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Merge remote-tracking branch 'upstream/master' into Non_greedy_quantifier_PR

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Adriane Boyd 2022-11-24 17:05:41 +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)
${{ parameters.prefix }} python -m pip install dist/$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)
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: |
${{ parameters.prefix }} python -m pytest --pyargs spacy
displayName: "Run CPU tests"
condition: eq(${{ 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')
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_md'}; config.to_disk('ner_source_md.cfg')"
# python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
# displayName: 'Test assemble CLI vectors warning'
# condition: eq(variables['python_version'], '3.8')
- script: |
python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
displayName: 'Test assemble CLI'
condition: eq(variables['python_version'], '3.8')
- script: |
python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
displayName: 'Test assemble CLI vectors warning'
condition: eq(variables['python_version'], '3.8')
- script: |
python -m pip install -U -r requirements.txt
displayName: "Install test requirements"
- script: |
python -m pytest --pyargs spacy -W error
displayName: "Run CPU tests"
- script: |
python -m pip install --pre thinc-apple-ops
python -m pytest --pyargs spacy
displayName: "Run CPU tests with thinc-apple-ops"
condition: and(startsWith(variables['imageName'], 'macos'), eq(variables['python.version'], '3.11'))
- script: |
python .github/validate_universe_json.py website/meta/universe.json
displayName: 'Test website/meta/universe.json'
condition: eq(variables['python_version'], '3.8')
- script: |
${{ parameters.prefix }} python -m pip install --pre thinc-apple-ops
${{ parameters.prefix }} python -m pytest --pyargs spacy
displayName: "Run CPU tests with thinc-apple-ops"
condition: and(startsWith(variables['imageName'], 'macos'), eq(variables['python.version'], '3.10'))

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.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.

67
.github/spacy_universe_alert.py vendored Normal file
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@ -0,0 +1,67 @@
import os
import sys
import json
from datetime import datetime
from slack_sdk.web.client import WebClient
CHANNEL = "#alerts-universe"
SLACK_TOKEN = os.environ.get("SLACK_BOT_TOKEN", "ENV VAR not available!")
DATETIME_FORMAT = "%Y-%m-%dT%H:%M:%SZ"
client = WebClient(SLACK_TOKEN)
github_context = json.loads(sys.argv[1])
event = github_context['event']
pr_title = event['pull_request']["title"]
pr_link = event['pull_request']["patch_url"].replace(".patch", "")
pr_author_url = event['sender']["html_url"]
pr_author_name = pr_author_url.rsplit('/')[-1]
pr_created_at_dt = datetime.strptime(
event['pull_request']["created_at"],
DATETIME_FORMAT
)
pr_created_at = pr_created_at_dt.strftime("%c")
pr_updated_at_dt = datetime.strptime(
event['pull_request']["updated_at"],
DATETIME_FORMAT
)
pr_updated_at = pr_updated_at_dt.strftime("%c")
blocks = [
{
"type": "section",
"text": {
"type": "mrkdwn",
"text": "📣 New spaCy Universe Project Alert ✨"
}
},
{
"type": "section",
"fields": [
{
"type": "mrkdwn",
"text": f"*Pull Request:*\n<{pr_link}|{pr_title}>"
},
{
"type": "mrkdwn",
"text": f"*Author:*\n<{pr_author_url}|{pr_author_name}>"
},
{
"type": "mrkdwn",
"text": f"*Created at:*\n {pr_created_at}"
},
{
"type": "mrkdwn",
"text": f"*Last Updated:*\n {pr_updated_at}"
}
]
}
]
client.chat_postMessage(
channel=CHANNEL,
text="spaCy universe project PR alert",
blocks=blocks
)

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
}
}

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

32
.github/workflows/spacy_universe_alert.yml vendored Normal file
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@ -0,0 +1,32 @@
name: spaCy universe project alert
on:
pull_request_target:
paths:
- "website/meta/universe.json"
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Dump GitHub context
env:
GITHUB_CONTEXT: ${{ toJson(github) }}
PR_NUMBER: ${{github.event.number}}
run: |
echo "$GITHUB_CONTEXT"
- 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 }}
GITHUB_CONTEXT: ${{ toJson(github) }}
CHANNEL: "#alerts-universe"
run: |
pip install slack-sdk==3.17.2 aiohttp==3.8.1
echo "$CHANNEL"
python .github/spacy_universe_alert.py "$GITHUB_CONTEXT"

1
.gitignore vendored
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@ -24,6 +24,7 @@ quickstart-training-generator.js
cythonize.json
spacy/*.html
*.cpp
*.c
*.so
# Vim / VSCode / editors

4
.pre-commit-config.yaml
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@ -5,8 +5,8 @@ repos:
- id: black
language_version: python3.7
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:

3
CONTRIBUTING.md
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@ -271,7 +271,8 @@ except: # noqa: E722
### Python conventions
All Python code must be written **compatible with Python 3.6+**.
All Python code must be written **compatible with Python 3.6+**. More detailed
code conventions can be found in the [developer docs](https://github.com/explosion/spaCy/blob/master/extra/DEVELOPER_DOCS/Code%20Conventions.md).
#### I/O and handling paths

6
README.md
View File

@ -8,7 +8,7 @@ be used in real products.
spaCy comes with
[pretrained pipelines](https://spacy.io/models) and
currently supports tokenization and training for **60+ languages**. It features
currently supports tokenization and training for **70+ languages**. It features
state-of-the-art speed and **neural network models** for tagging,
parsing, **named entity recognition**, **text classification** and more,
multi-task learning with pretrained **transformers** like BERT, as well as a
@ -16,7 +16,7 @@ production-ready [**training system**](https://spacy.io/usage/training) and easy
model packaging, deployment and workflow management. spaCy is commercial
open-source software, released under the MIT license.
💫 **Version 3.3.1 out now!**
💫 **Version 3.4 out now!**
[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
[![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
@ -79,7 +79,7 @@ more people can benefit from it.
## Features
- Support for **60+ languages**
- Support for **70+ languages**
- **Trained pipelines** for different languages and tasks
- Multi-task learning with pretrained **transformers** like BERT
- Support for pretrained **word vectors** and embeddings

42
azure-pipelines.yml
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@ -31,8 +31,8 @@ jobs:
inputs:
versionSpec: "3.7"
- script: |
pip install flake8==3.9.2
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823 --show-source --statistics
pip install flake8==5.0.4
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
displayName: "flake8"
- job: "Test"
@ -76,15 +76,24 @@ jobs:
# Python39Mac:
# imageName: "macos-latest"
# python.version: "3.9"
Python310Linux:
imageName: "ubuntu-latest"
python.version: "3.10"
# Python310Linux:
# imageName: "ubuntu-latest"
# python.version: "3.10"
Python310Windows:
imageName: "windows-latest"
python.version: "3.10"
Python310Mac:
imageName: "macos-latest"
python.version: "3.10"
# Python310Mac:
# imageName: "macos-latest"
# python.version: "3.10"
Python311Linux:
imageName: 'ubuntu-latest'
python.version: '3.11'
Python311Windows:
imageName: 'windows-latest'
python.version: '3.11'
Python311Mac:
imageName: 'macos-latest'
python.version: '3.11'
maxParallel: 4
pool:
vmImage: $(imageName)
@ -92,20 +101,3 @@ jobs:
- template: .github/azure-steps.yml
parameters:
python_version: '$(python.version)'
architecture: 'x64'
# - job: "TestGPU"
# dependsOn: "Validate"
# strategy:
# matrix:
# Python38LinuxX64_GPU:
# python.version: '3.8'
# pool:
# name: "LinuxX64_GPU"
# steps:
# - template: .github/azure-steps.yml
# parameters:
# python_version: '$(python.version)'
# architecture: 'x64'
# gpu: true
# num_build_jobs: 24

6
build-constraints.txt
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@ -1,6 +1,8 @@
# build version constraints for use with wheelwright + multibuild
numpy==1.15.0; python_version<='3.7'
numpy==1.17.3; python_version=='3.8'
numpy==1.15.0; python_version<='3.7' and platform_machine!='aarch64'
numpy==1.19.2; python_version<='3.7' and platform_machine=='aarch64'
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'

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.

42
extra/DEVELOPER_DOCS/ExplosionBot.md
View File

@ -16,18 +16,38 @@ To summon the robot, write a github comment on the issue/PR you wish to test. Th
Some things to note:
* The `@explosion-bot please` must be the beginning of the command - you cannot add anything in front of this or else the robot won't know how to parse it. Adding anything at the end aside from the test name will also confuse the robot, so keep it simple!
* The command name (such as `test_gpu`) must be one of the tests that the bot knows how to run. The available commands are documented in the bot's [workflow config](https://github.com/explosion/spaCy/blob/master/.github/workflows/explosionbot.yml#L26) and must match exactly one of the commands listed there.
* The robot can't do multiple things at once, so if you want it to run multiple tests, you'll have to summon it with one comment per test.
* For the `test_gpu` command, you can specify an optional thinc branch (from the spaCy repo) or a spaCy branch (from the thinc repo) with either the `--thinc-branch` or `--spacy-branch` flags. By default, the bot will pull in the PR branch from the repo where the command was issued, and the main branch of the other repository. However, if you need to run against another branch, you can say (for example):
- The `@explosion-bot please` must be the beginning of the command - you cannot add anything in front of this or else the robot won't know how to parse it. Adding anything at the end aside from the test name will also confuse the robot, so keep it simple!
- The command name (such as `test_gpu`) must be one of the tests that the bot knows how to run. The available commands are documented in the bot's [workflow config](https://github.com/explosion/spaCy/blob/master/.github/workflows/explosionbot.yml#L26) and must match exactly one of the commands listed there.
- The robot can't do multiple things at once, so if you want it to run multiple tests, you'll have to summon it with one comment per test.
```
@explosion-bot please test_gpu --thinc-branch develop
```
You can also specify a branch from an unmerged PR:
```
@explosion-bot please test_gpu --thinc-branch refs/pull/633/head
```
### Examples
- Execute spaCy slow GPU tests with a custom thinc branch from a spaCy PR:
```
@explosion-bot please test_slow_gpu --thinc-branch <branch_name>
```
`branch_name` can either be a named branch, e.g: `develop`, or an unmerged PR, e.g: `refs/pull/<pr_number>/head`.
- Execute spaCy Transformers GPU tests from a spaCy PR:
```
@explosion-bot please test_gpu --run-on spacy-transformers --run-on-branch master --spacy-branch current_pr
```
This will launch the GPU pipeline for the `spacy-transformers` repo on its `master` branch, using the current spaCy PR's branch to build spaCy. The name of the repository passed to `--run-on` is case-sensitive, e.g: use `spaCy` instead of `spacy`.
- General info about supported commands.
```
@explosion-bot please info
```
- Help text for a specific command
```
@explosion-bot please <command> --help
```
## Troubleshooting

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.dev3,<8.2.0",
"pathy",
"thinc>=8.1.0,<8.2.0",
"numpy>=1.15.0",
]
build-backend = "setuptools.build_meta"

14
requirements.txt
View File

@ -1,21 +1,21 @@
# Our libraries
spacy-legacy>=3.0.9,<3.1.0
spacy-legacy>=3.0.10,<3.1.0
spacy-loggers>=1.0.0,<2.0.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.0.dev3,<8.2.0
thinc>=8.1.0,<8.2.0
ml_datasets>=0.2.0,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.9.1,<1.1.0
srsly>=2.4.3,<3.0.0
catalogue>=2.0.6,<2.1.0
typer>=0.3.0,<0.5.0
typer>=0.3.0,<0.8.0
pathy>=0.3.5
# 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
@ -28,10 +28,12 @@ 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,<=0.960
mypy>=0.990,<0.1000; platform_machine != "aarch64" and python_version >= "3.7"
types-dataclasses>=0.1.3; python_version < "3.7"
types-mock>=0.1.1
types-setuptools>=57.0.0
types-requests
types-setuptools>=57.0.0
black>=22.0,<23.0

50
setup.cfg
View File

@ -38,25 +38,25 @@ setup_requires =
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc>=8.1.0.dev3,<8.2.0
thinc>=8.1.0,<8.2.0
install_requires =
# Our libraries
spacy-legacy>=3.0.9,<3.1.0
spacy-legacy>=3.0.10,<3.1.0
spacy-loggers>=1.0.0,<2.0.0
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.0.dev3,<8.2.0
thinc>=8.1.0,<8.2.0
wasabi>=0.9.1,<1.1.0
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>=0.3.5
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
@ -76,37 +76,41 @@ transformers =
ray =
spacy_ray>=0.1.0,<1.0.0
cuda =
cupy>=5.0.0b4,<11.0.0
cupy>=5.0.0b4,<12.0.0
cuda80 =
cupy-cuda80>=5.0.0b4,<11.0.0
cupy-cuda80>=5.0.0b4,<12.0.0
cuda90 =
cupy-cuda90>=5.0.0b4,<11.0.0
cupy-cuda90>=5.0.0b4,<12.0.0
cuda91 =
cupy-cuda91>=5.0.0b4,<11.0.0
cupy-cuda91>=5.0.0b4,<12.0.0
cuda92 =
cupy-cuda92>=5.0.0b4,<11.0.0
cupy-cuda92>=5.0.0b4,<12.0.0
cuda100 =
cupy-cuda100>=5.0.0b4,<11.0.0
cupy-cuda100>=5.0.0b4,<12.0.0
cuda101 =
cupy-cuda101>=5.0.0b4,<11.0.0
cupy-cuda101>=5.0.0b4,<12.0.0
cuda102 =
cupy-cuda102>=5.0.0b4,<11.0.0
cupy-cuda102>=5.0.0b4,<12.0.0
cuda110 =
cupy-cuda110>=5.0.0b4,<11.0.0
cupy-cuda110>=5.0.0b4,<12.0.0
cuda111 =
cupy-cuda111>=5.0.0b4,<11.0.0
cupy-cuda111>=5.0.0b4,<12.0.0
cuda112 =
cupy-cuda112>=5.0.0b4,<11.0.0
cupy-cuda112>=5.0.0b4,<12.0.0
cuda113 =
cupy-cuda113>=5.0.0b4,<11.0.0
cupy-cuda113>=5.0.0b4,<12.0.0
cuda114 =
cupy-cuda114>=5.0.0b4,<11.0.0
cupy-cuda114>=5.0.0b4,<12.0.0
cuda115 =
cupy-cuda115>=5.0.0b4,<11.0.0
cupy-cuda115>=5.0.0b4,<12.0.0
cuda116 =
cupy-cuda116>=5.0.0b4,<11.0.0
cupy-cuda116>=5.0.0b4,<12.0.0
cuda117 =
cupy-cuda117>=5.0.0b4,<11.0.0
cupy-cuda117>=5.0.0b4,<12.0.0
cuda11x =
cupy-cuda11x>=11.0.0,<12.0.0
cuda-autodetect =
cupy-wheel>=11.0.0,<12.0.0
apple =
thinc-apple-ops>=0.1.0.dev0,<1.0.0
# Language tokenizers with external dependencies
@ -114,7 +118,7 @@ ja =
sudachipy>=0.5.2,!=0.6.1
sudachidict_core>=20211220
ko =
natto-py==0.9.0
natto-py>=0.9.0
th =
pythainlp>=2.0

23
setup.py
View File

@ -30,7 +30,9 @@ MOD_NAMES = [
"spacy.lexeme",
"spacy.vocab",
"spacy.attrs",
"spacy.kb",
"spacy.kb.candidate",
"spacy.kb.kb",
"spacy.kb.kb_in_memory",
"spacy.ml.parser_model",
"spacy.morphology",
"spacy.pipeline.dep_parser",
@ -126,6 +128,8 @@ class build_ext_options:
class build_ext_subclass(build_ext, build_ext_options):
def build_extensions(self):
if self.parallel is None and os.environ.get("SPACY_NUM_BUILD_JOBS") is not None:
self.parallel = int(os.environ.get("SPACY_NUM_BUILD_JOBS"))
build_ext_options.build_options(self)
build_ext.build_extensions(self)
@ -203,10 +207,25 @@ 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(
name, [mod_path], language="c++", include_dirs=include_dirs, extra_compile_args=["-std=c++11"]
name,
[mod_path],
language="c++",
include_dirs=include_dirs,
extra_compile_args=["-std=c++11"],
)
ext_modules.append(ext)
print("Cythonizing sources")

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__ = "3.4.0"
__version__ = "3.4.2"
__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"

9
spacy/cli/_util.py
View File

@ -573,3 +573,12 @@ 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 _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)

29
spacy/cli/debug_data.py
View File

@ -9,7 +9,7 @@ import typer
import math
from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
from ._util import import_code, debug_cli
from ._util import import_code, debug_cli, _format_number
from ..training import Example, remove_bilu_prefix
from ..training.initialize import get_sourced_components
from ..schemas import ConfigSchemaTraining
@ -989,7 +989,8 @@ def _get_kl_divergence(p: Counter, q: Counter) -> float:
def _format_span_row(span_data: List[Dict], labels: List[str]) -> List[Any]:
"""Compile into one list for easier reporting"""
d = {
label: [label] + list(round(d[label], 2) for d in span_data) for label in labels
label: [label] + list(_format_number(d[label]) for d in span_data)
for label in labels
}
return list(d.values())
@ -1004,6 +1005,10 @@ def _get_span_characteristics(
label: _gmean(l)
for label, l in compiled_gold["spans_length"][spans_key].items()
}
spans_per_type = {
label: len(spans)
for label, spans in compiled_gold["spans_per_type"][spans_key].items()
}
min_lengths = [min(l) for l in compiled_gold["spans_length"][spans_key].values()]
max_lengths = [max(l) for l in compiled_gold["spans_length"][spans_key].values()]
@ -1031,6 +1036,7 @@ def _get_span_characteristics(
return {
"sd": span_distinctiveness,
"bd": sb_distinctiveness,
"spans_per_type": spans_per_type,
"lengths": span_length,
"min_length": min(min_lengths),
"max_length": max(max_lengths),
@ -1045,12 +1051,15 @@ def _get_span_characteristics(
def _print_span_characteristics(span_characteristics: Dict[str, Any]):
"""Print all span characteristics into a table"""
headers = ("Span Type", "Length", "SD", "BD")
headers = ("Span Type", "Length", "SD", "BD", "N")
# Wasabi has this at 30 by default, but we might have some long labels
max_col = max(30, max(len(label) for label in span_characteristics["labels"]))
# Prepare table data with all span characteristics
table_data = [
span_characteristics["lengths"],
span_characteristics["sd"],
span_characteristics["bd"],
span_characteristics["spans_per_type"],
]
table = _format_span_row(
span_data=table_data, labels=span_characteristics["labels"]
@ -1061,8 +1070,18 @@ def _print_span_characteristics(span_characteristics: Dict[str, Any]):
span_characteristics["avg_sd"],
span_characteristics["avg_bd"],
]
footer = ["Wgt. Average"] + [str(round(f, 2)) for f in footer_data]
msg.table(table, footer=footer, header=headers, divider=True)
footer = (
["Wgt. Average"] + ["{:.2f}".format(round(f, 2)) for f in footer_data] + ["-"]
)
msg.table(
table,
footer=footer,
header=headers,
divider=True,
aligns=["l"] + ["r"] * (len(footer_data) + 1),
max_col=max_col,
)
def _get_spans_length_freq_dist(

36
spacy/cli/download.py
View File

@ -7,6 +7,7 @@ import typer
from ._util import app, Arg, Opt, WHEEL_SUFFIX, SDIST_SUFFIX
from .. import about
from ..util import is_package, get_minor_version, run_command
from ..util import is_prerelease_version
from ..errors import OLD_MODEL_SHORTCUTS
@ -19,7 +20,7 @@ def download_cli(
ctx: typer.Context,
model: str = Arg(..., help="Name of pipeline package to download"),
direct: bool = Opt(False, "--direct", "-d", "-D", help="Force direct download of name + version"),
sdist: bool = Opt(False, "--sdist", "-S", help="Download sdist (.tar.gz) archive instead of pre-built binary wheel")
sdist: bool = Opt(False, "--sdist", "-S", help="Download sdist (.tar.gz) archive instead of pre-built binary wheel"),
# fmt: on
):
"""
@ -35,7 +36,12 @@ def download_cli(
download(model, direct, sdist, *ctx.args)
def download(model: str, direct: bool = False, sdist: bool = False, *pip_args) -> None:
def download(
model: str,
direct: bool = False,
sdist: bool = False,
*pip_args,
) -> None:
if (
not (is_package("spacy") or is_package("spacy-nightly"))
and "--no-deps" not in pip_args
@ -49,13 +55,10 @@ def download(model: str, direct: bool = False, sdist: bool = False, *pip_args) -
"dependencies, you'll have to install them manually."
)
pip_args = pip_args + ("--no-deps",)
suffix = SDIST_SUFFIX if sdist else WHEEL_SUFFIX
dl_tpl = "{m}-{v}/{m}-{v}{s}#egg={m}=={v}"
if direct:
components = model.split("-")
model_name = "".join(components[:-1])
version = components[-1]
download_model(dl_tpl.format(m=model_name, v=version, s=suffix), pip_args)
else:
model_name = model
if model in OLD_MODEL_SHORTCUTS:
@ -66,14 +69,30 @@ def download(model: str, direct: bool = False, sdist: bool = False, *pip_args) -
model_name = OLD_MODEL_SHORTCUTS[model]
compatibility = get_compatibility()
version = get_version(model_name, compatibility)
download_model(dl_tpl.format(m=model_name, v=version, s=suffix), pip_args)
filename = get_model_filename(model_name, version, sdist)
download_model(filename, pip_args)
msg.good(
"Download and installation successful",
f"You can now load the package via spacy.load('{model_name}')",
)
def get_model_filename(model_name: str, version: str, sdist: bool = False) -> str:
dl_tpl = "{m}-{v}/{m}-{v}{s}"
egg_tpl = "#egg={m}=={v}"
suffix = SDIST_SUFFIX if sdist else WHEEL_SUFFIX
filename = dl_tpl.format(m=model_name, v=version, s=suffix)
if sdist:
filename += egg_tpl.format(m=model_name, v=version)
return filename
def get_compatibility() -> dict:
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 +120,11 @@ def get_version(model: str, comp: dict) -> str:
return comp[model][0]
def get_latest_version(model: str) -> str:
comp = get_compatibility()
return get_version(model, comp)
def download_model(
filename: str, user_pip_args: Optional[Sequence[str]] = None
) -> None:

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),

2
spacy/cli/pretrain.py
View File

@ -61,7 +61,7 @@ def pretrain_cli(
# TODO: What's the solution here? How do we handle optional blocks?
msg.fail("The [pretraining] block in your config is empty", exits=1)
if not output_dir.exists():
output_dir.mkdir()
output_dir.mkdir(parents=True)
msg.good(f"Created output directory: {output_dir}")
# Save non-interpolated config
raw_config.to_disk(output_dir / "config.cfg")

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.

19
spacy/cli/project/remote_storage.py
View File

@ -10,6 +10,7 @@ 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 ...git_info import GIT_VERSION
from ... import about
from ...errors import Errors
if TYPE_CHECKING:
from pathy import Pathy # noqa: F401
@ -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(

52
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]
@ -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

7
spacy/cli/templates/quickstart_training.jinja
View File

@ -271,13 +271,8 @@ factory = "tok2vec"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = ${components.tok2vec.model.encode.width}
{% if has_letters -%}
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
rows = [5000, 2500, 2500, 2500]
{% else -%}
attrs = ["ORTH", "SHAPE"]
rows = [5000, 2500]
{% endif -%}
rows = [5000, 1000, 2500, 2500]
include_static_vectors = {{ "true" if optimize == "accuracy" else "false" }}
[components.tok2vec.model.encode]

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

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"

5
spacy/displacy/__init__.py
View File

@ -123,7 +123,8 @@ def app(environ, start_response):
def parse_deps(orig_doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
"""Generate dependency parse in {'words': [], 'arcs': []} format.
doc (Doc): Document do parse.
orig_doc (Doc): Document to parse.
options (Dict[str, Any]): Dependency parse specific visualisation options.
RETURNS (dict): Generated dependency parse keyed by words and arcs.
"""
doc = Doc(orig_doc.vocab).from_bytes(
@ -209,7 +210,7 @@ def parse_ents(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
def parse_spans(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
"""Generate spans in [{start: i, end: i, label: 'label'}] format.
"""Generate spans in [{start_token: i, end_token: i, label: 'label'}] format.
doc (Doc): Document to parse.
options (Dict[str, any]): Span-specific visualisation options.

61
spacy/displacy/render.py
View File

@ -130,26 +130,56 @@ class SpanRenderer:
title (str / None): Document title set in Doc.user_data['title'].
"""
per_token_info = []
# we must sort so that we can correctly describe when spans need to "stack"
# which is determined by their start token, then span length (longer spans on top),
# then break any remaining ties with the span label
spans = sorted(
spans,
key=lambda s: (
s["start_token"],
-(s["end_token"] - s["start_token"]),
s["label"],
),
)
for s in spans:
# this is the vertical 'slot' that the span will be rendered in
# vertical_position = span_label_offset + (offset_step * (slot - 1))
s["render_slot"] = 0
for idx, token in enumerate(tokens):
# Identify if a token belongs to a Span (and which) and if it's a
# start token of said Span. We'll use this for the final HTML render
token_markup: Dict[str, Any] = {}
token_markup["text"] = token
concurrent_spans = 0
entities = []
for span in spans:
ent = {}
if span["start_token"] <= idx < span["end_token"]:
concurrent_spans += 1
span_start = idx == span["start_token"]
ent["label"] = span["label"]
ent["is_start"] = True if idx == span["start_token"] else False
ent["is_start"] = span_start
if span_start:
# When the span starts, we need to know how many other
# spans are on the 'span stack' and will be rendered.
# This value becomes the vertical render slot for this entire span
span["render_slot"] = concurrent_spans
ent["render_slot"] = span["render_slot"]
kb_id = span.get("kb_id", "")
kb_url = span.get("kb_url", "#")
ent["kb_link"] = (
TPL_KB_LINK.format(kb_id=kb_id, kb_url=kb_url) if kb_id else ""
)
entities.append(ent)
else:
# We don't specifically need to do this since we loop
# over tokens and spans sorted by their start_token,
# so we'll never use a span again after the last token it appears in,
# but if we were to use these spans again we'd want to make sure
# this value was reset correctly.
span["render_slot"] = 0
token_markup["entities"] = entities
per_token_info.append(token_markup)
markup = self._render_markup(per_token_info)
markup = TPL_SPANS.format(content=markup, dir=self.direction)
if title:
@ -160,8 +190,12 @@ class SpanRenderer:
"""Render the markup from per-token information"""
markup = ""
for token in per_token_info:
entities = sorted(token["entities"], key=lambda d: d["label"])
if entities:
entities = sorted(token["entities"], key=lambda d: d["render_slot"])
# Whitespace tokens disrupt the vertical space (no line height) so that the
# span indicators get misaligned. We don't render them as individual
# tokens anyway, so we'll just not display a span indicator either.
is_whitespace = token["text"].strip() == ""
if entities and not is_whitespace:
slices = self._get_span_slices(token["entities"])
starts = self._get_span_starts(token["entities"])
total_height = (
@ -182,10 +216,18 @@ class SpanRenderer:
def _get_span_slices(self, entities: List[Dict]) -> str:
"""Get the rendered markup of all Span slices"""
span_slices = []
for entity, step in zip(entities, itertools.count(step=self.offset_step)):
for entity in entities:
# rather than iterate over multiples of offset_step, we use entity['render_slot']
# to determine the vertical position, since that tells where
# the span starts vertically so we can extend it horizontally,
# past other spans that might have already ended
color = self.colors.get(entity["label"].upper(), self.default_color)
top_offset = self.top_offset + (
self.offset_step * (entity["render_slot"] - 1)
)
span_slice = self.span_slice_template.format(
bg=color, top_offset=self.top_offset + step
bg=color,
top_offset=top_offset,
)
span_slices.append(span_slice)
return "".join(span_slices)
@ -193,12 +235,15 @@ class SpanRenderer:
def _get_span_starts(self, entities: List[Dict]) -> str:
"""Get the rendered markup of all Span start tokens"""
span_starts = []
for entity, step in zip(entities, itertools.count(step=self.offset_step)):
for entity in entities:
color = self.colors.get(entity["label"].upper(), self.default_color)
top_offset = self.top_offset + (
self.offset_step * (entity["render_slot"] - 1)
)
span_start = (
self.span_start_template.format(
bg=color,
top_offset=self.top_offset + step,
top_offset=top_offset,
label=entity["label"],
kb_link=entity["kb_link"],
)

43
spacy/errors.py
View File

@ -16,8 +16,8 @@ def setup_default_warnings():
filter_warning("ignore", error_msg="numpy.dtype size changed") # noqa
filter_warning("ignore", error_msg="numpy.ufunc size changed") # noqa
# warn about entity_ruler & matcher having no patterns only once
for pipe in ["matcher", "entity_ruler"]:
# warn about entity_ruler, span_ruler & matcher having no patterns only once
for pipe in ["matcher", "entity_ruler", "span_ruler"]:
filter_warning("once", error_msg=Warnings.W036.format(name=pipe))
# warn once about lemmatizer without required POS
@ -212,6 +212,8 @@ class Warnings(metaclass=ErrorsWithCodes):
W121 = ("Attempting to trace non-existent method '{method}' in pipe '{pipe}'")
W122 = ("Couldn't trace method '{method}' in pipe '{pipe}'. This can happen if the pipe class "
"is a Cython extension type.")
W123 = ("Argument `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.")
class Errors(metaclass=ErrorsWithCodes):
@ -230,8 +232,9 @@ class Errors(metaclass=ErrorsWithCodes):
"initialized component.")
E004 = ("Can't set up pipeline component: a factory for '{name}' already "
"exists. Existing factory: {func}. New factory: {new_func}")
E005 = ("Pipeline component '{name}' returned None. If you're using a "
"custom component, maybe you forgot to return the processed Doc?")
E005 = ("Pipeline component '{name}' returned {returned_type} instead of a "
"Doc. If you're using a custom component, maybe you forgot to "
"return the processed Doc?")
E006 = ("Invalid constraints for adding pipeline component. You can only "
"set one of the following: before (component name or index), "
"after (component name or index), first (True) or last (True). "
@ -389,7 +392,7 @@ class Errors(metaclass=ErrorsWithCodes):
"consider using doc.spans instead.")
E106 = ("Can't find `doc._.{attr}` attribute specified in the underscore "
"settings: {opts}")
E107 = ("Value of `doc._.{attr}` is not JSON-serializable: {value}")
E107 = ("Value of custom attribute `{attr}` is not JSON-serializable: {value}")
E109 = ("Component '{name}' could not be run. Did you forget to "
"call `initialize()`?")
E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
@ -535,11 +538,18 @@ 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 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.")
@ -705,11 +715,11 @@ 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}")
@ -935,8 +945,17 @@ 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`.")
# Deprecated model shortcuts, only used in errors and warnings

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
View File

@ -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
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@ -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/kb_in_memory
"""
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

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

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"},
},

17
spacy/lang/ru/lemmatizer.py
View File

@ -19,11 +19,11 @@ 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:
@ -33,6 +33,16 @@ class RussianLemmatizer(Lemmatizer):
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer()
elif mode == "pymorphy3":
try:
from pymorphy3 import MorphAnalyzer
except ImportError:
raise ImportError(
"The Russian lemmatizer mode 'pymorphy3' requires the "
"pymorphy3 library. Install it with: pip install pymorphy3"
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer()
super().__init__(
vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
)
@ -104,6 +114,9 @@ class RussianLemmatizer(Lemmatizer):
return [analyses[0].normal_form]
return [string]
def pymorphy3_lemmatize(self, token: Token) -> List[str]:
return self.pymorphy2_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
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@ -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 == "pymorphy3":
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
)

101
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
from typing import Union, Tuple, List, Set, Pattern, Sequence
from typing import NoReturn, TYPE_CHECKING, TypeVar, cast, overload
@ -10,6 +10,7 @@ from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import warnings
from thinc.api import get_current_ops, Config, CupyOps, Optimizer
import srsly
import multiprocessing as mp
@ -24,7 +25,7 @@ from .pipe_analysis import validate_attrs, analyze_pipes, print_pipe_analysis
from .training import Example, validate_examples
from .training.initialize import init_vocab, init_tok2vec
from .scorer import Scorer
from .util import registry, SimpleFrozenList, _pipe, raise_error
from .util import registry, SimpleFrozenList, _pipe, raise_error, _DEFAULT_EMPTY_PIPES
from .util import SimpleFrozenDict, combine_score_weights, CONFIG_SECTION_ORDER
from .util import warn_if_jupyter_cupy
from .lang.tokenizer_exceptions import URL_MATCH, BASE_EXCEPTIONS
@ -465,6 +466,8 @@ class Language:
"""
if not isinstance(name, str):
raise ValueError(Errors.E963.format(decorator="factory"))
if "." in name:
raise ValueError(Errors.E853.format(name=name))
if not isinstance(default_config, dict):
err = Errors.E962.format(
style="default config", name=name, cfg_type=type(default_config)
@ -543,8 +546,11 @@ class Language:
DOCS: https://spacy.io/api/language#component
"""
if name is not None and not isinstance(name, str):
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:
@ -700,13 +706,7 @@ class Language:
# 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(
@ -784,14 +784,6 @@ 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,
@ -1023,8 +1015,8 @@ class Language:
raise ValueError(Errors.E109.format(name=name)) from e
except Exception as e:
error_handler(name, proc, [doc], e)
if doc is None:
raise ValueError(Errors.E005.format(name=name))
if not isinstance(doc, Doc):
raise ValueError(Errors.E005.format(name=name, returned_type=type(doc)))
return doc
def disable_pipes(self, *names) -> "DisabledPipes":
@ -1058,7 +1050,7 @@ class Language:
"""
if enable is None and disable is None:
raise ValueError(Errors.E991)
if disable is not None and isinstance(disable, str):
if isinstance(disable, str):
disable = [disable]
if enable is not None:
if isinstance(enable, str):
@ -1693,9 +1685,9 @@ class Language:
config: Union[Dict[str, Any], Config] = {},
*,
vocab: Union[Vocab, bool] = True,
disable: Iterable[str] = SimpleFrozenList(),
enable: Iterable[str] = SimpleFrozenList(),
exclude: Iterable[str] = SimpleFrozenList(),
disable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
meta: Dict[str, Any] = SimpleFrozenDict(),
auto_fill: bool = True,
validate: bool = True,
@ -1706,12 +1698,12 @@ class Language:
config (Dict[str, Any] / Config): The loaded config.
vocab (Vocab): A Vocab object. If True, a vocab is created.
disable (Iterable[str]): Names of pipeline components to disable.
disable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to disable.
Disabled pipes will be loaded but they won't be run unless you
explicitly enable them by calling nlp.enable_pipe.
enable (Iterable[str]): Names of pipeline components to enable. All other
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable. All other
pipes will be disabled (and can be enabled using `nlp.enable_pipe`).
exclude (Iterable[str]): Names of pipeline components to exclude.
exclude (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to exclude.
Excluded components won't be loaded.
meta (Dict[str, Any]): Meta overrides for nlp.meta.
auto_fill (bool): Automatically fill in missing values in config based
@ -1866,9 +1858,29 @@ 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)
@ -2026,37 +2038,36 @@ 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)

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"]

20
spacy/matcher/dependencymatcher.pyx
View File

@ -82,6 +82,10 @@ cdef class DependencyMatcher:
"$-": self._imm_left_sib,
"$++": self._right_sib,
"$--": self._left_sib,
">++": self._right_child,
">--": self._left_child,
"<++": self._right_parent,
"<--": self._left_parent,
}
def __reduce__(self):
@ -423,6 +427,22 @@ cdef class DependencyMatcher:
def _left_sib(self, doc, node):
return [doc[child.i] for child in doc[node].head.children if child.i < node]
def _right_child(self, doc, node):
return [doc[child.i] for child in doc[node].children if child.i > node]
def _left_child(self, doc, node):
return [doc[child.i] for child in doc[node].children if child.i < node]
def _right_parent(self, doc, node):
if doc[node].head.i > node:
return [doc[node].head]
return []
def _left_parent(self, doc, node):
if doc[node].head.i < node:
return [doc[node].head]
return []
def _normalize_key(self, key):
if isinstance(key, str):
return self.vocab.strings.add(key)

15
spacy/matcher/levenshtein.pyx Normal file
View File

@ -0,0 +1,15 @@
# cython: profile=True, binding=True, infer_types=True
from cpython.object cimport PyObject
from libc.stdint cimport int64_t
from typing import Optional
cdef extern from "polyleven.c":
int64_t polyleven(PyObject *o1, PyObject *o2, int64_t k)
cpdef int64_t levenshtein(a: str, b: str, k: Optional[int] = None):
if k is None:
k = -1
return polyleven(<PyObject*>a, <PyObject*>b, k)

25
spacy/matcher/matcher.pyx
View File

@ -1,5 +1,5 @@
# cython: infer_types=True, cython: profile=True
from typing import List
# cython: infer_types=True, profile=True
from typing import List, Iterable
from libcpp.vector cimport vector
from libc.stdint cimport int32_t, int8_t
@ -1012,20 +1012,27 @@ class _SetPredicate:
def __call__(self, Token token):
if self.is_extension:
value = get_string_id(token._.get(self.attr))
value = token._.get(self.attr)
else:
value = get_token_attr_for_matcher(token.c, self.attr)
if self.predicate in ("IS_SUBSET", "IS_SUPERSET", "INTERSECTS"):
if self.predicate in ("IN", "NOT_IN"):
if isinstance(value, (str, int)):
value = get_string_id(value)
else:
return False
elif self.predicate in ("IS_SUBSET", "IS_SUPERSET", "INTERSECTS"):
# ensure that all values are enclosed in a set
if self.attr == MORPH:
# break up MORPH into individual Feat=Val values
value = set(get_string_id(v) for v in MorphAnalysis.from_id(self.vocab, value))
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
elif self.predicate == "NOT_IN":

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);
}

6
spacy/ml/_precomputable_affine.py
View File

@ -26,7 +26,11 @@ def forward(model, X, is_train):
Yf = model.ops.alloc2f(X.shape[0] + 1, nF * nO * nP, zeros=False)
model.ops.gemm(X, W.reshape((nF * nO * nP, nI)), trans2=True, out=Yf[1:])
Yf = Yf.reshape((Yf.shape[0], nF, nO, nP))
Yf[0] = model.get_param("pad")
# Set padding. Padding has shape (1, nF, nO, nP). Unfortunately, we cannot
# change its shape to (nF, nO, nP) without breaking existing models. So
# we'll squeeze the first dimension here.
Yf[0] = model.ops.xp.squeeze(model.get_param("pad"), 0)
def backward(dY_ids):
# This backprop is particularly tricky, because we get back a different

7
spacy/ml/callbacks.py
View File

@ -89,11 +89,14 @@ def pipes_with_nvtx_range(
types.MethodType(nvtx_range_wrapper_for_pipe_method, pipe), func
)
# Try to preserve the original function signature.
# We need to preserve the original function signature so that
# the original parameters are passed to pydantic for validation downstream.
try:
wrapped_func.__signature__ = inspect.signature(func) # type: ignore
except:
pass
# Can fail for Cython methods that do not have bindings.
warnings.warn(Warnings.W122.format(method=name, pipe=pipe.name))
continue
try:
setattr(

37
spacy/ml/models/entity_linker.py
View File

@ -1,11 +1,12 @@
from pathlib import Path
from typing import Optional, Callable, Iterable, List, Tuple
from thinc.types import Floats2d
from thinc.api import chain, clone, list2ragged, reduce_mean, residual
from thinc.api import Model, Maxout, Linear, noop, tuplify, Ragged
from thinc.api import chain, list2ragged, reduce_mean, residual
from thinc.api import Model, Maxout, Linear, tuplify, Ragged
from ...util import registry
from ...kb import KnowledgeBase, Candidate, get_candidates
from ...kb import KnowledgeBase, InMemoryLookupKB
from ...kb import Candidate, get_candidates, get_candidates_batch
from ...vocab import Vocab
from ...tokens import Span, Doc
from ..extract_spans import extract_spans
@ -70,17 +71,18 @@ def span_maker_forward(model, docs: List[Doc], is_train) -> Tuple[Ragged, Callab
cands.append((start_token, end_token))
candidates.append(ops.asarray2i(cands))
candlens = ops.asarray1i([len(cands) for cands in candidates])
candidates = ops.xp.concatenate(candidates)
outputs = Ragged(candidates, candlens)
lengths = model.ops.asarray1i([len(cands) for cands in candidates])
out = Ragged(model.ops.flatten(candidates), lengths)
# because this is just rearranging docs, the backprop does nothing
return outputs, lambda x: []
return out, lambda x: []
@registry.misc("spacy.KBFromFile.v1")
def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
def kb_from_file(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
def load_kb(
kb_path: Path,
) -> Callable[[Vocab], KnowledgeBase]:
def kb_from_file(vocab: Vocab):
kb = InMemoryLookupKB(vocab, entity_vector_length=1)
kb.from_disk(kb_path)
return kb
@ -88,9 +90,11 @@ def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
@registry.misc("spacy.EmptyKB.v1")
def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
def empty_kb_factory(vocab):
return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
def empty_kb(
entity_vector_length: int,
) -> Callable[[Vocab], KnowledgeBase]:
def empty_kb_factory(vocab: Vocab):
return InMemoryLookupKB(vocab=vocab, entity_vector_length=entity_vector_length)
return empty_kb_factory
@ -98,3 +102,10 @@ def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
@registry.misc("spacy.CandidateGenerator.v1")
def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
return get_candidates
@registry.misc("spacy.CandidateBatchGenerator.v1")
def create_candidates_batch() -> Callable[
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
]:
return get_candidates_batch

2
spacy/ml/parser_model.pyx
View File

@ -441,7 +441,7 @@ cdef class precompute_hiddens:
cdef CBlas cblas
if isinstance(self.ops, CupyOps):
cblas = get_ops("cpu").cblas()
cblas = NUMPY_OPS.cblas()
else:
cblas = self.ops.cblas()

7
spacy/pipeline/edit_tree_lemmatizer.py
View File

@ -1,7 +1,6 @@
from typing import cast, Any, Callable, Dict, Iterable, List, Optional
from typing import Sequence, Tuple, Union
from typing import Tuple
from collections import Counter
from copy import deepcopy
from itertools import islice
import numpy as np
@ -149,9 +148,7 @@ class EditTreeLemmatizer(TrainablePipe):
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
n_labels = len(self.cfg["labels"])
guesses: List[Ints2d] = [
self.model.ops.alloc((0, n_labels), dtype="i") for doc in docs
]
guesses: List[Ints2d] = [self.model.ops.alloc2i(0, n_labels) for _ in docs]
assert len(guesses) == n_docs
return guesses
scores = self.model.predict(docs)

110
spacy/pipeline/entity_linker.py
View File

@ -53,9 +53,12 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
"incl_context": True,
"entity_vector_length": 64,
"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
"get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
"overwrite": True,
"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
"use_gold_ents": True,
"candidates_batch_size": 1,
"threshold": None,
},
default_score_weights={
"nel_micro_f": 1.0,
@ -74,9 +77,14 @@ def make_entity_linker(
incl_context: bool,
entity_vector_length: int,
get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
get_candidates_batch: Callable[
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
],
overwrite: bool,
scorer: Optional[Callable],
use_gold_ents: bool,
candidates_batch_size: int,
threshold: Optional[float] = None,
):
"""Construct an EntityLinker component.
@ -88,13 +96,21 @@ def make_entity_linker(
incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
incl_context (bool): Whether or not to include the local context in the model.
entity_vector_length (int): Size of encoding vectors in the KB.
get_candidates (Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]): Function that
get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
produces a list of candidates, given a certain knowledge base and a textual mention.
get_candidates_batch (
Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]]
): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
scorer (Optional[Callable]): The scoring method.
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
component must provide entity annotations.
candidates_batch_size (int): Size of batches for entity candidate generation.
threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
prediction is discarded. If None, predictions are not filtered by any threshold.
"""
if not model.attrs.get("include_span_maker", False):
# The only difference in arguments here is that use_gold_ents is not available
# The only difference in arguments here is that use_gold_ents and threshold aren't available.
return EntityLinker_v1(
nlp.vocab,
model,
@ -118,9 +134,12 @@ def make_entity_linker(
incl_context=incl_context,
entity_vector_length=entity_vector_length,
get_candidates=get_candidates,
get_candidates_batch=get_candidates_batch,
overwrite=overwrite,
scorer=scorer,
use_gold_ents=use_gold_ents,
candidates_batch_size=candidates_batch_size,
threshold=threshold,
)
@ -153,9 +172,14 @@ class EntityLinker(TrainablePipe):
incl_context: bool,
entity_vector_length: int,
get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
get_candidates_batch: Callable[
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
],
overwrite: bool = BACKWARD_OVERWRITE,
scorer: Optional[Callable] = entity_linker_score,
use_gold_ents: bool,
candidates_batch_size: int,
threshold: Optional[float] = None,
) -> None:
"""Initialize an entity linker.
@ -170,13 +194,28 @@ class EntityLinker(TrainablePipe):
entity_vector_length (int): Size of encoding vectors in the KB.
get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
produces a list of candidates, given a certain knowledge base and a textual mention.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_links.
get_candidates_batch (
Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]],
Iterable[Candidate]]
): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
component must provide entity annotations.
candidates_batch_size (int): Size of batches for entity candidate generation.
threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the
threshold, prediction is discarded. If None, predictions are not filtered by any threshold.
DOCS: https://spacy.io/api/entitylinker#init
"""
if threshold is not None and not (0 <= threshold <= 1):
raise ValueError(
Errors.E1043.format(
range_start=0,
range_end=1,
value=threshold,
)
)
self.vocab = vocab
self.model = model
self.name = name
@ -185,13 +224,19 @@ class EntityLinker(TrainablePipe):
self.incl_prior = incl_prior
self.incl_context = incl_context
self.get_candidates = get_candidates
self.get_candidates_batch = get_candidates_batch
self.cfg: Dict[str, Any] = {"overwrite": overwrite}
self.distance = CosineDistance(normalize=False)
# how many neighbour sentences to take into account
# create an empty KB by default. If you want to load a predefined one, specify it in 'initialize'.
# create an empty KB by default
self.kb = empty_kb(entity_vector_length)(self.vocab)
self.scorer = scorer
self.use_gold_ents = use_gold_ents
self.candidates_batch_size = candidates_batch_size
self.threshold = threshold
if candidates_batch_size < 1:
raise ValueError(Errors.E1044)
def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
"""Define the KB of this pipe by providing a function that will
@ -199,7 +244,7 @@ class EntityLinker(TrainablePipe):
if not callable(kb_loader):
raise ValueError(Errors.E885.format(arg_type=type(kb_loader)))
self.kb = kb_loader(self.vocab)
self.kb = kb_loader(self.vocab) # type: ignore
def validate_kb(self) -> None:
# Raise an error if the knowledge base is not initialized.
@ -221,8 +266,8 @@ class EntityLinker(TrainablePipe):
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
nlp (Language): The current nlp object the component is part of.
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
Note that providing this argument, will overwrite all data accumulated in the current KB.
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab
instance. Note that providing this argument will overwrite all data accumulated in the current KB.
Use this only when loading a KB as-such from file.
DOCS: https://spacy.io/api/entitylinker#initialize
@ -399,15 +444,40 @@ class EntityLinker(TrainablePipe):
if len(doc) == 0:
continue
sentences = [s for s in doc.sents]
# Looping through each entity (TODO: rewrite)
for ent in doc.ents:
# Loop over entities in batches.
for ent_idx in range(0, len(doc.ents), self.candidates_batch_size):
ent_batch = doc.ents[ent_idx : ent_idx + self.candidates_batch_size]
# Look up candidate entities.
valid_ent_idx = [
idx
for idx in range(len(ent_batch))
if ent_batch[idx].label_ not in self.labels_discard
]
batch_candidates = list(
self.get_candidates_batch(
self.kb, [ent_batch[idx] for idx in valid_ent_idx]
)
if self.candidates_batch_size > 1
else [
self.get_candidates(self.kb, ent_batch[idx])
for idx in valid_ent_idx
]
)
# Looping through each entity in batch (TODO: rewrite)
for j, ent in enumerate(ent_batch):
sent_index = sentences.index(ent.sent)
assert sent_index >= 0
if self.incl_context:
# get n_neighbour sentences, clipped to the length of the document
start_sentence = max(0, sent_index - self.n_sents)
end_sentence = min(len(sentences) - 1, sent_index + self.n_sents)
end_sentence = min(
len(sentences) - 1, sent_index + self.n_sents
)
start_token = sentences[start_sentence].start
end_token = sentences[end_sentence].end
sent_doc = doc[start_token:end_token].as_doc()
@ -420,13 +490,12 @@ class EntityLinker(TrainablePipe):
# ignoring this entity - setting to NIL
final_kb_ids.append(self.NIL)
else:
candidates = list(self.get_candidates(self.kb, ent))
candidates = list(batch_candidates[j])
if not candidates:
# no prediction possible for this entity - setting to NIL
final_kb_ids.append(self.NIL)
elif len(candidates) == 1:
elif len(candidates) == 1 and self.threshold is None:
# shortcut for efficiency reasons: take the 1 candidate
# TODO: thresholding
final_kb_ids.append(candidates[0].entity_)
else:
random.shuffle(candidates)
@ -455,10 +524,13 @@ class EntityLinker(TrainablePipe):
if sims.shape != prior_probs.shape:
raise ValueError(Errors.E161)
scores = prior_probs + sims - (prior_probs * sims)
# TODO: thresholding
best_index = scores.argmax().item()
best_candidate = candidates[best_index]
final_kb_ids.append(best_candidate.entity_)
final_kb_ids.append(
candidates[scores.argmax().item()].entity_
if self.threshold is None
or scores.max() >= self.threshold
else EntityLinker.NIL
)
if not (len(final_kb_ids) == entity_count):
err = Errors.E147.format(
method="predict", msg="result variables not of equal length"

5
spacy/pipeline/entityruler.py
View File

@ -1,6 +1,5 @@
import warnings
from typing import Optional, Union, List, Dict, Tuple, Iterable, Any, Callable, Sequence
from typing import cast
import warnings
from collections import defaultdict
from pathlib import Path
import srsly
@ -317,7 +316,7 @@ class EntityRuler(Pipe):
phrase_pattern["id"] = ent_id
phrase_patterns.append(phrase_pattern)
for entry in token_patterns + phrase_patterns: # type: ignore[operator]
label = entry["label"]
label = entry["label"] # type: ignore
if "id" in entry:
ent_label = label
label = self._create_label(label, entry["id"])

12
spacy/pipeline/legacy/entity_linker.py
View File

@ -7,7 +7,7 @@ from pathlib import Path
from itertools import islice
import srsly
import random
from thinc.api import CosineDistance, Model, Optimizer, Config
from thinc.api import CosineDistance, Model, Optimizer
from thinc.api import set_dropout_rate
import warnings
@ -20,7 +20,7 @@ from ...language import Language
from ...vocab import Vocab
from ...training import Example, validate_examples, validate_get_examples
from ...errors import Errors, Warnings
from ...util import SimpleFrozenList, registry
from ...util import SimpleFrozenList
from ... import util
from ...scorer import Scorer
@ -68,9 +68,7 @@ class EntityLinker_v1(TrainablePipe):
entity_vector_length (int): Size of encoding vectors in the KB.
get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
produces a list of candidates, given a certain knowledge base and a textual mention.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_links.
scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
DOCS: https://spacy.io/api/entitylinker#init
"""
self.vocab = vocab
@ -116,7 +114,7 @@ class EntityLinker_v1(TrainablePipe):
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
nlp (Language): The current nlp object the component is part of.
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates an InMemoryLookupKB from a Vocab instance.
Note that providing this argument, will overwrite all data accumulated in the current KB.
Use this only when loading a KB as-such from file.
@ -272,7 +270,6 @@ class EntityLinker_v1(TrainablePipe):
final_kb_ids.append(self.NIL)
elif len(candidates) == 1:
# shortcut for efficiency reasons: take the 1 candidate
# TODO: thresholding
final_kb_ids.append(candidates[0].entity_)
else:
random.shuffle(candidates)
@ -301,7 +298,6 @@ class EntityLinker_v1(TrainablePipe):
if sims.shape != prior_probs.shape:
raise ValueError(Errors.E161)
scores = prior_probs + sims - (prior_probs * sims)
# TODO: thresholding
best_index = scores.argmax().item()
best_candidate = candidates[best_index]
final_kb_ids.append(best_candidate.entity_)

2
spacy/pipeline/pipe.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, profile=True
# cython: infer_types=True, profile=True, binding=True
from typing import Optional, Tuple, Iterable, Iterator, Callable, Union, Dict
import srsly
import warnings

9
spacy/pipeline/spancat.py
View File

@ -26,17 +26,17 @@ scorer = {"@layers": "spacy.LinearLogistic.v1"}
hidden_size = 128
[model.tok2vec]
@architectures = "spacy.Tok2Vec.v1"
@architectures = "spacy.Tok2Vec.v2"
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v1"
@architectures = "spacy.MultiHashEmbed.v2"
width = 96
rows = [5000, 2000, 1000, 1000]
attrs = ["ORTH", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = ${model.tok2vec.embed.width}
window_size = 1
maxout_pieces = 3
@ -133,6 +133,9 @@ def make_spancat(
spans_key (str): Key of the doc.spans dict to save the spans under. During
initialization and training, the component will look for spans on the
reference document under the same key.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
threshold (float): Minimum probability to consider a prediction positive.
Spans with a positive prediction will be saved on the Doc. Defaults to
0.5.

23
spacy/pipeline/textcat.py
View File

@ -24,8 +24,8 @@ single_label_default_config = """
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = 64
rows = [2000, 2000, 1000, 1000, 1000, 1000]
attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
rows = [2000, 2000, 500, 1000, 500]
attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@ -72,7 +72,7 @@ subword_features = true
"textcat",
assigns=["doc.cats"],
default_config={
"threshold": 0.5,
"threshold": 0.0,
"model": DEFAULT_SINGLE_TEXTCAT_MODEL,
"scorer": {"@scorers": "spacy.textcat_scorer.v1"},
},
@ -144,7 +144,8 @@ class TextCategorizer(TrainablePipe):
model (thinc.api.Model): The Thinc Model powering the pipeline component.
name (str): The component instance name, used to add entries to the
losses during training.
threshold (float): Cutoff to consider a prediction "positive".
threshold (float): Unused, not needed for single-label (exclusive
classes) classification.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_cats for the attribute "cats".
@ -154,7 +155,11 @@ class TextCategorizer(TrainablePipe):
self.model = model
self.name = name
self._rehearsal_model = None
cfg = {"labels": [], "threshold": threshold, "positive_label": None}
cfg: Dict[str, Any] = {
"labels": [],
"threshold": threshold,
"positive_label": None,
}
self.cfg = dict(cfg)
self.scorer = scorer
@ -192,7 +197,7 @@ class TextCategorizer(TrainablePipe):
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
tensors = [doc.tensor for doc in docs]
xp = get_array_module(tensors)
xp = self.model.ops.xp
scores = xp.zeros((len(list(docs)), len(self.labels)))
return scores
scores = self.model.predict(docs)
@ -396,5 +401,9 @@ class TextCategorizer(TrainablePipe):
def _validate_categories(self, examples: Iterable[Example]):
"""Check whether the provided examples all have single-label cats annotations."""
for ex in examples:
if list(ex.reference.cats.values()).count(1.0) > 1:
vals = list(ex.reference.cats.values())
if vals.count(1.0) > 1:
raise ValueError(Errors.E895.format(value=ex.reference.cats))
for val in vals:
if not (val == 1.0 or val == 0.0):
raise ValueError(Errors.E851.format(val=val))

22
spacy/pipeline/textcat_multilabel.py
View File

@ -19,17 +19,17 @@ multi_label_default_config = """
@architectures = "spacy.TextCatEnsemble.v2"
[model.tok2vec]
@architectures = "spacy.Tok2Vec.v1"
@architectures = "spacy.Tok2Vec.v2"
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = 64
rows = [2000, 2000, 1000, 1000, 1000, 1000]
attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
rows = [2000, 2000, 500, 1000, 500]
attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = ${model.tok2vec.embed.width}
window_size = 1
maxout_pieces = 3
@ -96,8 +96,8 @@ def make_multilabel_textcat(
model: Model[List[Doc], List[Floats2d]],
threshold: float,
scorer: Optional[Callable],
) -> "TextCategorizer":
"""Create a TextCategorizer component. The text categorizer predicts categories
) -> "MultiLabel_TextCategorizer":
"""Create a MultiLabel_TextCategorizer component. The text categorizer predicts categories
over a whole document. It can learn one or more labels, and the labels are considered
to be non-mutually exclusive, which means that there can be zero or more labels
per doc).
@ -105,6 +105,7 @@ def make_multilabel_textcat(
model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
scores for each category.
threshold (float): Cutoff to consider a prediction "positive".
scorer (Optional[Callable]): The scoring method.
"""
return MultiLabel_TextCategorizer(
nlp.vocab, model, name, threshold=threshold, scorer=scorer
@ -147,6 +148,7 @@ class MultiLabel_TextCategorizer(TextCategorizer):
name (str): The component instance name, used to add entries to the
losses during training.
threshold (float): Cutoff to consider a prediction "positive".
scorer (Optional[Callable]): The scoring method.
DOCS: https://spacy.io/api/textcategorizer#init
"""
@ -190,6 +192,8 @@ class MultiLabel_TextCategorizer(TextCategorizer):
for label in labels:
self.add_label(label)
subbatch = list(islice(get_examples(), 10))
self._validate_categories(subbatch)
doc_sample = [eg.reference for eg in subbatch]
label_sample, _ = self._examples_to_truth(subbatch)
self._require_labels()
@ -200,4 +204,8 @@ class MultiLabel_TextCategorizer(TextCategorizer):
def _validate_categories(self, examples: Iterable[Example]):
"""This component allows any type of single- or multi-label annotations.
This method overwrites the more strict one from 'textcat'."""
pass
# check that annotation values are valid
for ex in examples:
for val in ex.reference.cats.values():
if not (val == 1.0 or val == 0.0):
raise ValueError(Errors.E851.format(val=val))

16
spacy/pipeline/tok2vec.py
View File

@ -123,9 +123,6 @@ class Tok2Vec(TrainablePipe):
width = self.model.get_dim("nO")
return [self.model.ops.alloc((0, width)) for doc in docs]
tokvecs = self.model.predict(docs)
batch_id = Tok2VecListener.get_batch_id(docs)
for listener in self.listeners:
listener.receive(batch_id, tokvecs, _empty_backprop)
return tokvecs
def set_annotations(self, docs: Sequence[Doc], tokvecses) -> None:
@ -286,6 +283,17 @@ class Tok2VecListener(Model):
def forward(model: Tok2VecListener, inputs, is_train: bool):
"""Supply the outputs from the upstream Tok2Vec component."""
if is_train:
# This might occur during training when the tok2vec layer is frozen / hasn't been updated.
# In that case, it should be set to "annotating" so we can retrieve the embeddings from the doc.
if model._batch_id is None:
outputs = []
for doc in inputs:
if doc.tensor.size == 0:
raise ValueError(Errors.E203.format(name="tok2vec"))
else:
outputs.append(doc.tensor)
return outputs, _empty_backprop
else:
model.verify_inputs(inputs)
return model._outputs, model._backprop
else:
@ -306,7 +314,7 @@ def forward(model: Tok2VecListener, inputs, is_train: bool):
outputs.append(model.ops.alloc2f(len(doc), width))
else:
outputs.append(doc.tensor)
return outputs, lambda dX: []
return outputs, _empty_backprop
def _empty_backprop(dX): # for pickling

2
spacy/pipeline/trainable_pipe.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, profile=True
# cython: infer_types=True, profile=True, binding=True
from typing import Iterable, Iterator, Optional, Dict, Tuple, Callable
import srsly
from thinc.api import set_dropout_rate, Model, Optimizer

7
spacy/pipeline/transition_parser.pyx
View File

@ -9,7 +9,7 @@ from libc.stdlib cimport calloc, free
import random
import srsly
from thinc.api import get_ops, set_dropout_rate, CupyOps
from thinc.api import get_ops, set_dropout_rate, CupyOps, NumpyOps
from thinc.extra.search cimport Beam
import numpy.random
import numpy
@ -30,6 +30,9 @@ from ..errors import Errors, Warnings
from .. import util
NUMPY_OPS = NumpyOps()
cdef class Parser(TrainablePipe):
"""
Base class of the DependencyParser and EntityRecognizer.
@ -262,7 +265,7 @@ cdef class Parser(TrainablePipe):
ops = self.model.ops
cdef CBlas cblas
if isinstance(ops, CupyOps):
cblas = get_ops("cpu").cblas()
cblas = NUMPY_OPS.cblas()
else:
cblas = ops.cblas()
self._ensure_labels_are_added(docs)

37
spacy/schemas.py
View File

@ -181,12 +181,12 @@ class TokenPatternNumber(BaseModel):
IS_SUBSET: Optional[List[StrictInt]] = Field(None, alias="is_subset")
IS_SUPERSET: Optional[List[StrictInt]] = Field(None, alias="is_superset")
INTERSECTS: Optional[List[StrictInt]] = Field(None, alias="intersects")
EQ: Union[StrictInt, StrictFloat] = Field(None, alias="==")
NEQ: Union[StrictInt, StrictFloat] = Field(None, alias="!=")
GEQ: Union[StrictInt, StrictFloat] = Field(None, alias=">=")
LEQ: Union[StrictInt, StrictFloat] = Field(None, alias="<=")
GT: Union[StrictInt, StrictFloat] = Field(None, alias=">")
LT: Union[StrictInt, StrictFloat] = Field(None, alias="<")
EQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="==")
NEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="!=")
GEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias=">=")
LEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="<=")
GT: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias=">")
LT: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="<")
class Config:
extra = "forbid"
@ -209,7 +209,7 @@ class TokenPatternOperatorSimple(str, Enum):
class TokenPatternOperatorMinMax(ConstrainedStr):
regex = re.compile("^({\d+}|{\d+,\d*}|{\d*,\d+})$")
regex = re.compile(r"^({\d+}|{\d+,\d*}|{\d*,\d+})$")
TokenPatternOperator = Union[TokenPatternOperatorSimple, TokenPatternOperatorMinMax]
@ -331,6 +331,7 @@ class ConfigSchemaTraining(BaseModel):
frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training")
annotating_components: List[str] = Field(..., title="Pipeline components that should set annotations during training")
before_to_disk: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after training, before it's saved to disk")
before_update: Optional[Callable[["Language", Dict[str, Any]], None]] = Field(..., title="Optional callback that is invoked at the start of each training step")
# fmt: on
class Config:
@ -432,7 +433,7 @@ class ProjectConfigAssetURL(BaseModel):
# fmt: off
dest: StrictStr = Field(..., title="Destination of downloaded asset")
url: Optional[StrictStr] = Field(None, title="URL of asset")
checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
description: StrictStr = Field("", title="Description of asset")
# fmt: on
@ -440,7 +441,7 @@ class ProjectConfigAssetURL(BaseModel):
class ProjectConfigAssetGit(BaseModel):
# fmt: off
git: ProjectConfigAssetGitItem = Field(..., title="Git repo information")
checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
description: Optional[StrictStr] = Field(None, title="Description of asset")
# fmt: on
@ -510,12 +511,20 @@ class DocJSONSchema(BaseModel):
None, title="Indices of sentences' start and end indices"
)
text: StrictStr = Field(..., title="Document text")
spans: Dict[StrictStr, List[Dict[StrictStr, Union[StrictStr, StrictInt]]]] = Field(
None, title="Span information - end/start indices, label, KB ID"
)
spans: Optional[
Dict[StrictStr, List[Dict[StrictStr, Union[StrictStr, StrictInt]]]]
] = Field(None, title="Span information - end/start indices, label, KB ID")
tokens: List[Dict[StrictStr, Union[StrictStr, StrictInt]]] = Field(
..., title="Token information - ID, start, annotations"
)
_: Optional[Dict[StrictStr, Any]] = Field(
None, title="Any custom data stored in the document's _ attribute"
underscore_doc: Optional[Dict[StrictStr, Any]] = Field(
None,
title="Any custom data stored in the document's _ attribute",
alias="_",
)
underscore_token: Optional[Dict[StrictStr, List[Dict[StrictStr, Any]]]] = Field(
None, title="Any custom data stored in the token's _ attribute"
)
underscore_span: Optional[Dict[StrictStr, List[Dict[StrictStr, Any]]]] = Field(
None, title="Any custom data stored in the span's _ attribute"
)

8
spacy/scorer.py
View File

@ -446,7 +446,7 @@ class Scorer:
labels (Iterable[str]): The set of possible labels. Defaults to [].
multi_label (bool): Whether the attribute allows multiple labels.
Defaults to True. When set to False (exclusive labels), missing
gold labels are interpreted as 0.0.
gold labels are interpreted as 0.0 and the threshold is set to 0.0.
positive_label (str): The positive label for a binary task with
exclusive classes. Defaults to None.
threshold (float): Cutoff to consider a prediction "positive". Defaults
@ -471,6 +471,8 @@ class Scorer:
"""
if threshold is None:
threshold = 0.5 if multi_label else 0.0
if not multi_label:
threshold = 0.0
f_per_type = {label: PRFScore() for label in labels}
auc_per_type = {label: ROCAUCScore() for label in labels}
labels = set(labels)
@ -505,11 +507,10 @@ class Scorer:
# Get the highest-scoring for each.
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
gold_label, gold_score = max(gold_cats.items(), key=lambda it: it[1])
if pred_label == gold_label and pred_score >= threshold:
if pred_label == gold_label:
f_per_type[pred_label].tp += 1
else:
f_per_type[gold_label].fn += 1
if pred_score >= threshold:
f_per_type[pred_label].fp += 1
elif gold_cats:
gold_label, gold_score = max(gold_cats, key=lambda it: it[1])
@ -517,7 +518,6 @@ class Scorer:
f_per_type[gold_label].fn += 1
elif pred_cats:
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
if pred_score >= threshold:
f_per_type[pred_label].fp += 1
micro_prf = PRFScore()
for label_prf in f_per_type.values():

2
spacy/strings.pxd
View File

@ -26,4 +26,4 @@ cdef class StringStore:
cdef public PreshMap _map
cdef const Utf8Str* intern_unicode(self, str py_string)
cdef const Utf8Str* _intern_utf8(self, char* utf8_string, int length)
cdef const Utf8Str* _intern_utf8(self, char* utf8_string, int length, hash_t* precalculated_hash)

119
spacy/strings.pyx
View File

@ -14,6 +14,13 @@ from .symbols import NAMES as SYMBOLS_BY_INT
from .errors import Errors
from . import util
# Not particularly elegant, but this is faster than `isinstance(key, numbers.Integral)`
cdef inline bint _try_coerce_to_hash(object key, hash_t* out_hash):
try:
out_hash[0] = key
return True
except:
return False
def get_string_id(key):
"""Get a string ID, handling the reserved symbols correctly. If the key is
@ -22,15 +29,27 @@ def get_string_id(key):
This function optimises for convenience over performance, so shouldn't be
used in tight loops.
"""
if not isinstance(key, str):
return key
elif key in SYMBOLS_BY_STR:
return SYMBOLS_BY_STR[key]
elif not key:
cdef hash_t str_hash
if isinstance(key, str):
if len(key) == 0:
return 0
symbol = SYMBOLS_BY_STR.get(key, None)
if symbol is not None:
return symbol
else:
chars = key.encode("utf8")
return hash_utf8(chars, len(chars))
elif _try_coerce_to_hash(key, &str_hash):
# Coerce the integral key to the expected primitive hash type.
# This ensures that custom/overloaded "primitive" data types
# such as those implemented by numpy are not inadvertently used
# downsteam (as these are internally implemented as custom PyObjects
# whose comparison operators can incur a significant overhead).
return str_hash
else:
# TODO: Raise an error instead
return key
cpdef hash_t hash_string(str string) except 0:
@ -110,24 +129,32 @@ cdef class StringStore:
string_or_id (bytes, str or uint64): The value to encode.
Returns (str / uint64): The value to be retrieved.
"""
if isinstance(string_or_id, str) and len(string_or_id) == 0:
return 0
elif string_or_id == 0:
return ""
elif string_or_id in SYMBOLS_BY_STR:
return SYMBOLS_BY_STR[string_or_id]
cdef hash_t key
cdef hash_t str_hash
cdef Utf8Str* utf8str = NULL
if isinstance(string_or_id, str):
key = hash_string(string_or_id)
return key
elif isinstance(string_or_id, bytes):
key = hash_utf8(string_or_id, len(string_or_id))
return key
elif string_or_id < len(SYMBOLS_BY_INT):
return SYMBOLS_BY_INT[string_or_id]
if len(string_or_id) == 0:
return 0
# Return early if the string is found in the symbols LUT.
symbol = SYMBOLS_BY_STR.get(string_or_id, None)
if symbol is not None:
return symbol
else:
key = string_or_id
utf8str = <Utf8Str*>self._map.get(key)
return hash_string(string_or_id)
elif isinstance(string_or_id, bytes):
return hash_utf8(string_or_id, len(string_or_id))
elif _try_coerce_to_hash(string_or_id, &str_hash):
if str_hash == 0:
return ""
elif str_hash < len(SYMBOLS_BY_INT):
return SYMBOLS_BY_INT[str_hash]
else:
utf8str = <Utf8Str*>self._map.get(str_hash)
else:
# TODO: Raise an error instead
utf8str = <Utf8Str*>self._map.get(string_or_id)
if utf8str is NULL:
raise KeyError(Errors.E018.format(hash_value=string_or_id))
else:
@ -153,19 +180,22 @@ cdef class StringStore:
string (str): The string to add.
RETURNS (uint64): The string's hash value.
"""
cdef hash_t str_hash
if isinstance(string, str):
if string in SYMBOLS_BY_STR:
return SYMBOLS_BY_STR[string]
key = hash_string(string)
self.intern_unicode(string)
string = string.encode("utf8")
str_hash = hash_utf8(string, len(string))
self._intern_utf8(string, len(string), &str_hash)
elif isinstance(string, bytes):
if string in SYMBOLS_BY_STR:
return SYMBOLS_BY_STR[string]
key = hash_utf8(string, len(string))
self._intern_utf8(string, len(string))
str_hash = hash_utf8(string, len(string))
self._intern_utf8(string, len(string), &str_hash)
else:
raise TypeError(Errors.E017.format(value_type=type(string)))
return key
return str_hash
def __len__(self):
"""The number of strings in the store.
@ -174,30 +204,29 @@ cdef class StringStore:
"""
return self.keys.size()
def __contains__(self, string not None):
"""Check whether a string is in the store.
def __contains__(self, string_or_id not None):
"""Check whether a string or ID is in the store.
string (str): The string to check.
string_or_id (str or int): The string to check.
RETURNS (bool): Whether the store contains the string.
"""
cdef hash_t key
if isinstance(string, int) or isinstance(string, long):
if string == 0:
cdef hash_t str_hash
if isinstance(string_or_id, str):
if len(string_or_id) == 0:
return True
key = string
elif len(string) == 0:
elif string_or_id in SYMBOLS_BY_STR:
return True
elif string in SYMBOLS_BY_STR:
return True
elif isinstance(string, str):
key = hash_string(string)
str_hash = hash_string(string_or_id)
elif _try_coerce_to_hash(string_or_id, &str_hash):
pass
else:
string = string.encode("utf8")
key = hash_utf8(string, len(string))
if key < len(SYMBOLS_BY_INT):
# TODO: Raise an error instead
return self._map.get(string_or_id) is not NULL
if str_hash < len(SYMBOLS_BY_INT):
return True
else:
return self._map.get(key) is not NULL
return self._map.get(str_hash) is not NULL
def __iter__(self):
"""Iterate over the strings in the store, in order.
@ -272,13 +301,13 @@ cdef class StringStore:
cdef const Utf8Str* intern_unicode(self, str py_string):
# 0 means missing, but we don't bother offsetting the index.
cdef bytes byte_string = py_string.encode("utf8")
return self._intern_utf8(byte_string, len(byte_string))
return self._intern_utf8(byte_string, len(byte_string), NULL)
@cython.final
cdef const Utf8Str* _intern_utf8(self, char* utf8_string, int length):
cdef const Utf8Str* _intern_utf8(self, char* utf8_string, int length, hash_t* precalculated_hash):
# TODO: This function's API/behaviour is an unholy mess...
# 0 means missing, but we don't bother offsetting the index.
cdef hash_t key = hash_utf8(utf8_string, length)
cdef hash_t key = precalculated_hash[0] if precalculated_hash is not NULL else hash_utf8(utf8_string, length)
cdef Utf8Str* value = <Utf8Str*>self._map.get(key)
if value is not NULL:
return value

41
spacy/tests/conftest.py
View File

@ -1,5 +1,11 @@
import pytest
from spacy.util import get_lang_class
from hypothesis import settings
# Functionally disable deadline settings for tests
# to prevent spurious test failures in CI builds.
settings.register_profile("no_deadlines", deadline=2 * 60 * 1000) # in ms
settings.load_profile("no_deadlines")
def pytest_addoption(parser):
@ -250,11 +256,21 @@ def ko_tokenizer_tokenizer():
return nlp.tokenizer
@pytest.fixture(scope="module")
def la_tokenizer():
return get_lang_class("la")().tokenizer
@pytest.fixture(scope="session")
def lb_tokenizer():
return get_lang_class("lb")().tokenizer
@pytest.fixture(scope="session")
def lg_tokenizer():
return get_lang_class("lg")().tokenizer
@pytest.fixture(scope="session")
def lt_tokenizer():
return get_lang_class("lt")().tokenizer
@ -317,16 +333,24 @@ def ro_tokenizer():
@pytest.fixture(scope="session")
def ru_tokenizer():
pytest.importorskip("pymorphy2")
pytest.importorskip("pymorphy3")
return get_lang_class("ru")().tokenizer
@pytest.fixture
def ru_lemmatizer():
pytest.importorskip("pymorphy2")
pytest.importorskip("pymorphy3")
return get_lang_class("ru")().add_pipe("lemmatizer")
@pytest.fixture
def ru_lookup_lemmatizer():
pytest.importorskip("pymorphy2")
return get_lang_class("ru")().add_pipe(
"lemmatizer", config={"mode": "pymorphy2_lookup"}
)
@pytest.fixture(scope="session")
def sa_tokenizer():
return get_lang_class("sa")().tokenizer
@ -395,15 +419,24 @@ def ky_tokenizer():
@pytest.fixture(scope="session")
def uk_tokenizer():
pytest.importorskip("pymorphy2")
pytest.importorskip("pymorphy3")
return get_lang_class("uk")().tokenizer
@pytest.fixture
def uk_lemmatizer():
pytest.importorskip("pymorphy3")
pytest.importorskip("pymorphy3_dicts_uk")
return get_lang_class("uk")().add_pipe("lemmatizer")
@pytest.fixture
def uk_lookup_lemmatizer():
pytest.importorskip("pymorphy2")
pytest.importorskip("pymorphy2_dicts_uk")
return get_lang_class("uk")().add_pipe("lemmatizer")
return get_lang_class("uk")().add_pipe(
"lemmatizer", config={"mode": "pymorphy2_lookup"}
)
@pytest.fixture(scope="session")

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