__(self, vocab: Vocab):
self.vocab = vocab
self._mecab = try_mecab_import() # type: ignore[func-returns-value]
self._mecab = self._try_mecab_import() # type: ignore[func-returns-value]
self._mecab_tokenizer = None
@property
@ -48,7 +127,7 @@ class KoreanTokenizer(DummyTokenizer):
return self._mecab_tokenizer
def __reduce__(self):
return KoreanTokenizer, (self.vocab,)
return KoreanNattoTokenizer, (self.vocab,)
def __call__(self, text: str) -> Doc:
dtokens = list(self.detailed_tokens(text))
@ -75,7 +154,7 @@ class KoreanTokenizer(DummyTokenizer):
feature = node.feature
tag, _, expr = feature.partition(",")
lemma, _, remainder = expr.partition("/")
if lemma == "*":
if lemma == "*" or lemma == "":
lemma = surface
yield {"surface": surface, "lemma": lemma, "tag": tag}
@ -83,28 +162,14 @@ class KoreanTokenizer(DummyTokenizer):
validate_examples(examples, "KoreanTokenizer.score")
return Scorer.score_tokenization(examples)
class KoreanDefaults(BaseDefaults):
config = load_config_from_str(DEFAULT_CONFIG)
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
infixes = TOKENIZER_INFIXES
class Korean(Language):
lang = "ko"
Defaults = KoreanDefaults
def try_mecab_import() -> None:
def _try_mecab_import(self):
try:
from natto import MeCab
return MeCab
except ImportError:
raise ImportError(
'The Korean tokenizer ("spacy.ko.KoreanTokenizer") requires '
'The Korean Natto tokenizer ("spacy.ko.KoreanNattoTokenizer") requires '
"[mecab-ko](https://bitbucket.org/eunjeon/mecab-ko/src/master/README.md), "
"[mecab-ko-dic](https://bitbucket.org/eunjeon/mecab-ko-dic), "
"and [natto-py](https://github.com/buruzaemon/natto-py)"

2
spacy/lang/ko/punctuation.py
View File

@ -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
View 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
View 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
View 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
View 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
View 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
View 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
View 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
View 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
View 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()
)

2
spacy/lang/nl/stop_words.py
View File

@ -15,7 +15,7 @@
STOP_WORDS = set(
"""
aan af al alle alles allebei alleen allen als altijd ander anders andere anderen aangaangde aangezien achter achterna
aan af al alle alles allebei alleen allen als altijd ander anders andere anderen aangaande aangezien achter achterna
afgelopen aldus alhoewel anderzijds
ben bij bijna bijvoorbeeld behalve beide beiden beneden bent bepaald beter betere betreffende binnen binnenin boven

3
spacy/lang/nl/syntax_iterators.py
View File

@ -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)
@ -58,6 +59,7 @@ 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)
if start_span >= end_span:
end_span = max(children_i) + 1
yield start_span, end_span, span_label
@ -65,6 +67,7 @@ def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
elif word.pos == PRON:
if word.dep in pronoun_deps:
start_span = word.i
if start_span >= end_span:
end_span = word.i + 1
yield start_span, end_span, span_label

2
spacy/lang/ru/__init__.py
View File

@ -28,7 +28,7 @@ class Russian(Language):
assigns=["token.lemma"],
default_config={
"model": None,
"mode": "pymorphy2",
"mode": "pymorphy3",
"overwrite": False,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},

58
spacy/lang/ru/lemmatizer.py
View File

@ -19,33 +19,48 @@ class RussianLemmatizer(Lemmatizer):
model: Optional[Model],
name: str = "lemmatizer",
*,
mode: str = "pymorphy2",
mode: str = "pymorphy3",
overwrite: bool = False,
scorer: Optional[Callable] = lemmatizer_score,
) -> None:
if mode == "pymorphy2":
if mode in {"pymorphy2", "pymorphy2_lookup"}:
try:
from pymorphy2 import MorphAnalyzer
except ImportError:
raise ImportError(
"The Russian lemmatizer mode 'pymorphy2' requires the "
"pymorphy2 library. Install it with: pip install pymorphy2"
"The lemmatizer mode 'pymorphy2' requires the "
"pymorphy2 library and dictionaries. Install them with: "
"pip install pymorphy2"
"# for Ukrainian dictionaries:"
"pip install pymorphy2-dicts-uk"
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer()
self._morph = MorphAnalyzer(lang="ru")
elif mode in {"pymorphy3", "pymorphy3_lookup"}:
try:
from pymorphy3 import MorphAnalyzer
except ImportError:
raise ImportError(
"The lemmatizer mode 'pymorphy3' requires the "
"pymorphy3 library and dictionaries. Install them with: "
"pip install pymorphy3"
"# for Ukrainian dictionaries:"
"pip install pymorphy3-dicts-uk"
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer(lang="ru")
super().__init__(
vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
)
def pymorphy2_lemmatize(self, token: Token) -> List[str]:
def _pymorphy_lemmatize(self, token: Token) -> List[str]:
string = token.text
univ_pos = token.pos_
morphology = token.morph.to_dict()
if univ_pos == "PUNCT":
return [PUNCT_RULES.get(string, string)]
if univ_pos not in ("ADJ", "DET", "NOUN", "NUM", "PRON", "PROPN", "VERB"):
# Skip unchangeable pos
return [string.lower()]
return self._pymorphy_lookup_lemmatize(token)
analyses = self._morph.parse(string)
filtered_analyses = []
for analysis in analyses:
@ -53,8 +68,10 @@ class RussianLemmatizer(Lemmatizer):
# Skip suggested parse variant for unknown word for pymorphy
continue
analysis_pos, _ = oc2ud(str(analysis.tag))
if analysis_pos == univ_pos or (
analysis_pos in ("NOUN", "PROPN") and univ_pos in ("NOUN", "PROPN")
if (
analysis_pos == univ_pos
or (analysis_pos in ("NOUN", "PROPN") and univ_pos in ("NOUN", "PROPN"))
or ((analysis_pos == "PRON") and (univ_pos == "DET"))
):
filtered_analyses.append(analysis)
if not len(filtered_analyses):
@ -97,13 +114,28 @@ class RussianLemmatizer(Lemmatizer):
dict.fromkeys([analysis.normal_form for analysis in filtered_analyses])
)
def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]:
def _pymorphy_lookup_lemmatize(self, token: Token) -> List[str]:
string = token.text
analyses = self._morph.parse(string)
if len(analyses) == 1:
return [analyses[0].normal_form]
# often multiple forms would derive from the same normal form
# thus check _unique_ normal forms
normal_forms = set([an.normal_form for an in analyses])
if len(normal_forms) == 1:
return [next(iter(normal_forms))]
return [string]
def pymorphy2_lemmatize(self, token: Token) -> List[str]:
return self._pymorphy_lemmatize(token)
def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]:
return self._pymorphy_lookup_lemmatize(token)
def pymorphy3_lemmatize(self, token: Token) -> List[str]:
return self._pymorphy_lemmatize(token)
def pymorphy3_lookup_lemmatize(self, token: Token) -> List[str]:
return self._pymorphy_lookup_lemmatize(token)
def oc2ud(oc_tag: str) -> Tuple[str, Dict[str, str]]:
gram_map = {

18
spacy/lang/ru/tokenizer_exceptions.py
View File

@ -61,6 +61,11 @@ for abbr in [
{ORTH: "2к23", NORM: "2023"},
{ORTH: "2к24", NORM: "2024"},
{ORTH: "2к25", NORM: "2025"},
{ORTH: "2к26", NORM: "2026"},
{ORTH: "2к27", NORM: "2027"},
{ORTH: "2к28", NORM: "2028"},
{ORTH: "2к29", NORM: "2029"},
{ORTH: "2к30", NORM: "2030"},
]:
_exc[abbr[ORTH]] = [abbr]
@ -268,8 +273,8 @@ for abbr in [
{ORTH: "з-ка", NORM: "заимка"},
{ORTH: "п-к", NORM: "починок"},
{ORTH: "киш.", NORM: "кишлак"},
{ORTH: "п. ст. ", NORM: "поселок станция"},
{ORTH: "п. ж/д ст. ", NORM: "поселок при железнодорожной станции"},
{ORTH: "п. ст.", NORM: "поселок станция"},
{ORTH: "п. ж/д ст.", NORM: "поселок при железнодорожной станции"},
{ORTH: "ж/д бл-ст", NORM: "железнодорожный блокпост"},
{ORTH: "ж/д б-ка", NORM: "железнодорожная будка"},
{ORTH: "ж/д в-ка", NORM: "железнодорожная ветка"},
@ -280,12 +285,12 @@ for abbr in [
{ORTH: "ж/д п.п.", NORM: "железнодорожный путевой пост"},
{ORTH: "ж/д о.п.", NORM: "железнодорожный остановочный пункт"},
{ORTH: "ж/д рзд.", NORM: "железнодорожный разъезд"},
{ORTH: "ж/д ст. ", NORM: "железнодорожная станция"},
{ORTH: "ж/д ст.", NORM: "железнодорожная станция"},
{ORTH: "м-ко", NORM: "местечко"},
{ORTH: "д.", NORM: "деревня"},
{ORTH: "с.", NORM: "село"},
{ORTH: "сл.", NORM: "слобода"},
{ORTH: "ст. ", NORM: "станция"},
{ORTH: "ст.", NORM: "станция"},
{ORTH: "ст-ца", NORM: "станица"},
{ORTH: "у.", NORM: "улус"},
{ORTH: "х.", NORM: "хутор"},
@ -388,8 +393,9 @@ for abbr in [
{ORTH: "прим.", NORM: "примечание"},
{ORTH: "прим.ред.", NORM: "примечание редакции"},
{ORTH: "см. также", NORM: "смотри также"},
{ORTH: "кв.м.", NORM: "квадрантный метр"},
{ORTH: "м2", NORM: "квадрантный метр"},
{ORTH: "см.", NORM: "смотри"},
{ORTH: "кв.м.", NORM: "квадратный метр"},
{ORTH: "м2", NORM: "квадратный метр"},
{ORTH: "б/у", NORM: "бывший в употреблении"},
{ORTH: "сокр.", NORM: "сокращение"},
{ORTH: "чел.", NORM: "человек"},

8
spacy/lang/sl/__init__.py
View File

@ -1,9 +1,17 @@
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES, TOKENIZER_PREFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from ...language import Language, BaseDefaults
class SlovenianDefaults(BaseDefaults):
stop_words = STOP_WORDS
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
prefixes = TOKENIZER_PREFIXES
infixes = TOKENIZER_INFIXES
suffixes = TOKENIZER_SUFFIXES
lex_attr_getters = LEX_ATTRS
class Slovenian(Language):

145
spacy/lang/sl/lex_attrs.py Normal file
View File

@ -0,0 +1,145 @@
from ...attrs import LIKE_NUM
from ...attrs import IS_CURRENCY
import unicodedata
_num_words = set(
"""
nula ničla nič ena dva tri štiri pet šest sedem osem
devet deset enajst dvanajst trinajst štirinajst petnajst
šestnajst sedemnajst osemnajst devetnajst dvajset trideset štirideset
petdeset šestdest sedemdeset osemdeset devedeset sto tisoč
milijon bilijon trilijon kvadrilijon nešteto
en eden enega enemu ennem enim enih enima enimi ene eni eno
dveh dvema dvem dvoje trije treh trem tremi troje štirje štirih štirim štirimi
petih petim petimi šestih šestim šestimi sedmih sedmim sedmimi osmih osmim osmimi
devetih devetim devetimi desetih desetim desetimi enajstih enajstim enajstimi
dvanajstih dvanajstim dvanajstimi trinajstih trinajstim trinajstimi
šestnajstih šestnajstim šestnajstimi petnajstih petnajstim petnajstimi
sedemnajstih sedemnajstim sedemnajstimi osemnajstih osemnajstim osemnajstimi
devetnajstih devetnajstim devetnajstimi dvajsetih dvajsetim dvajsetimi
""".split()
)
_ordinal_words = set(
"""
prvi drugi tretji četrti peti šesti sedmi osmi
deveti deseti enajsti dvanajsti trinajsti štirinajsti
petnajsti šestnajsti sedemnajsti osemnajsti devetnajsti
dvajseti trideseti štirideseti petdeseti šestdeseti sedemdeseti
osemdeseti devetdeseti stoti tisoči milijonti bilijonti
trilijonti kvadrilijonti nešteti
prva druga tretja četrta peta šesta sedma osma
deveta deseta enajsta dvanajsta trinajsta štirnajsta
petnajsta šestnajsta sedemnajsta osemnajsta devetnajsta
dvajseta trideseta štirideseta petdeseta šestdeseta sedemdeseta
osemdeseta devetdeseta stota tisoča milijonta bilijonta
trilijonta kvadrilijonta nešteta
prvo drugo tretje četrto peto šestro sedmo osmo
deveto deseto enajsto dvanajsto trinajsto štirnajsto
petnajsto šestnajsto sedemnajsto osemnajsto devetnajsto
dvajseto trideseto štirideseto petdeseto šestdeseto sedemdeseto
osemdeseto devetdeseto stoto tisočo milijonto bilijonto
trilijonto kvadrilijonto nešteto
prvega drugega tretjega četrtega petega šestega sedmega osmega
devega desetega enajstega dvanajstega trinajstega štirnajstega
petnajstega šestnajstega sedemnajstega osemnajstega devetnajstega
dvajsetega tridesetega štiridesetega petdesetega šestdesetega sedemdesetega
osemdesetega devetdesetega stotega tisočega milijontega bilijontega
trilijontega kvadrilijontega neštetega
prvemu drugemu tretjemu četrtemu petemu šestemu sedmemu osmemu devetemu desetemu
enajstemu dvanajstemu trinajstemu štirnajstemu petnajstemu šestnajstemu sedemnajstemu
osemnajstemu devetnajstemu dvajsetemu tridesetemu štiridesetemu petdesetemu šestdesetemu
sedemdesetemu osemdesetemu devetdesetemu stotemu tisočemu milijontemu bilijontemu
trilijontemu kvadrilijontemu neštetemu
prvem drugem tretjem četrtem petem šestem sedmem osmem devetem desetem
enajstem dvanajstem trinajstem štirnajstem petnajstem šestnajstem sedemnajstem
osemnajstem devetnajstem dvajsetem tridesetem štiridesetem petdesetem šestdesetem
sedemdesetem osemdesetem devetdesetem stotem tisočem milijontem bilijontem
trilijontem kvadrilijontem neštetem
prvim drugim tretjim četrtim petim šestim sedtim osmim devetim desetim
enajstim dvanajstim trinajstim štirnajstim petnajstim šestnajstim sedemnajstim
osemnajstim devetnajstim dvajsetim tridesetim štiridesetim petdesetim šestdesetim
sedemdesetim osemdesetim devetdesetim stotim tisočim milijontim bilijontim
trilijontim kvadrilijontim neštetim
prvih drugih tretjih četrthih petih šestih sedmih osmih deveth desetih
enajstih dvanajstih trinajstih štirnajstih petnajstih šestnajstih sedemnajstih
osemnajstih devetnajstih dvajsetih tridesetih štiridesetih petdesetih šestdesetih
sedemdesetih osemdesetih devetdesetih stotih tisočih milijontih bilijontih
trilijontih kvadrilijontih nešteth
prvima drugima tretjima četrtima petima šestima sedmima osmima devetima desetima
enajstima dvanajstima trinajstima štirnajstima petnajstima šestnajstima sedemnajstima
osemnajstima devetnajstima dvajsetima tridesetima štiridesetima petdesetima šestdesetima
sedemdesetima osemdesetima devetdesetima stotima tisočima milijontima bilijontima
trilijontima kvadrilijontima neštetima
prve druge četrte pete šeste sedme osme devete desete
enajste dvanajste trinajste štirnajste petnajste šestnajste sedemnajste
osemnajste devetnajste dvajsete tridesete štiridesete petdesete šestdesete
sedemdesete osemdesete devetdesete stote tisoče milijonte bilijonte
trilijonte kvadrilijonte neštete
prvimi drugimi tretjimi četrtimi petimi šestimi sedtimi osmimi devetimi desetimi
enajstimi dvanajstimi trinajstimi štirnajstimi petnajstimi šestnajstimi sedemnajstimi
osemnajstimi devetnajstimi dvajsetimi tridesetimi štiridesetimi petdesetimi šestdesetimi
sedemdesetimi osemdesetimi devetdesetimi stotimi tisočimi milijontimi bilijontimi
trilijontimi kvadrilijontimi neštetimi
""".split()
)
_currency_words = set(
"""
evro evra evru evrom evrov evroma evrih evrom evre evri evr eur
cent centa centu cenom centov centoma centih centom cente centi
dolar dolarja dolarji dolarju dolarjem dolarjev dolarjema dolarjih dolarje usd
tolar tolarja tolarji tolarju tolarjem tolarjev tolarjema tolarjih tolarje tol
dinar dinarja dinarji dinarju dinarjem dinarjev dinarjema dinarjih dinarje din
funt funta funti funtu funtom funtov funtoma funtih funte gpb
forint forinta forinti forintu forintom forintov forintoma forintih forinte
zlot zlota zloti zlotu zlotom zlotov zlotoma zlotih zlote
rupij rupija rupiji rupiju rupijem rupijev rupijema rupijih rupije
jen jena jeni jenu jenom jenov jenoma jenih jene
kuna kuni kune kuno kun kunama kunah kunam kunami
marka marki marke markama markah markami
""".split()
)
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
if text_lower in _ordinal_words:
return True
return False
def is_currency(text):
text_lower = text.lower()
if text in _currency_words:
return True
for char in text:
if unicodedata.category(char) != "Sc":
return False
return True
LEX_ATTRS = {LIKE_NUM: like_num, IS_CURRENCY: is_currency}

84
spacy/lang/sl/punctuation.py Normal file
View File

@ -0,0 +1,84 @@
from ..char_classes import (
LIST_ELLIPSES,
LIST_ICONS,
HYPHENS,
LIST_PUNCT,
LIST_QUOTES,
CURRENCY,
UNITS,
PUNCT,
LIST_CURRENCY,
CONCAT_QUOTES,
)
from ..char_classes import CONCAT_QUOTES, ALPHA_LOWER, ALPHA_UPPER, ALPHA
from ..char_classes import merge_chars
from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
INCLUDE_SPECIAL = ["\\+", "\\/", "\\•", "\\¯", "\\=", "\\×"] + HYPHENS.split("|")
_prefixes = INCLUDE_SPECIAL + BASE_TOKENIZER_PREFIXES
_suffixes = (
INCLUDE_SPECIAL
+ LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ LIST_ICONS
+ [
r"(?<=°[FfCcKk])\.",
r"(?<=[0-9])(?:{c})".format(c=CURRENCY),
r"(?<=[0-9])(?:{u})".format(u=UNITS),
r"(?<=[{al}{e}{p}(?:{q})])\.".format(
al=ALPHA_LOWER, e=r"%²\-\+", q=CONCAT_QUOTES, p=PUNCT
),
r"(?<=[{au}][{au}])\.".format(au=ALPHA_UPPER),
# split initials like J.K. Rowling
r"(?<=[A-Z]\.)(?:[A-Z].)",
]
)
# a list of all suffixes following a hyphen that are shouldn't split (eg. BTC-jev)
# source: Obeliks tokenizer - https://github.com/clarinsi/obeliks/blob/master/obeliks/res/TokRulesPart1.txt
CONCAT_QUOTES = CONCAT_QUOTES.replace("'", "")
HYPHENS_PERMITTED = (
"((a)|(evemu)|(evskega)|(i)|(jevega)|(jevska)|(jevskimi)|(jinemu)|(oma)|(ovim)|"
"(ovski)|(e)|(evi)|(evskem)|(ih)|(jevem)|(jevske)|(jevsko)|(jini)|(ov)|(ovima)|"
"(ovskih)|(em)|(evih)|(evskemu)|(ja)|(jevemu)|(jevskega)|(ji)|(jinih)|(ova)|"
"(ovimi)|(ovskim)|(ema)|(evim)|(evski)|(je)|(jevi)|(jevskem)|(jih)|(jinim)|"
"(ove)|(ovo)|(ovskima)|(ev)|(evima)|(evskih)|(jem)|(jevih)|(jevskemu)|(jin)|"
"(jinima)|(ovega)|(ovska)|(ovskimi)|(eva)|(evimi)|(evskim)|(jema)|(jevim)|"
"(jevski)|(jina)|(jinimi)|(ovem)|(ovske)|(ovsko)|(eve)|(evo)|(evskima)|(jev)|"
"(jevima)|(jevskih)|(jine)|(jino)|(ovemu)|(ovskega)|(u)|(evega)|(evska)|"
"(evskimi)|(jeva)|(jevimi)|(jevskim)|(jinega)|(ju)|(ovi)|(ovskem)|(evem)|"
"(evske)|(evsko)|(jeve)|(jevo)|(jevskima)|(jinem)|(om)|(ovih)|(ovskemu)|"
"(ovec)|(ovca)|(ovcu)|(ovcem)|(ovcev)|(ovcema)|(ovcih)|(ovci)|(ovce)|(ovcimi)|"
"(evec)|(evca)|(evcu)|(evcem)|(evcev)|(evcema)|(evcih)|(evci)|(evce)|(evcimi)|"
"(jevec)|(jevca)|(jevcu)|(jevcem)|(jevcev)|(jevcema)|(jevcih)|(jevci)|(jevce)|"
"(jevcimi)|(ovka)|(ovke)|(ovki)|(ovko)|(ovk)|(ovkama)|(ovkah)|(ovkam)|(ovkami)|"
"(evka)|(evke)|(evki)|(evko)|(evk)|(evkama)|(evkah)|(evkam)|(evkami)|(jevka)|"
"(jevke)|(jevki)|(jevko)|(jevk)|(jevkama)|(jevkah)|(jevkam)|(jevkami)|(timi)|"
"(im)|(ima)|(a)|(imi)|(e)|(o)|(ega)|(ti)|(em)|(tih)|(emu)|(tim)|(i)|(tima)|"
"(ih)|(ta)|(te)|(to)|(tega)|(tem)|(temu))"
)
_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})(?!{hp}$)(?=[{a}])".format(
a=ALPHA, h=HYPHENS, hp=HYPHENS_PERMITTED
),
r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
]
)
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_SUFFIXES = _suffixes
TOKENIZER_INFIXES = _infixes

392
spacy/lang/sl/stop_words.py
View File

@ -1,326 +1,84 @@
# Source: https://github.com/stopwords-iso/stopwords-sl
# Removed various words that are not normally considered stop words, such as months.
STOP_WORDS = set(
"""
a
ali
b
bi
bil
bila
bile
bili
bilo
biti
blizu
bo
bodo
bolj
bom
bomo
boste
bova
boš
brez
c
cel
cela
celi
celo
d
da
daleč
dan
danes
do
dober
dobra
dobri
dobro
dokler
dol
dovolj
e
eden
en
ena
ene
eni
enkrat
eno
etc.
a ali
b bi bil bila bile bili bilo biti blizu bo bodo bojo bolj bom bomo
boste bova boš brez
c cel cela celi celo
č če često četrta četrtek četrti četrto čez čigav
d da daleč dan danes datum deset deseta deseti deseto devet
deveta deveti deveto do dober dobra dobri dobro dokler dol dolg
dolga dolgi dovolj drug druga drugi drugo dva dve
e eden en ena ene eni enkrat eno etc.
f
g
g.
ga
ga.
gor
gospa
gospod
h
halo
i
idr.
ii
iii
in
iv
ix
iz
j
jaz
je
ji
jih
jim
jo
k
kadarkoli
kaj
kajti
kako
kakor
kamor
kamorkoli
kar
karkoli
katerikoli
kdaj
kdo
kdorkoli
ker
ki
kje
kjer
kjerkoli
ko
koderkoli
koga
komu
kot
l
le
lep
lepa
lepe
lepi
lepo
m
manj
me
med
medtem
mene
mi
midva
midve
mnogo
moj
moja
moje
mora
morajo
moram
moramo
morate
moraš
morem
mu
n
na
nad
naj
najina
najino
najmanj
naju
največ
nam
nas
nato
nazaj
naš
naša
naše
ne
nedavno
nek
neka
nekaj
nekatere
nekateri
nekatero
nekdo
neke
nekega
neki
nekje
neko
nekoga
nekoč
ni
nikamor
nikdar
nikjer
nikoli
nič
nje
njega
njegov
njegova
njegovo
njej
njemu
njen
njena
njeno
nji
njih
njihov
njihova
njihovo
njiju
njim
njo
njun
njuna
njuno
no
nocoj
npr.
o
ob
oba
obe
oboje
od
okoli
on
onadva
one
oni
onidve
oz.
p
pa
po
pod
pogosto
poleg
ponavadi
ponovno
potem
povsod
prbl.
precej
pred
prej
preko
pri
pribl.
približno
proti
r
redko
res
s
saj
sam
sama
same
sami
samo
se
sebe
sebi
sedaj
sem
seveda
si
sicer
skoraj
skozi
smo
so
spet
sta
ste
sva
t
ta
tak
taka
take
taki
tako
takoj
tam
te
tebe
tebi
tega
ti
tista
tiste
tisti
tisto
tj.
tja
to
toda
tu
tudi
tukaj
tvoj
tvoja
tvoje
g g. ga ga. gor gospa gospod
h halo
i idr. ii iii in iv ix iz
j jaz je ji jih jim jo jutri
k kadarkoli kaj kajti kako kakor kamor kamorkoli kar karkoli
katerikoli kdaj kdo kdorkoli ker ki kje kjer kjerkoli
ko koder koderkoli koga komu kot kratek kratka kratke kratki
l lahka lahke lahki lahko le lep lepa lepe lepi lepo leto
m majhen majhna majhni malce malo manj me med medtem mene
mesec mi midva midve mnogo moj moja moje mora morajo moram
moramo morate moraš morem mu
n na nad naj najina najino najmanj naju največ nam narobe
nas nato nazaj naš naša naše ne nedavno nedelja nek neka
nekaj nekatere nekateri nekatero nekdo neke nekega neki
nekje neko nekoga nekoč ni nikamor nikdar nikjer nikoli
nič nje njega njegov njegova njegovo njej njemu njen
njena njeno nji njih njihov njihova njihovo njiju njim
njo njun njuna njuno no nocoj npr.
o ob oba obe oboje od odprt odprta odprti okoli on
onadva one oni onidve osem osma osmi osmo oz.
p pa pet peta petek peti peto po pod pogosto poleg poln
polna polni polno ponavadi ponedeljek ponovno potem
povsod pozdravljen pozdravljeni prav prava prave pravi
pravo prazen prazna prazno prbl. precej pred prej preko
pri pribl. približno primer pripravljen pripravljena
pripravljeni proti prva prvi prvo
r ravno redko res reč
s saj sam sama same sami samo se sebe sebi sedaj sedem
sedma sedmi sedmo sem seveda si sicer skoraj skozi slab sm
so sobota spet sreda srednja srednji sta ste stran stvar sva
š šest šesta šesti šesto štiri
t ta tak taka take taki tako takoj tam te tebe tebi tega
težak težka težki težko ti tista tiste tisti tisto tj.
tja to toda torek tretja tretje tretji tri tu tudi tukaj
tvoj tvoja tvoje
u
v
vaju
vam
vas
vaš
vaša
vaše
ve
vedno
vendar
ves
več
vi
vidva
vii
viii
vsa
vsaj
vsak
vsaka
vsakdo
vsake
vsaki
vsakomur
vse
vsega
vsi
vso
včasih
v vaju vam vas vaš vaša vaše ve vedno velik velika veliki
veliko vendar ves več vi vidva vii viii visok visoka visoke
visoki vsa vsaj vsak vsaka vsakdo vsake vsaki vsakomur vse
vsega vsi vso včasih včeraj
x
z
za
zadaj
zadnji
zakaj
zdaj
zelo
zunaj
č
če
često
čez
čigav
š
ž
že
z za zadaj zadnji zakaj zaprta zaprti zaprto zdaj zelo zunaj
ž že
""".split()
)

272
spacy/lang/sl/tokenizer_exceptions.py Normal file
View File

@ -0,0 +1,272 @@
from typing import Dict, List
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ...symbols import ORTH, NORM
from ...util import update_exc
_exc: Dict[str, List[Dict]] = {}
_other_exc = {
"t.i.": [{ORTH: "t.", NORM: "tako"}, {ORTH: "i.", NORM: "imenovano"}],
"t.j.": [{ORTH: "t.", NORM: "to"}, {ORTH: "j.", NORM: "je"}],
"T.j.": [{ORTH: "T.", NORM: "to"}, {ORTH: "j.", NORM: "je"}],
"d.o.o.": [
{ORTH: "d.", NORM: "družba"},
{ORTH: "o.", NORM: "omejeno"},
{ORTH: "o.", NORM: "odgovornostjo"},
],
"D.O.O.": [
{ORTH: "D.", NORM: "družba"},
{ORTH: "O.", NORM: "omejeno"},
{ORTH: "O.", NORM: "odgovornostjo"},
],
"d.n.o.": [
{ORTH: "d.", NORM: "družba"},
{ORTH: "n.", NORM: "neomejeno"},
{ORTH: "o.", NORM: "odgovornostjo"},
],
"D.N.O.": [
{ORTH: "D.", NORM: "družba"},
{ORTH: "N.", NORM: "neomejeno"},
{ORTH: "O.", NORM: "odgovornostjo"},
],
"d.d.": [{ORTH: "d.", NORM: "delniška"}, {ORTH: "d.", NORM: "družba"}],
"D.D.": [{ORTH: "D.", NORM: "delniška"}, {ORTH: "D.", NORM: "družba"}],
"s.p.": [{ORTH: "s.", NORM: "samostojni"}, {ORTH: "p.", NORM: "podjetnik"}],
"S.P.": [{ORTH: "S.", NORM: "samostojni"}, {ORTH: "P.", NORM: "podjetnik"}],
"l.r.": [{ORTH: "l.", NORM: "lastno"}, {ORTH: "r.", NORM: "ročno"}],
"le-te": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "te"}],
"Le-te": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "te"}],
"le-ti": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "ti"}],
"Le-ti": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "ti"}],
"le-to": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "to"}],
"Le-to": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "to"}],
"le-ta": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "ta"}],
"Le-ta": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "ta"}],
"le-tega": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "tega"}],
"Le-tega": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "tega"}],
}
_exc.update(_other_exc)
for exc_data in [
{ORTH: "adm.", NORM: "administracija"},
{ORTH: "aer.", NORM: "aeronavtika"},
{ORTH: "agr.", NORM: "agronomija"},
{ORTH: "amer.", NORM: "ameriško"},
{ORTH: "anat.", NORM: "anatomija"},
{ORTH: "angl.", NORM: "angleški"},
{ORTH: "ant.", NORM: "antonim"},
{ORTH: "antr.", NORM: "antropologija"},
{ORTH: "apr.", NORM: "april"},
{ORTH: "arab.", NORM: "arabsko"},
{ORTH: "arheol.", NORM: "arheologija"},
{ORTH: "arhit.", NORM: "arhitektura"},
{ORTH: "avg.", NORM: "avgust"},
{ORTH: "avstr.", NORM: "avstrijsko"},
{ORTH: "avt.", NORM: "avtomobilizem"},
{ORTH: "bibl.", NORM: "biblijsko"},
{ORTH: "biokem.", NORM: "biokemija"},
{ORTH: "biol.", NORM: "biologija"},
{ORTH: "bolg.", NORM: "bolgarski"},
{ORTH: "bot.", NORM: "botanika"},
{ORTH: "cit.", NORM: "citat"},
{ORTH: "daj.", NORM: "dajalnik"},
{ORTH: "del.", NORM: "deležnik"},
{ORTH: "ed.", NORM: "ednina"},
{ORTH: "etn.", NORM: "etnografija"},
{ORTH: "farm.", NORM: "farmacija"},
{ORTH: "filat.", NORM: "filatelija"},
{ORTH: "filoz.", NORM: "filozofija"},
{ORTH: "fin.", NORM: "finančništvo"},
{ORTH: "fiz.", NORM: "fizika"},
{ORTH: "fot.", NORM: "fotografija"},
{ORTH: "fr.", NORM: "francoski"},
{ORTH: "friz.", NORM: "frizerstvo"},
{ORTH: "gastr.", NORM: "gastronomija"},
{ORTH: "geogr.", NORM: "geografija"},
{ORTH: "geol.", NORM: "geologija"},
{ORTH: "geom.", NORM: "geometrija"},
{ORTH: "germ.", NORM: "germanski"},
{ORTH: "gl.", NORM: "glej"},
{ORTH: "glag.", NORM: "glagolski"},
{ORTH: "glasb.", NORM: "glasba"},
{ORTH: "gled.", NORM: "gledališče"},
{ORTH: "gost.", NORM: "gostinstvo"},
{ORTH: "gozd.", NORM: "gozdarstvo"},
{ORTH: "gr.", NORM: "grški"},
{ORTH: "grad.", NORM: "gradbeništvo"},
{ORTH: "hebr.", NORM: "hebrejsko"},
{ORTH: "hrv.", NORM: "hrvaško"},
{ORTH: "ide.", NORM: "indoevropsko"},
{ORTH: "igr.", NORM: "igre"},
{ORTH: "im.", NORM: "imenovalnik"},
{ORTH: "iron.", NORM: "ironično"},
{ORTH: "it.", NORM: "italijanski"},
{ORTH: "itd.", NORM: "in tako dalje"},
{ORTH: "itn.", NORM: "in tako naprej"},
{ORTH: "ipd.", NORM: "in podobno"},
{ORTH: "jap.", NORM: "japonsko"},
{ORTH: "jul.", NORM: "julij"},
{ORTH: "jun.", NORM: "junij"},
{ORTH: "kit.", NORM: "kitajsko"},
{ORTH: "knj.", NORM: "knjižno"},
{ORTH: "knjiž.", NORM: "knjižno"},
{ORTH: "kor.", NORM: "koreografija"},
{ORTH: "lat.", NORM: "latinski"},
{ORTH: "les.", NORM: "lesna stroka"},
{ORTH: "lingv.", NORM: "lingvistika"},
{ORTH: "lit.", NORM: "literarni"},
{ORTH: "ljubk.", NORM: "ljubkovalno"},
{ORTH: "lov.", NORM: "lovstvo"},
{ORTH: "m.", NORM: "moški"},
{ORTH: "mak.", NORM: "makedonski"},
{ORTH: "mar.", NORM: "marec"},
{ORTH: "mat.", NORM: "matematika"},
{ORTH: "med.", NORM: "medicina"},
{ORTH: "meh.", NORM: "mehiško"},
{ORTH: "mest.", NORM: "mestnik"},
{ORTH: "mdr.", NORM: "med drugim"},
{ORTH: "min.", NORM: "mineralogija"},
{ORTH: "mitol.", NORM: "mitologija"},
{ORTH: "mn.", NORM: "množina"},
{ORTH: "mont.", NORM: "montanistika"},
{ORTH: "muz.", NORM: "muzikologija"},
{ORTH: "nam.", NORM: "namenilnik"},
{ORTH: "nar.", NORM: "narečno"},
{ORTH: "nav.", NORM: "navadno"},
{ORTH: "nedol.", NORM: "nedoločnik"},
{ORTH: "nedov.", NORM: "nedovršni"},
{ORTH: "neprav.", NORM: "nepravilno"},
{ORTH: "nepreh.", NORM: "neprehodno"},
{ORTH: "neskl.", NORM: "nesklonljiv(o)"},
{ORTH: "nestrok.", NORM: "nestrokovno"},
{ORTH: "num.", NORM: "numizmatika"},
{ORTH: "npr.", NORM: "na primer"},
{ORTH: "obrt.", NORM: "obrtništvo"},
{ORTH: "okt.", NORM: "oktober"},
{ORTH: "or.", NORM: "orodnik"},
{ORTH: "os.", NORM: "oseba"},
{ORTH: "otr.", NORM: "otroško"},
{ORTH: "oz.", NORM: "oziroma"},
{ORTH: "pal.", NORM: "paleontologija"},
{ORTH: "papir.", NORM: "papirništvo"},
{ORTH: "ped.", NORM: "pedagogika"},
{ORTH: "pisar.", NORM: "pisarniško"},
{ORTH: "pog.", NORM: "pogovorno"},
{ORTH: "polit.", NORM: "politika"},
{ORTH: "polj.", NORM: "poljsko"},
{ORTH: "poljud.", NORM: "poljudno"},
{ORTH: "preg.", NORM: "pregovor"},
{ORTH: "preh.", NORM: "prehodno"},
{ORTH: "pren.", NORM: "preneseno"},
{ORTH: "prid.", NORM: "pridevnik"},
{ORTH: "prim.", NORM: "primerjaj"},
{ORTH: "prisl.", NORM: "prislov"},
{ORTH: "psih.", NORM: "psihologija"},
{ORTH: "psiht.", NORM: "psihiatrija"},
{ORTH: "rad.", NORM: "radiotehnika"},
{ORTH: "rač.", NORM: "računalništvo"},
{ORTH: "rib.", NORM: "ribištvo"},
{ORTH: "rod.", NORM: "rodilnik"},
{ORTH: "rus.", NORM: "rusko"},
{ORTH: "s.", NORM: "srednji"},
{ORTH: "sam.", NORM: "samostalniški"},
{ORTH: "sed.", NORM: "sedanjik"},
{ORTH: "sep.", NORM: "september"},
{ORTH: "slabš.", NORM: "slabšalno"},
{ORTH: "slovan.", NORM: "slovansko"},
{ORTH: "slovaš.", NORM: "slovaško"},
{ORTH: "srb.", NORM: "srbsko"},
{ORTH: "star.", NORM: "starinsko"},
{ORTH: "stil.", NORM: "stilno"},
{ORTH: "sv.", NORM: "svet(i)"},
{ORTH: "teh.", NORM: "tehnika"},
{ORTH: "tisk.", NORM: "tiskarstvo"},
{ORTH: "tj.", NORM: "to je"},
{ORTH: "tož.", NORM: "tožilnik"},
{ORTH: "trg.", NORM: "trgovina"},
{ORTH: "ukr.", NORM: "ukrajinski"},
{ORTH: "um.", NORM: "umetnost"},
{ORTH: "vel.", NORM: "velelnik"},
{ORTH: "vet.", NORM: "veterina"},
{ORTH: "vez.", NORM: "veznik"},
{ORTH: "vn.", NORM: "visokonemško"},
{ORTH: "voj.", NORM: "vojska"},
{ORTH: "vrtn.", NORM: "vrtnarstvo"},
{ORTH: "vulg.", NORM: "vulgarno"},
{ORTH: "vznes.", NORM: "vzneseno"},
{ORTH: "zal.", NORM: "založništvo"},
{ORTH: "zastar.", NORM: "zastarelo"},
{ORTH: "zgod.", NORM: "zgodovina"},
{ORTH: "zool.", NORM: "zoologija"},
{ORTH: "čeb.", NORM: "čebelarstvo"},
{ORTH: "češ.", NORM: "češki"},
{ORTH: "člov.", NORM: "človeškost"},
{ORTH: "šah.", NORM: "šahovski"},
{ORTH: "šalj.", NORM: "šaljivo"},
{ORTH: "šp.", NORM: "španski"},
{ORTH: "špan.", NORM: "špansko"},
{ORTH: "šport.", NORM: "športni"},
{ORTH: "štev.", NORM: "števnik"},
{ORTH: "šved.", NORM: "švedsko"},
{ORTH: "švic.", NORM: "švicarsko"},
{ORTH: "ž.", NORM: "ženski"},
{ORTH: "žarg.", NORM: "žargonsko"},
{ORTH: "žel.", NORM: "železnica"},
{ORTH: "živ.", NORM: "živost"},
]:
_exc[exc_data[ORTH]] = [exc_data]
abbrv = """
Co. Ch. DIPL. DR. Dr. Ev. Inc. Jr. Kr. Mag. M. MR. Mr. Mt. Murr. Npr. OZ.
Opr. Osn. Prim. Roj. ST. Sim. Sp. Sred. St. Sv. Škofl. Tel. UR. Zb.
a. aa. ab. abc. abit. abl. abs. abt. acc. accel. add. adj. adv. aet. afr. akad. al. alban. all. alleg.
alp. alt. alter. alžir. am. an. andr. ang. anh. anon. ans. antrop. apoc. app. approx. apt. ar. arc. arch.
arh. arr. as. asist. assist. assoc. asst. astr. attn. aug. avstral. az. b. bab. bal. bbl. bd. belg. bioinf.
biomed. bk. bl. bn. borg. bp. br. braz. brit. bros. broš. bt. bu. c. ca. cal. can. cand. cantab. cap. capt.
cat. cath. cc. cca. cd. cdr. cdre. cent. cerkv. cert. cf. cfr. ch. chap. chem. chr. chs. cic. circ. civ. cl.
cm. cmd. cnr. co. cod. col. coll. colo. com. comp. con. conc. cond. conn. cons. cont. coop. corr. cost. cp.
cpl. cr. crd. cres. cresc. ct. cu. d. dan. dat. davč. ddr. dec. ded. def. dem. dent. dept. dia. dip. dipl.
dir. disp. diss. div. do. doc. dok. dol. doo. dop. dott. dr. dram. druž. družb. drž. dt. duh. dur. dvr. dwt. e.
ea. ecc. eccl. eccles. econ. edn. egipt. egr. ekon. eksp. el. em. enc. eng. eo. ep. err. esp. esq. est.
et. etc. etnogr. etnol. ev. evfem. evr. ex. exc. excl. exp. expl. ext. exx. f. fa. facs. fak. faks. fas.
fasc. fco. fcp. feb. febr. fec. fed. fem. ff. fff. fid. fig. fil. film. fiziol. fiziot. flam. fm. fo. fol. folk.
frag. fran. franc. fsc. g. ga. gal. gdč. ge. gen. geod. geog. geotehnol. gg. gimn. glas. glav. gnr. go. gor.
gosp. gp. graf. gram. gren. grš. gs. h. hab. hf. hist. ho. hort. i. ia. ib. ibid. id. idr. idridr. ill. imen.
imp. impf. impr. in. inc. incl. ind. indus. inf. inform. ing. init. ins. int. inv. inšp. inštr. inž. is. islam.
ist. ital. iur. iz. izbr. izd. izg. izgr. izr. izv. j. jak. jam. jan. jav. je. jez. jr. jsl. jud. jug.
jugoslovan. jur. juž. jv. jz. k. kal. kan. kand. kat. kdo. kem. kip. kmet. kol. kom. komp. konf. kont. kost. kov.
kp. kpfw. kr. kraj. krat. kub. kult. kv. kval. l. la. lab. lb. ld. let. lib. lik. litt. lj. ljud. ll. loc. log.
loč. lt. ma. madž. mag. manag. manjš. masc. mass. mater. max. maxmax. mb. md. mech. medic. medij. medn.
mehč. mem. menedž. mes. mess. metal. meteor. meteorol. mex. mi. mikr. mil. minn. mio. misc. miss. mit. mk.
mkt. ml. mlad. mlle. mlr. mm. mme. množ. mo. moj. moš. možn. mr. mrd. mrs. ms. msc. msgr. mt. murr. mus. mut.
n. na. nad. nadalj. nadom. nagl. nakl. namer. nan. naniz. nasl. nat. navt. nač. ned. nem. nik. nizoz. nm. nn.
no. nom. norv. notr. nov. novogr. ns. o. ob. obd. obj. oblač. obl. oblik. obr. obraz. obs. obst. obt. obč. oc.
oct. od. odd. odg. odn. odst. odv. oec. off. ok. okla. okr. ont. oo. op. opis. opp. opr. orch. ord. ore. oreg.
org. orient. orig. ork. ort. oseb. osn. ot. ozir. ošk. p. pag. par. para. parc. parl. part. past. pat. pdk.
pen. perf. pert. perz. pesn. pet. pev. pf. pfc. ph. pharm. phil. pis. pl. po. pod. podr. podaljš. pogl. pogoj. pojm.
pok. pokr. pol. poljed. poljub. polu. pom. pomen. pon. ponov. pop. por. port. pos. posl. posn. pov. pp. ppl. pr.
praet. prav. pravopis. pravosl. preb. pred. predl. predm. predp. preds. pref. pregib. prel. prem. premen. prep.
pres. pret. prev. pribl. prih. pril. primerj. primor. prip. pripor. prir. prist. priv. proc. prof. prog. proiz.
prom. pron. prop. prot. protest. prov. ps. pss. pt. publ. pz. q. qld. qu. quad. que. r. racc. rastl. razgl.
razl. razv. rd. red. ref. reg. rel. relig. rep. repr. rer. resp. rest. ret. rev. revol. rež. rim. rist. rkp. rm.
roj. rom. romun. rp. rr. rt. rud. ruš. ry. sal. samogl. san. sc. scen. sci. scr. sdv. seg. sek. sen. sept. ser.
sev. sg. sgt. sh. sig. sigg. sign. sim. sin. sing. sinh. skand. skl. sklad. sklanj. sklep. skr. sl. slik. slov.
slovak. slovn. sn. so. sob. soc. sociol. sod. sopomen. sopr. sor. sov. sovj. sp. spec. spl. spr. spreg. sq. sr.
sre. sred. sredoz. srh. ss. ssp. st. sta. stan. stanstar. stcsl. ste. stim. stol. stom. str. stroj. strok. stsl.
stud. sup. supl. suppl. svet. sz. t. tab. tech. ted. tehn. tehnol. tek. teks. tekst. tel. temp. ten. teol. ter.
term. test. th. theol. tim. tip. tisočl. tit. tl. tol. tolmač. tom. tor. tov. tr. trad. traj. trans. tren.
trib. tril. trop. trp. trž. ts. tt. tu. tur. turiz. tvor. tvorb. tč. u. ul. umet. un. univ. up. upr. ur. urad.
us. ust. utr. v. va. val. var. varn. ven. ver. verb. vest. vezal. vic. vis. viv. viz. viš. vod. vok. vol. vpr.
vrst. vrstil. vs. vv. vzd. vzg. vzh. vzor. w. wed. wg. wk. x. y. z. zah. zaim. zak. zap. zasl. zavar. zač. zb.
združ. zg. zn. znan. znanstv. zoot. zun. zv. zvd. á. é. ć. č. čas. čet. čl. člen. čustv. đ. ľ. ł. ş. ŠT. š. šir.
škofl. škot. šol. št. števil. štud. ů. ű. žen. žival.
""".split()
for orth in abbrv:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)

2
spacy/lang/uk/__init__.py
View File

@ -29,7 +29,7 @@ class Ukrainian(Language):
assigns=["token.lemma"],
default_config={
"model": None,
"mode": "pymorphy2",
"mode": "pymorphy3",
"overwrite": False,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},

15
spacy/lang/uk/lemmatizer.py
View File

@ -14,11 +14,11 @@ class UkrainianLemmatizer(RussianLemmatizer):
model: Optional[Model],
name: str = "lemmatizer",
*,
mode: str = "pymorphy2",
mode: str = "pymorphy3",
overwrite: bool = False,
scorer: Optional[Callable] = lemmatizer_score,
) -> None:
if mode == "pymorphy2":
if mode in {"pymorphy2", "pymorphy2_lookup"}:
try:
from pymorphy2 import MorphAnalyzer
except ImportError:
@ -29,6 +29,17 @@ class UkrainianLemmatizer(RussianLemmatizer):
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer(lang="uk")
elif mode in {"pymorphy3", "pymorphy3_lookup"}:
try:
from pymorphy3 import MorphAnalyzer
except ImportError:
raise ImportError(
"The Ukrainian lemmatizer mode 'pymorphy3' requires the "
"pymorphy3 library and dictionaries. Install them with: "
"pip install pymorphy3 pymorphy3-dicts-uk"
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer(lang="uk")
super().__init__(
vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
)

274
spacy/language.py
View File

@ -1,4 +1,4 @@
from typing import Iterator, Optional, Any, Dict, Callable, Iterable, Collection
from typing import Iterator, Optional, Any, Dict, Callable, Iterable, Literal
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
@ -21,10 +22,10 @@ from . import ty
from .tokens.underscore import Underscore
from .vocab import Vocab, create_vocab
from .pipe_analysis import validate_attrs, analyze_pipes, print_pipe_analysis
from .training import Example, validate_examples
from .training import Example, validate_examples, validate_distillation_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
@ -39,11 +40,9 @@ from .git_info import GIT_VERSION
from . import util
from . import about
from .lookups import load_lookups
from .compat import Literal
if TYPE_CHECKING:
from .pipeline import Pipe # noqa: F401
PipeCallable = Callable[[Doc], Doc]
# This is the base config will all settings (training etc.)
@ -180,7 +179,7 @@ class Language:
self.vocab: Vocab = vocab
if self.lang is None:
self.lang = self.vocab.lang
self._components: List[Tuple[str, "Pipe"]] = []
self._components: List[Tuple[str, PipeCallable]] = []
self._disabled: Set[str] = set()
self.max_length = max_length
# Create the default tokenizer from the default config
@ -302,7 +301,7 @@ class Language:
return SimpleFrozenList(names)
@property
def components(self) -> List[Tuple[str, "Pipe"]]:
def components(self) -> List[Tuple[str, PipeCallable]]:
"""Get all (name, component) tuples in the pipeline, including the
currently disabled components.
"""
@ -321,12 +320,12 @@ class Language:
return SimpleFrozenList(names, error=Errors.E926.format(attr="component_names"))
@property
def pipeline(self) -> List[Tuple[str, "Pipe"]]:
def pipeline(self) -> List[Tuple[str, PipeCallable]]:
"""The processing pipeline consisting of (name, component) tuples. The
components are called on the Doc in order as it passes through the
pipeline.
RETURNS (List[Tuple[str, Pipe]]): The pipeline.
RETURNS (List[Tuple[str, Callable[[Doc], Doc]]]): The pipeline.
"""
pipes = [(n, p) for n, p in self._components if n not in self._disabled]
return SimpleFrozenList(pipes, error=Errors.E926.format(attr="pipeline"))
@ -465,6 +464,8 @@ 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)
@ -524,7 +525,7 @@ class Language:
assigns: Iterable[str] = SimpleFrozenList(),
requires: Iterable[str] = SimpleFrozenList(),
retokenizes: bool = False,
func: Optional["Pipe"] = None,
func: Optional[PipeCallable] = None,
) -> Callable[..., Any]:
"""Register a new pipeline component. Can be used for stateless function
components that don't require a separate factory. Can be used as a
@ -539,19 +540,22 @@ class Language:
e.g. "token.ent_id". Used for pipeline analysis.
retokenizes (bool): Whether the component changes the tokenization.
Used for pipeline analysis.
func (Optional[Callable]): Factory function if not used as a decorator.
func (Optional[Callable[[Doc], Doc]): Factory function if not used as a decorator.
DOCS: https://spacy.io/api/language#component
"""
if name is not None and not isinstance(name, str):
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:
def add_component(component_func: PipeCallable) -> Callable:
if isinstance(func, type): # function is a class
raise ValueError(Errors.E965.format(name=component_name))
def factory_func(nlp, name: str) -> "Pipe":
def factory_func(nlp, name: str) -> PipeCallable:
return component_func
internal_name = cls.get_factory_name(name)
@ -601,7 +605,7 @@ class Language:
print_pipe_analysis(analysis, keys=keys)
return analysis
def get_pipe(self, name: str) -> "Pipe":
def get_pipe(self, name: str) -> PipeCallable:
"""Get a pipeline component for a given component name.
name (str): Name of pipeline component to get.
@ -622,7 +626,7 @@ class Language:
config: Dict[str, Any] = SimpleFrozenDict(),
raw_config: Optional[Config] = None,
validate: bool = True,
) -> "Pipe":
) -> PipeCallable:
"""Create a pipeline component. Mostly used internally. To create and
add a component to the pipeline, you can use nlp.add_pipe.
@ -634,7 +638,7 @@ class Language:
raw_config (Optional[Config]): Internals: the non-interpolated config.
validate (bool): Whether to validate the component config against the
arguments and types expected by the factory.
RETURNS (Pipe): The pipeline component.
RETURNS (Callable[[Doc], Doc]): The pipeline component.
DOCS: https://spacy.io/api/language#create_pipe
"""
@ -689,24 +693,18 @@ class Language:
def create_pipe_from_source(
self, source_name: str, source: "Language", *, name: str
) -> Tuple["Pipe", str]:
) -> Tuple[PipeCallable, str]:
"""Create a pipeline component by copying it from an existing model.
source_name (str): Name of the component in the source pipeline.
source (Language): The source nlp object to copy from.
name (str): Optional alternative name to use in current pipeline.
RETURNS (Tuple[Callable, str]): The component and its factory name.
RETURNS (Tuple[Callable[[Doc], Doc], str]): The component and its factory name.
"""
# Check source type
if not isinstance(source, Language):
raise ValueError(Errors.E945.format(name=source_name, source=type(source)))
# Check vectors, with faster checks first
if (
self.vocab.vectors.shape != source.vocab.vectors.shape
or self.vocab.vectors.key2row != source.vocab.vectors.key2row
or self.vocab.vectors.to_bytes(exclude=["strings"])
!= source.vocab.vectors.to_bytes(exclude=["strings"])
):
if self.vocab.vectors != source.vocab.vectors:
warnings.warn(Warnings.W113.format(name=source_name))
if source_name not in source.component_names:
raise KeyError(
@ -740,7 +738,7 @@ class Language:
config: Dict[str, Any] = SimpleFrozenDict(),
raw_config: Optional[Config] = None,
validate: bool = True,
) -> "Pipe":
) -> PipeCallable:
"""Add a component to the processing pipeline. Valid components are
callables that take a `Doc` object, modify it and return it. Only one
of before/after/first/last can be set. Default behaviour is "last".
@ -763,7 +761,7 @@ class Language:
raw_config (Optional[Config]): Internals: the non-interpolated config.
validate (bool): Whether to validate the component config against the
arguments and types expected by the factory.
RETURNS (Pipe): The pipeline component.
RETURNS (Callable[[Doc], Doc]): The pipeline component.
DOCS: https://spacy.io/api/language#add_pipe
"""
@ -784,14 +782,6 @@ class Language:
factory_name, source, name=name
)
else:
if not self.has_factory(factory_name):
err = Errors.E002.format(
name=factory_name,
opts=", ".join(self.factory_names),
method="add_pipe",
lang=util.get_object_name(self),
lang_code=self.lang,
)
pipe_component = self.create_pipe(
factory_name,
name=name,
@ -877,7 +867,7 @@ class Language:
*,
config: Dict[str, Any] = SimpleFrozenDict(),
validate: bool = True,
) -> "Pipe":
) -> PipeCallable:
"""Replace a component in the pipeline.
name (str): Name of the component to replace.
@ -886,7 +876,7 @@ class Language:
component. Will be merged with default config, if available.
validate (bool): Whether to validate the component config against the
arguments and types expected by the factory.
RETURNS (Pipe): The new pipeline component.
RETURNS (Callable[[Doc], Doc]): The new pipeline component.
DOCS: https://spacy.io/api/language#replace_pipe
"""
@ -938,11 +928,11 @@ class Language:
init_cfg = self._config["initialize"]["components"].pop(old_name)
self._config["initialize"]["components"][new_name] = init_cfg
def remove_pipe(self, name: str) -> Tuple[str, "Pipe"]:
def remove_pipe(self, name: str) -> Tuple[str, PipeCallable]:
"""Remove a component from the pipeline.
name (str): Name of the component to remove.
RETURNS (tuple): A `(name, component)` tuple of the removed component.
RETURNS (Tuple[str, Callable[[Doc], Doc]]): A `(name, component)` tuple of the removed component.
DOCS: https://spacy.io/api/language#remove_pipe
"""
@ -1023,10 +1013,106 @@ 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 distill(
self,
teacher: "Language",
examples: Iterable[Example],
*,
drop: float = 0.0,
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None,
component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
exclude: Iterable[str] = SimpleFrozenList(),
annotates: Iterable[str] = SimpleFrozenList(),
student_to_teacher: Optional[Dict[str, str]] = None,
):
"""Distill the models in a student pipeline from a teacher pipeline.
teacher (Language): Teacher to distill from.
examples (Iterable[Example]): Distillation examples. The reference
(teacher) and predicted (student) docs must have the same number of
tokens and the same orthography.
drop (float): The dropout rate.
sgd (Optional[Optimizer]): An optimizer.
losses (Optional(Dict[str, float])): Dictionary to update with the loss,
keyed by component.
component_cfg (Optional[Dict[str, Dict[str, Any]]]): Config parameters
for specific pipeline components, keyed by component name.
exclude (Iterable[str]): Names of components that shouldn't be updated.
annotates (Iterable[str]): Names of components that should set
annotations on the predicted examples after updating.
student_to_teacher (Optional[Dict[str, str]]): Map student pipe name to
teacher pipe name, only needed for pipes where the student pipe
name does not match the teacher pipe name.
RETURNS (Dict[str, float]): The updated losses dictionary
DOCS: https://spacy.io/api/language#distill
"""
if student_to_teacher is None:
student_to_teacher = {}
if losses is None:
losses = {}
if isinstance(examples, list) and len(examples) == 0:
return losses
validate_distillation_examples(examples, "Language.distill")
examples = _copy_examples(examples)
if sgd is None:
if self._optimizer is None:
self._optimizer = self.create_optimizer()
sgd = self._optimizer
if component_cfg is None:
component_cfg = {}
pipe_kwargs = {}
for student_name, student_proc in self.pipeline:
component_cfg.setdefault(student_name, {})
pipe_kwargs[student_name] = deepcopy(component_cfg[student_name])
component_cfg[student_name].setdefault("drop", drop)
pipe_kwargs[student_name].setdefault("batch_size", self.batch_size)
teacher_pipes = dict(teacher.pipeline)
for student_name, student_proc in self.pipeline:
if student_name in annotates:
for doc, eg in zip(
_pipe(
(eg.predicted for eg in examples),
proc=student_proc,
name=student_name,
default_error_handler=self.default_error_handler,
kwargs=pipe_kwargs[student_name],
),
examples,
):
eg.predicted = doc
if (
student_name not in exclude
and isinstance(student_proc, ty.DistillableComponent)
and student_proc.is_distillable
):
# A missing teacher pipe is not an error, some student pipes
# do not need a teacher, such as tok2vec layer losses.
teacher_name = (
student_to_teacher[student_name]
if student_name in student_to_teacher
else student_name
)
teacher_pipe = teacher_pipes.get(teacher_name, None)
student_proc.distill(
teacher_pipe,
examples,
sgd=sgd,
losses=losses,
**component_cfg[student_name],
)
return losses
def disable_pipes(self, *names) -> "DisabledPipes":
"""Disable one or more pipeline components. If used as a context
manager, the pipeline will be restored to the initial state at the end
@ -1058,7 +1144,7 @@ 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):
@ -1248,25 +1334,20 @@ class Language:
sgd(key, W, dW) # type: ignore[call-arg, misc]
return losses
def begin_training(
self,
get_examples: Optional[Callable[[], Iterable[Example]]] = None,
*,
sgd: Optional[Optimizer] = None,
) -> Optimizer:
warnings.warn(Warnings.W089, DeprecationWarning)
return self.initialize(get_examples, sgd=sgd)
def initialize(
self,
get_examples: Optional[Callable[[], Iterable[Example]]] = None,
*,
labels: Optional[Dict[str, Any]] = None,
sgd: Optional[Optimizer] = None,
) -> Optimizer:
"""Initialize the pipe for training, using data examples if available.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
labels (Optional[Dict[str, Any]]): Labels to pass to pipe initialization,
using the names of the pipes as keys. Overrides labels that are in
the model configuration.
sgd (Optional[Optimizer]): An optimizer to use for updates. If not
provided, will be created using the .create_optimizer() method.
RETURNS (thinc.api.Optimizer): The optimizer.
@ -1311,6 +1392,8 @@ class Language:
for name, proc in self.pipeline:
if isinstance(proc, ty.InitializableComponent):
p_settings = I["components"].get(name, {})
if labels is not None and name in labels:
p_settings["labels"] = labels[name]
p_settings = validate_init_settings(
proc.initialize, p_settings, section="components", name=name
)
@ -1357,15 +1440,15 @@ class Language:
def set_error_handler(
self,
error_handler: Callable[[str, "Pipe", List[Doc], Exception], NoReturn],
error_handler: Callable[[str, PipeCallable, List[Doc], Exception], NoReturn],
):
"""Set an error handler object for all the components in the pipeline that implement
a set_error_handler function.
"""Set an error handler object for all the components in the pipeline
that implement a set_error_handler function.
error_handler (Callable[[str, Pipe, List[Doc], Exception], NoReturn]):
Function that deals with a failing batch of documents. This callable function should take in
the component's name, the component itself, the offending batch of documents, and the exception
that was thrown.
error_handler (Callable[[str, Callable[[Doc], Doc], List[Doc], Exception], NoReturn]):
Function that deals with a failing batch of documents. This callable
function should take in the component's name, the component itself,
the offending batch of documents, and the exception that was thrown.
DOCS: https://spacy.io/api/language#set_error_handler
"""
self.default_error_handler = error_handler
@ -1670,9 +1753,9 @@ 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,
@ -1683,12 +1766,12 @@ 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
@ -1721,6 +1804,7 @@ class Language:
# using the nlp.config with all defaults.
config = util.copy_config(config)
orig_pipeline = config.pop("components", {})
orig_distill = config.pop("distill", None)
orig_pretraining = config.pop("pretraining", None)
config["components"] = {}
if auto_fill:
@ -1729,6 +1813,9 @@ class Language:
filled = config
filled["components"] = orig_pipeline
config["components"] = orig_pipeline
if orig_distill is not None:
filled["distill"] = orig_distill
config["distill"] = orig_distill
if orig_pretraining is not None:
filled["pretraining"] = orig_pretraining
config["pretraining"] = orig_pretraining
@ -1843,9 +1930,29 @@ 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]
# `enable` should not be merged with `enabled` (the opposite is true for `disable`/`disabled`). If the config
# specifies values for `enabled` not included in `enable`, emit warning.
if id(enable) != id(_DEFAULT_EMPTY_PIPES):
enabled = config["nlp"].get("enabled", [])
if len(enabled) and not set(enabled).issubset(enable):
warnings.warn(
Warnings.W123.format(
enable=enable,
enabled=enabled,
)
)
# Ensure sets of disabled/enabled pipe names are not contradictory.
disabled_pipes = cls._resolve_component_status(
[*config["nlp"]["disabled"], *disable],
[*config["nlp"].get("enabled", []), *enable],
list({*disable, *config["nlp"].get("disabled", [])}),
enable,
config["nlp"]["pipeline"],
)
nlp._disabled = set(p for p in disabled_pipes if p not in exclude)
@ -2003,37 +2110,36 @@ 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:
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,
)
)
if isinstance(enable, str):
enable = [enable]
to_disable = {
*[pipe_name for pipe_name in pipe_names if pipe_name not in enable],
*disable,
}
# If any pipe to be enabled is in to_disable, the specification is inconsistent.
if len(set(enable) & to_disable):
raise ValueError(Errors.E1042.format(enable=enable, disable=disable))
return tuple(to_disable)

1
spacy/lexeme.pxd
View File

@ -5,7 +5,6 @@ from .attrs cimport attr_id_t
from .attrs cimport ID, ORTH, LOWER, NORM, SHAPE, PREFIX, SUFFIX, LENGTH, LANG
from .structs cimport LexemeC
from .strings cimport StringStore
from .vocab cimport Vocab

1
spacy/lexeme.pyi
View File

@ -20,7 +20,6 @@ class Lexeme:
def vector_norm(self) -> float: ...
vector: Floats1d
rank: int
sentiment: float
@property
def orth_(self) -> str: ...
@property

15
spacy/lexeme.pyx
View File

@ -41,7 +41,7 @@ cdef class Lexeme:
"""
self.vocab = vocab
self.orth = orth
self.c = <LexemeC*><void*>vocab.get_by_orth(vocab.mem, orth)
self.c = <LexemeC*><void*>vocab.get_by_orth(orth)
if self.c.orth != orth:
raise ValueError(Errors.E071.format(orth=orth, vocab_orth=self.c.orth))
@ -173,19 +173,6 @@ cdef class Lexeme:
def __set__(self, value):
self.c.id = value
property sentiment:
"""RETURNS (float): A scalar value indicating the positivity or
negativity of the lexeme."""
def __get__(self):
sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment", {})
return sentiment_table.get(self.c.orth, 0.0)
def __set__(self, float x):
if "lexeme_sentiment" not in self.vocab.lookups:
self.vocab.lookups.add_table("lexeme_sentiment")
sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment")
sentiment_table[self.c.orth] = x
@property
def orth_(self):
"""RETURNS (str): The original verbatim text of the lexeme

3
spacy/matcher/__init__.py
View File

@ -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"]

6
spacy/matcher/dependencymatcher.pyx
View File

@ -165,9 +165,9 @@ cdef class DependencyMatcher:
on_match (callable): Optional callback executed on match.
"""
if on_match is not None and not hasattr(on_match, "__call__"):
raise ValueError(Errors.E171.format(arg_type=type(on_match)))
if patterns is None or not isinstance(patterns, List): # old API
raise ValueError(Errors.E948.format(arg_type=type(patterns)))
raise ValueError(Errors.E171.format(name="DependencyMatcher", arg_type=type(on_match)))
if patterns is None or not isinstance(patterns, List):
raise ValueError(Errors.E948.format(name="DependencyMatcher", arg_type=type(patterns)))
for pattern in patterns:
if len(pattern) == 0:
raise ValueError(Errors.E012.format(key=key))

32
spacy/matcher/levenshtein.pyx Normal file
View File

@ -0,0 +1,32 @@
# cython: profile=True, binding=True, infer_types=True
from cpython.object cimport PyObject
from libc.stdint cimport int64_t
from typing import Optional
from ..util import registry
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)
cpdef bint levenshtein_compare(input_text: str, pattern_text: str, fuzzy: int = -1):
if fuzzy >= 0:
max_edits = fuzzy
else:
# allow at least two edits (to allow at least one transposition) and up
# to 30% of the pattern string length
max_edits = max(2, round(0.3 * len(pattern_text)))
return levenshtein(input_text, pattern_text, max_edits) <= max_edits
@registry.misc("spacy.levenshtein_compare.v1")
def make_levenshtein_compare():
return levenshtein_compare

1
spacy/matcher/matcher.pxd
View File

@ -77,3 +77,4 @@ cdef class Matcher:
cdef public object _extensions
cdef public object _extra_predicates
cdef public object _seen_attrs
cdef public object _fuzzy_compare

10
spacy/matcher/matcher.pyi
View File

@ -1,11 +1,15 @@
from typing import Any, List, Dict, Tuple, Optional, Callable, Union
from typing import Any, List, Dict, Tuple, Optional, Callable, Union, Literal
from typing import Iterator, Iterable, overload
from ..compat import Literal
from ..vocab import Vocab
from ..tokens import Doc, Span
class Matcher:
def __init__(self, vocab: Vocab, validate: bool = ...) -> None: ...
def __init__(
self,
vocab: Vocab,
validate: bool = ...,
fuzzy_compare: Callable[[str, str, int], bool] = ...,
) -> None: ...
def __reduce__(self) -> Any: ...
def __len__(self) -> int: ...
def __contains__(self, key: str) -> bool: ...

185
spacy/matcher/matcher.pyx
View File

@ -1,5 +1,5 @@
# cython: infer_types=True, cython: profile=True
from typing import List
# cython: binding=True, infer_types=True, profile=True
from typing import List, Iterable
from libcpp.vector cimport vector
from libc.stdint cimport int32_t, int8_t
@ -20,10 +20,12 @@ from ..tokens.token cimport Token
from ..tokens.morphanalysis cimport MorphAnalysis
from ..attrs cimport ID, attr_id_t, NULL_ATTR, ORTH, POS, TAG, DEP, LEMMA, MORPH, ENT_IOB
from .levenshtein import levenshtein_compare
from ..schemas import validate_token_pattern
from ..errors import Errors, MatchPatternError, Warnings
from ..strings import get_string_id
from ..strings cimport get_string_id
from ..attrs import IDS
from ..util import registry
DEF PADDING = 5
@ -36,11 +38,13 @@ cdef class Matcher:
USAGE: https://spacy.io/usage/rule-based-matching
"""
def __init__(self, vocab, validate=True):
def __init__(self, vocab, validate=True, *, fuzzy_compare=levenshtein_compare):
"""Create the Matcher.
vocab (Vocab): The vocabulary object, which must be shared with the
documents the matcher will operate on.
validate (bool): Validate all patterns added to this matcher.
fuzzy_compare (Callable[[str, str, int], bool]): The comparison method
for the FUZZY operators.
"""
self._extra_predicates = []
self._patterns = {}
@ -51,9 +55,10 @@ cdef class Matcher:
self.vocab = vocab
self.mem = Pool()
self.validate = validate
self._fuzzy_compare = fuzzy_compare
def __reduce__(self):
data = (self.vocab, self._patterns, self._callbacks)
data = (self.vocab, self._patterns, self._callbacks, self.validate, self._fuzzy_compare)
return (unpickle_matcher, data, None, None)
def __len__(self):
@ -110,9 +115,9 @@ cdef class Matcher:
"""
errors = {}
if on_match is not None and not hasattr(on_match, "__call__"):
raise ValueError(Errors.E171.format(arg_type=type(on_match)))
if patterns is None or not isinstance(patterns, List): # old API
raise ValueError(Errors.E948.format(arg_type=type(patterns)))
raise ValueError(Errors.E171.format(name="Matcher", arg_type=type(on_match)))
if patterns is None or not isinstance(patterns, List):
raise ValueError(Errors.E948.format(name="Matcher", arg_type=type(patterns)))
if greedy is not None and greedy not in ["FIRST", "LONGEST"]:
raise ValueError(Errors.E947.format(expected=["FIRST", "LONGEST"], arg=greedy))
for i, pattern in enumerate(patterns):
@ -128,7 +133,7 @@ cdef class Matcher:
for pattern in patterns:
try:
specs = _preprocess_pattern(pattern, self.vocab,
self._extensions, self._extra_predicates)
self._extensions, self._extra_predicates, self._fuzzy_compare)
self.patterns.push_back(init_pattern(self.mem, key, specs))
for spec in specs:
for attr, _ in spec[1]:
@ -327,8 +332,8 @@ cdef class Matcher:
return key
def unpickle_matcher(vocab, patterns, callbacks):
matcher = Matcher(vocab)
def unpickle_matcher(vocab, patterns, callbacks, validate, fuzzy_compare):
matcher = Matcher(vocab, validate=validate, fuzzy_compare=fuzzy_compare)
for key, pattern in patterns.items():
callback = callbacks.get(key, None)
matcher.add(key, pattern, on_match=callback)
@ -755,7 +760,7 @@ cdef attr_t get_ent_id(const TokenPatternC* pattern) nogil:
return id_attr.value
def _preprocess_pattern(token_specs, vocab, extensions_table, extra_predicates):
def _preprocess_pattern(token_specs, vocab, extensions_table, extra_predicates, fuzzy_compare):
"""This function interprets the pattern, converting the various bits of
syntactic sugar before we compile it into a struct with init_pattern.
@ -782,7 +787,7 @@ def _preprocess_pattern(token_specs, vocab, extensions_table, extra_predicates):
ops = _get_operators(spec)
attr_values = _get_attr_values(spec, string_store)
extensions = _get_extensions(spec, string_store, extensions_table)
predicates = _get_extra_predicates(spec, extra_predicates, vocab)
predicates = _get_extra_predicates(spec, extra_predicates, vocab, fuzzy_compare)
for op in ops:
tokens.append((op, list(attr_values), list(extensions), list(predicates), token_idx))
return tokens
@ -827,16 +832,45 @@ def _get_attr_values(spec, string_store):
# These predicate helper classes are used to match the REGEX, IN, >= etc
# extensions to the matcher introduced in #3173.
class _FuzzyPredicate:
operators = ("FUZZY", "FUZZY1", "FUZZY2", "FUZZY3", "FUZZY4", "FUZZY5",
"FUZZY6", "FUZZY7", "FUZZY8", "FUZZY9")
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None,
regex=False, fuzzy=None, fuzzy_compare=None):
self.i = i
self.attr = attr
self.value = value
self.predicate = predicate
self.is_extension = is_extension
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
fuzz = self.predicate[len("FUZZY"):] # number after prefix
self.fuzzy = int(fuzz) if fuzz else -1
self.fuzzy_compare = fuzzy_compare
self.key = (self.attr, self.fuzzy, self.predicate, srsly.json_dumps(value, sort_keys=True))
def __call__(self, Token token):
if self.is_extension:
value = token._.get(self.attr)
else:
value = token.vocab.strings[get_token_attr_for_matcher(token.c, self.attr)]
if self.value == value:
return True
return self.fuzzy_compare(value, self.value, self.fuzzy)
class _RegexPredicate:
operators = ("REGEX",)
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None):
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None,
regex=False, fuzzy=None, fuzzy_compare=None):
self.i = i
self.attr = attr
self.value = re.compile(value)
self.predicate = predicate
self.is_extension = is_extension
self.key = (attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
self.key = (self.attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
@ -851,41 +885,78 @@ class _RegexPredicate:
class _SetPredicate:
operators = ("IN", "NOT_IN", "IS_SUBSET", "IS_SUPERSET", "INTERSECTS")
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None):
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None,
regex=False, fuzzy=None, fuzzy_compare=None):
self.i = i
self.attr = attr
self.vocab = vocab
self.regex = regex
self.fuzzy = fuzzy
self.fuzzy_compare = fuzzy_compare
if self.attr == MORPH:
# normalize morph strings
self.value = set(self.vocab.morphology.add(v) for v in value)
else:
if self.regex:
self.value = set(re.compile(v) for v in value)
elif self.fuzzy is not None:
# add to string store
self.value = set(self.vocab.strings.add(v) for v in value)
else:
self.value = set(get_string_id(v) for v in value)
self.predicate = predicate
self.is_extension = is_extension
self.key = (attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
self.key = (self.attr, self.regex, self.fuzzy, self.predicate, srsly.json_dumps(value, sort_keys=True))
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
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))
else:
# treat a single value as a list
if isinstance(value, (str, int)):
value = set([get_string_id(value)])
else:
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:
return False
if self.predicate == "IN":
return value in self.value
if self.regex:
value = self.vocab.strings[value]
return any(bool(v.search(value)) for v in self.value)
elif self.fuzzy is not None:
value = self.vocab.strings[value]
return any(self.fuzzy_compare(value, self.vocab.strings[v], self.fuzzy)
for v in self.value)
elif value in self.value:
return True
else:
return False
elif self.predicate == "NOT_IN":
return value not in self.value
if self.regex:
value = self.vocab.strings[value]
return not any(bool(v.search(value)) for v in self.value)
elif self.fuzzy is not None:
value = self.vocab.strings[value]
return not any(self.fuzzy_compare(value, self.vocab.strings[v], self.fuzzy)
for v in self.value)
elif value in self.value:
return False
else:
return True
elif self.predicate == "IS_SUBSET":
return value <= self.value
elif self.predicate == "IS_SUPERSET":
@ -900,13 +971,14 @@ class _SetPredicate:
class _ComparisonPredicate:
operators = ("==", "!=", ">=", "<=", ">", "<")
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None):
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None,
regex=False, fuzzy=None, fuzzy_compare=None):
self.i = i
self.attr = attr
self.value = value
self.predicate = predicate
self.is_extension = is_extension
self.key = (attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
self.key = (self.attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
@ -929,7 +1001,7 @@ class _ComparisonPredicate:
return value < self.value
def _get_extra_predicates(spec, extra_predicates, vocab):
def _get_extra_predicates(spec, extra_predicates, vocab, fuzzy_compare):
predicate_types = {
"REGEX": _RegexPredicate,
"IN": _SetPredicate,
@ -943,6 +1015,16 @@ def _get_extra_predicates(spec, extra_predicates, vocab):
"<=": _ComparisonPredicate,
">": _ComparisonPredicate,
"<": _ComparisonPredicate,
"FUZZY": _FuzzyPredicate,
"FUZZY1": _FuzzyPredicate,
"FUZZY2": _FuzzyPredicate,
"FUZZY3": _FuzzyPredicate,
"FUZZY4": _FuzzyPredicate,
"FUZZY5": _FuzzyPredicate,
"FUZZY6": _FuzzyPredicate,
"FUZZY7": _FuzzyPredicate,
"FUZZY8": _FuzzyPredicate,
"FUZZY9": _FuzzyPredicate,
}
seen_predicates = {pred.key: pred.i for pred in extra_predicates}
output = []
@ -960,12 +1042,40 @@ def _get_extra_predicates(spec, extra_predicates, vocab):
attr = "ORTH"
attr = IDS.get(attr.upper())
if isinstance(value, dict):
processed = False
value_with_upper_keys = {k.upper(): v for k, v in value.items()}
for type_, cls in predicate_types.items():
if type_ in value_with_upper_keys:
predicate = cls(len(extra_predicates), attr, value_with_upper_keys[type_], type_, vocab=vocab)
# Don't create a redundant predicates.
output.extend(_get_extra_predicates_dict(attr, value, vocab, predicate_types,
extra_predicates, seen_predicates, fuzzy_compare=fuzzy_compare))
return output
def _get_extra_predicates_dict(attr, value_dict, vocab, predicate_types,
extra_predicates, seen_predicates, regex=False, fuzzy=None, fuzzy_compare=None):
output = []
for type_, value in value_dict.items():
type_ = type_.upper()
cls = predicate_types.get(type_)
if cls is None:
warnings.warn(Warnings.W035.format(pattern=value_dict))
# ignore unrecognized predicate type
continue
elif cls == _RegexPredicate:
if isinstance(value, dict):
# add predicates inside regex operator
output.extend(_get_extra_predicates_dict(attr, value, vocab, predicate_types,
extra_predicates, seen_predicates,
regex=True))
continue
elif cls == _FuzzyPredicate:
if isinstance(value, dict):
# add predicates inside fuzzy operator
fuzz = type_[len("FUZZY"):] # number after prefix
fuzzy_val = int(fuzz) if fuzz else -1
output.extend(_get_extra_predicates_dict(attr, value, vocab, predicate_types,
extra_predicates, seen_predicates,
fuzzy=fuzzy_val, fuzzy_compare=fuzzy_compare))
continue
predicate = cls(len(extra_predicates), attr, value, type_, vocab=vocab,
regex=regex, fuzzy=fuzzy, fuzzy_compare=fuzzy_compare)
# Don't create redundant predicates.
# This helps with efficiency, as we're caching the results.
if predicate.key in seen_predicates:
output.append(seen_predicates[predicate.key])
@ -973,9 +1083,6 @@ def _get_extra_predicates(spec, extra_predicates, vocab):
extra_predicates.append(predicate)
output.append(predicate.i)
seen_predicates[predicate.key] = predicate.i
processed = True
if not processed:
warnings.warn(Warnings.W035.format(pattern=value))
return output

13
spacy/matcher/phrasematcher.pyi
View File

@ -1,5 +1,5 @@
from typing import List, Tuple, Union, Optional, Callable, Any, Dict, overload
from ..compat import Literal
from typing import List, Tuple, Union, Optional, Callable, Any, Dict, Literal
from typing import overload
from .matcher import Matcher
from ..vocab import Vocab
from ..tokens import Doc, Span
@ -20,6 +20,15 @@ class PhraseMatcher:
Callable[[Matcher, Doc, int, List[Tuple[Any, ...]]], Any]
] = ...,
) -> None: ...
def _add_from_arrays(
self,
key: str,
specs: List[List[int]],
*,
on_match: Optional[
Callable[[Matcher, Doc, int, List[Tuple[Any, ...]]], Any]
] = ...,
) -> None: ...
def remove(self, key: str) -> None: ...
@overload
def __call__(

103
spacy/matcher/phrasematcher.pyx
View File

@ -1,4 +1,6 @@
# cython: infer_types=True, profile=True
from typing import List
from collections import defaultdict
from libc.stdint cimport uintptr_t
from preshed.maps cimport map_init, map_set, map_get, map_clear, map_iter
@ -39,7 +41,7 @@ cdef class PhraseMatcher:
"""
self.vocab = vocab
self._callbacks = {}
self._docs = {}
self._docs = defaultdict(set)
self._validate = validate
self.mem = Pool()
@ -155,41 +157,71 @@ cdef class PhraseMatcher:
del self._callbacks[key]
del self._docs[key]
def add(self, key, docs, *_docs, on_match=None):
"""Add a match-rule to the phrase-matcher. A match-rule consists of: an ID
key, an on_match callback, and one or more patterns.
Since spaCy v2.2.2, PhraseMatcher.add takes a list of patterns as the
second argument, with the on_match callback as an optional keyword
argument.
def _add_from_arrays(self, key, specs, *, on_match=None):
"""Add a preprocessed list of specs, with an optional callback.
key (str): The match ID.
docs (list): List of `Doc` objects representing match patterns.
specs (List[List[int]]): A list of lists of hashes to match.
on_match (callable): Callback executed on match.
*_docs (Doc): For backwards compatibility: list of patterns to add
as variable arguments. Will be ignored if a list of patterns is
provided as the second argument.
DOCS: https://spacy.io/api/phrasematcher#add
"""
if docs is None or hasattr(docs, "__call__"): # old API
on_match = docs
docs = _docs
_ = self.vocab[key]
self._callbacks[key] = on_match
self._docs.setdefault(key, set())
cdef MapStruct* current_node
cdef MapStruct* internal_node
cdef void* result
self._callbacks[key] = on_match
for spec in specs:
self._docs[key].add(tuple(spec))
current_node = self.c_map
for token in spec:
if token == self._terminal_hash:
warnings.warn(Warnings.W021)
break
result = <MapStruct*>map_get(current_node, token)
if not result:
internal_node = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
map_init(self.mem, internal_node, 8)
map_set(self.mem, current_node, token, internal_node)
result = internal_node
current_node = <MapStruct*>result
result = <MapStruct*>map_get(current_node, self._terminal_hash)
if not result:
internal_node = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
map_init(self.mem, internal_node, 8)
map_set(self.mem, current_node, self._terminal_hash, internal_node)
result = internal_node
map_set(self.mem, <MapStruct*>result, self.vocab.strings[key], NULL)
def add(self, key, docs, *, on_match=None):
"""Add a match-rule to the phrase-matcher. A match-rule consists of: an ID
key, a list of one or more patterns, and (optionally) an on_match callback.
key (str): The match ID.
docs (list): List of `Doc` objects representing match patterns.
on_match (callable): Callback executed on match.
If any of the input Docs are invalid, no internal state will be updated.
DOCS: https://spacy.io/api/phrasematcher#add
"""
if isinstance(docs, Doc):
raise ValueError(Errors.E179.format(key=key))
if docs is None or not isinstance(docs, List):
raise ValueError(Errors.E948.format(name="PhraseMatcher", arg_type=type(docs)))
if on_match is not None and not hasattr(on_match, "__call__"):
raise ValueError(Errors.E171.format(name="PhraseMatcher", arg_type=type(on_match)))
_ = self.vocab[key]
specs = []
for doc in docs:
if len(doc) == 0:
continue
if isinstance(doc, Doc):
if not isinstance(doc, Doc):
raise ValueError(Errors.E4000.format(type=type(doc)))
attrs = (TAG, POS, MORPH, LEMMA, DEP)
has_annotation = {attr: doc.has_annotation(attr) for attr in attrs}
for attr in attrs:
@ -208,30 +240,9 @@ cdef class PhraseMatcher:
and self.attr not in attrs:
string_attr = self.vocab.strings[self.attr]
warnings.warn(Warnings.W012.format(key=key, attr=string_attr))
keyword = self._convert_to_array(doc)
else:
keyword = doc
self._docs[key].add(tuple(keyword))
specs.append(self._convert_to_array(doc))
current_node = self.c_map
for token in keyword:
if token == self._terminal_hash:
warnings.warn(Warnings.W021)
break
result = <MapStruct*>map_get(current_node, token)
if not result:
internal_node = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
map_init(self.mem, internal_node, 8)
map_set(self.mem, current_node, token, internal_node)
result = internal_node
current_node = <MapStruct*>result
result = <MapStruct*>map_get(current_node, self._terminal_hash)
if not result:
internal_node = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
map_init(self.mem, internal_node, 8)
map_set(self.mem, current_node, self._terminal_hash, internal_node)
result = internal_node
map_set(self.mem, <MapStruct*>result, self.vocab.strings[key], NULL)
self._add_from_arrays(key, specs, on_match=on_match)
def __call__(self, object doclike, *, as_spans=False):
"""Find all sequences matching the supplied patterns on the `Doc`.
@ -345,7 +356,7 @@ def unpickle_matcher(vocab, docs, callbacks, attr):
matcher = PhraseMatcher(vocab, attr=attr)
for key, specs in docs.items():
callback = callbacks.get(key, None)
matcher.add(key, specs, on_match=callback)
matcher._add_from_arrays(key, specs, on_match=callback)
return matcher

384
spacy/matcher/polyleven.c Normal file
View 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);
}

164
spacy/ml/_precomputable_affine.py
View File

@ -1,164 +0,0 @@
from thinc.api import Model, normal_init
from ..util import registry
@registry.layers("spacy.PrecomputableAffine.v1")
def PrecomputableAffine(nO, nI, nF, nP, dropout=0.1):
model = Model(
"precomputable_affine",
forward,
init=init,
dims={"nO": nO, "nI": nI, "nF": nF, "nP": nP},
params={"W": None, "b": None, "pad": None},
attrs={"dropout_rate": dropout},
)
return model
def forward(model, X, is_train):
nF = model.get_dim("nF")
nO = model.get_dim("nO")
nP = model.get_dim("nP")
nI = model.get_dim("nI")
W = model.get_param("W")
# Preallocate array for layer output, including padding.
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))
# 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
# thing from what we put out. We put out an array of shape:
# (nB, nF, nO, nP), and get back:
# (nB, nO, nP) and ids (nB, nF)
# The ids tell us the values of nF, so we would have:
#
# dYf = zeros((nB, nF, nO, nP))
# for b in range(nB):
# for f in range(nF):
# dYf[b, ids[b, f]] += dY[b]
#
# However, we avoid building that array for efficiency -- and just pass
# in the indices.
dY, ids = dY_ids
assert dY.ndim == 3
assert dY.shape[1] == nO, dY.shape
assert dY.shape[2] == nP, dY.shape
# nB = dY.shape[0]
model.inc_grad("pad", _backprop_precomputable_affine_padding(model, dY, ids))
Xf = X[ids]
Xf = Xf.reshape((Xf.shape[0], nF * nI))
model.inc_grad("b", dY.sum(axis=0))
dY = dY.reshape((dY.shape[0], nO * nP))
Wopfi = W.transpose((1, 2, 0, 3))
Wopfi = Wopfi.reshape((nO * nP, nF * nI))
dXf = model.ops.gemm(dY.reshape((dY.shape[0], nO * nP)), Wopfi)
dWopfi = model.ops.gemm(dY, Xf, trans1=True)
dWopfi = dWopfi.reshape((nO, nP, nF, nI))
# (o, p, f, i) --> (f, o, p, i)
dWopfi = dWopfi.transpose((2, 0, 1, 3))
model.inc_grad("W", dWopfi)
return dXf.reshape((dXf.shape[0], nF, nI))
return Yf, backward
def _backprop_precomputable_affine_padding(model, dY, ids):
nB = dY.shape[0]
nF = model.get_dim("nF")
nP = model.get_dim("nP")
nO = model.get_dim("nO")
# Backprop the "padding", used as a filler for missing values.
# Values that are missing are set to -1, and each state vector could
# have multiple missing values. The padding has different values for
# different missing features. The gradient of the padding vector is:
#
# for b in range(nB):
# for f in range(nF):
# if ids[b, f] < 0:
# d_pad[f] += dY[b]
#
# Which can be rewritten as:
#
# (ids < 0).T @ dY
mask = model.ops.asarray(ids < 0, dtype="f")
d_pad = model.ops.gemm(mask, dY.reshape(nB, nO * nP), trans1=True)
return d_pad.reshape((1, nF, nO, nP))
def init(model, X=None, Y=None):
"""This is like the 'layer sequential unit variance', but instead
of taking the actual inputs, we randomly generate whitened data.
Why's this all so complicated? We have a huge number of inputs,
and the maxout unit makes guessing the dynamics tricky. Instead
we set the maxout weights to values that empirically result in
whitened outputs given whitened inputs.
"""
if model.has_param("W") and model.get_param("W").any():
return
nF = model.get_dim("nF")
nO = model.get_dim("nO")
nP = model.get_dim("nP")
nI = model.get_dim("nI")
W = model.ops.alloc4f(nF, nO, nP, nI)
b = model.ops.alloc2f(nO, nP)
pad = model.ops.alloc4f(1, nF, nO, nP)
ops = model.ops
W = normal_init(ops, W.shape, mean=float(ops.xp.sqrt(1.0 / nF * nI)))
pad = normal_init(ops, pad.shape, mean=1.0)
model.set_param("W", W)
model.set_param("b", b)
model.set_param("pad", pad)
ids = ops.alloc((5000, nF), dtype="f")
ids += ops.xp.random.uniform(0, 1000, ids.shape)
ids = ops.asarray(ids, dtype="i")
tokvecs = ops.alloc((5000, nI), dtype="f")
tokvecs += ops.xp.random.normal(loc=0.0, scale=1.0, size=tokvecs.size).reshape(
tokvecs.shape
)
def predict(ids, tokvecs):
# nS ids. nW tokvecs. Exclude the padding array.
hiddens = model.predict(tokvecs[:-1]) # (nW, f, o, p)
vectors = model.ops.alloc((ids.shape[0], nO * nP), dtype="f")
# need nS vectors
hiddens = hiddens.reshape((hiddens.shape[0] * nF, nO * nP))
model.ops.scatter_add(vectors, ids.flatten(), hiddens)
vectors = vectors.reshape((vectors.shape[0], nO, nP))
vectors += b
vectors = model.ops.asarray(vectors)
if nP >= 2:
return model.ops.maxout(vectors)[0]
else:
return vectors * (vectors >= 0)
tol_var = 0.01
tol_mean = 0.01
t_max = 10
W = model.get_param("W").copy()
b = model.get_param("b").copy()
for t_i in range(t_max):
acts1 = predict(ids, tokvecs)
var = model.ops.xp.var(acts1)
mean = model.ops.xp.mean(acts1)
if abs(var - 1.0) >= tol_var:
W /= model.ops.xp.sqrt(var)
model.set_param("W", W)
elif abs(mean) >= tol_mean:
b -= mean
model.set_param("b", b)
else:
break

8
spacy/ml/callbacks.py
View File

@ -23,6 +23,7 @@ DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS = [
"update",
"rehearse",
"get_loss",
"get_teacher_student_loss",
"initialize",
"begin_update",
"finish_update",
@ -89,11 +90,14 @@ 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(

0
spacy/ml/_character_embed.py → spacy/ml/character_embed.py
View File

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