mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-25 01:16:28 +03:00
Merge remote-tracking branch 'upstream/master' into merge-master-v4-20220728
This commit is contained in:
Daniël de Kok
commit
1ff683a50b
37
.github/azure-steps.yml
vendored
37
.github/azure-steps.yml
vendored
|
|
@ -27,7 +27,6 @@ steps:
|
|||
|
||||
- script: python -m mypy spacy
|
||||
displayName: 'Run mypy'
|
||||
condition: ne(variables['python_version'], '3.10')
|
||||
|
||||
- task: DeleteFiles@1
|
||||
inputs:
|
||||
|
|
@ -41,7 +40,7 @@ steps:
|
|||
|
||||
- bash: |
|
||||
${{ parameters.prefix }} SDIST=$(python -c "import os;print(os.listdir('./dist')[-1])" 2>&1)
|
||||
${{ parameters.prefix }} python -m pip install dist/$SDIST
|
||||
${{ parameters.prefix }} SPACY_NUM_BUILD_JOBS=2 python -m pip install dist/$SDIST
|
||||
displayName: "Install from sdist"
|
||||
|
||||
- script: |
|
||||
|
|
@ -64,12 +63,12 @@ steps:
|
|||
displayName: "Run GPU tests"
|
||||
condition: eq(${{ parameters.gpu }}, true)
|
||||
|
||||
# - script: |
|
||||
# python -m spacy download ca_core_news_sm
|
||||
# python -m spacy download ca_core_news_md
|
||||
# python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')"
|
||||
# displayName: 'Test download CLI'
|
||||
# condition: eq(variables['python_version'], '3.8')
|
||||
- script: |
|
||||
python -m spacy download ca_core_news_sm
|
||||
python -m spacy download ca_core_news_md
|
||||
python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')"
|
||||
displayName: 'Test download CLI'
|
||||
condition: eq(variables['python_version'], '3.8')
|
||||
|
||||
- script: |
|
||||
python -m spacy convert extra/example_data/ner_example_data/ner-token-per-line-conll2003.json .
|
||||
|
|
@ -93,17 +92,17 @@ steps:
|
|||
displayName: 'Test train CLI'
|
||||
condition: eq(variables['python_version'], '3.8')
|
||||
|
||||
# - script: |
|
||||
# python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
|
||||
# PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
|
||||
# displayName: 'Test assemble CLI'
|
||||
# condition: eq(variables['python_version'], '3.8')
|
||||
#
|
||||
# - script: |
|
||||
# python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
|
||||
# python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
|
||||
# displayName: 'Test assemble CLI vectors warning'
|
||||
# condition: eq(variables['python_version'], '3.8')
|
||||
- script: |
|
||||
python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
|
||||
PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
|
||||
displayName: 'Test assemble CLI'
|
||||
condition: eq(variables['python_version'], '3.8')
|
||||
|
||||
- script: |
|
||||
python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
|
||||
python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
|
||||
displayName: 'Test assemble CLI vectors warning'
|
||||
condition: eq(variables['python_version'], '3.8')
|
||||
|
||||
- script: |
|
||||
python .github/validate_universe_json.py website/meta/universe.json
|
||||
|
|
|
|||
67
.github/spacy_universe_alert.py
vendored
Normal file
67
.github/spacy_universe_alert.py
vendored
Normal file
|
|
@ -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
|
||||
)
|
||||
30
.github/workflows/spacy_universe_alert.yml
vendored
Normal file
30
.github/workflows/spacy_universe_alert.yml
vendored
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
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@v1
|
||||
- uses: actions/setup-python@v1
|
||||
- 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"
|
||||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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 3.4.0 out now!**
|
||||
[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
|
||||
|
||||
[](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
|
||||
|
|
|
|||
|
|
@ -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'
|
||||
|
|
|
|||
|
|
@ -16,21 +16,41 @@ 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
|
||||
|
||||
If the robot isn't responding to commands as expected, you can check its logs in the [Github Action](https://github.com/explosion/spaCy/actions/workflows/explosionbot.yml).
|
||||
If the robot isn't responding to commands as expected, you can check its logs in the [Github Action](https://github.com/explosion/spaCy/actions/workflows/explosionbot.yml).
|
||||
|
||||
For each command sent to the bot, there should be a run of the `explosion-bot` workflow. In the `Install and run explosion-bot` step, towards the ends of the logs you should see info about the configuration that the bot was run with, as well as any errors that the bot encountered.
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ requires = [
|
|||
"cymem>=2.0.2,<2.1.0",
|
||||
"preshed>=3.0.2,<3.1.0",
|
||||
"murmurhash>=0.28.0,<1.1.0",
|
||||
"thinc>=8.1.0.dev3,<8.2.0",
|
||||
"thinc>=8.1.0,<8.2.0",
|
||||
"pathy",
|
||||
"numpy>=1.15.0",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ spacy-legacy>=3.0.9,<3.1.0
|
|||
spacy-loggers>=1.0.0,<2.0.0
|
||||
cymem>=2.0.2,<2.1.0
|
||||
preshed>=3.0.2,<3.1.0
|
||||
thinc>=8.1.0.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
|
||||
|
|
@ -30,7 +30,7 @@ pytest-timeout>=1.3.0,<2.0.0
|
|||
mock>=2.0.0,<3.0.0
|
||||
flake8>=3.8.0,<3.10.0
|
||||
hypothesis>=3.27.0,<7.0.0
|
||||
mypy>=0.910,<=0.960
|
||||
mypy>=0.910,<0.970; platform_machine!='aarch64'
|
||||
types-dataclasses>=0.1.3; python_version < "3.7"
|
||||
types-mock>=0.1.1
|
||||
types-requests
|
||||
|
|
|
|||
10
setup.cfg
10
setup.cfg
|
|
@ -38,7 +38,7 @@ setup_requires =
|
|||
cymem>=2.0.2,<2.1.0
|
||||
preshed>=3.0.2,<3.1.0
|
||||
murmurhash>=0.28.0,<1.1.0
|
||||
thinc>=8.1.0.dev3,<8.2.0
|
||||
thinc>=8.1.0,<8.2.0
|
||||
install_requires =
|
||||
# Our libraries
|
||||
spacy-legacy>=3.0.9,<3.1.0
|
||||
|
|
@ -46,7 +46,7 @@ install_requires =
|
|||
murmurhash>=0.28.0,<1.1.0
|
||||
cymem>=2.0.2,<2.1.0
|
||||
preshed>=3.0.2,<3.1.0
|
||||
thinc>=8.1.0.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
|
||||
|
|
@ -103,6 +103,10 @@ cuda114 =
|
|||
cupy-cuda114>=5.0.0b4,<11.0.0
|
||||
cuda115 =
|
||||
cupy-cuda115>=5.0.0b4,<11.0.0
|
||||
cuda116 =
|
||||
cupy-cuda116>=5.0.0b4,<11.0.0
|
||||
cuda117 =
|
||||
cupy-cuda117>=5.0.0b4,<11.0.0
|
||||
apple =
|
||||
thinc-apple-ops>=0.1.0.dev0,<1.0.0
|
||||
# Language tokenizers with external dependencies
|
||||
|
|
@ -110,7 +114,7 @@ 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
|
||||
|
||||
|
|
|
|||
8
setup.py
8
setup.py
|
|
@ -126,6 +126,8 @@ 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)
|
||||
|
||||
|
|
@ -206,7 +208,11 @@ def setup_package():
|
|||
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")
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# fmt: off
|
||||
__title__ = "spacy"
|
||||
__version__ = "3.4.0"
|
||||
__version__ = "3.4.1"
|
||||
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
|
||||
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
|
||||
__projects__ = "https://github.com/explosion/projects"
|
||||
|
|
|
|||
|
|
@ -462,6 +462,23 @@ def git_sparse_checkout(repo, subpath, dest, branch):
|
|||
shutil.move(str(source_path), str(dest))
|
||||
|
||||
|
||||
def git_repo_branch_exists(repo: str, branch: str) -> bool:
|
||||
"""Uses 'git ls-remote' to check if a repository and branch exists
|
||||
|
||||
repo (str): URL to get repo.
|
||||
branch (str): Branch on repo to check.
|
||||
RETURNS (bool): True if repo:branch exists.
|
||||
"""
|
||||
get_git_version()
|
||||
cmd = f"git ls-remote {repo} {branch}"
|
||||
# We might be tempted to use `--exit-code` with `git ls-remote`, but
|
||||
# `run_command` handles the `returncode` for us, so we'll rely on
|
||||
# the fact that stdout returns '' if the requested branch doesn't exist
|
||||
ret = run_command(cmd, capture=True)
|
||||
exists = ret.stdout != ""
|
||||
return exists
|
||||
|
||||
|
||||
def get_git_version(
|
||||
error: str = "Could not run 'git'. Make sure it's installed and the executable is available.",
|
||||
) -> Tuple[int, int]:
|
||||
|
|
|
|||
|
|
@ -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")
|
||||
|
|
|
|||
|
|
@ -7,11 +7,11 @@ import re
|
|||
from ... import about
|
||||
from ...util import ensure_path
|
||||
from .._util import project_cli, Arg, Opt, COMMAND, PROJECT_FILE
|
||||
from .._util import git_checkout, get_git_version
|
||||
from .._util import git_checkout, get_git_version, git_repo_branch_exists
|
||||
|
||||
DEFAULT_REPO = about.__projects__
|
||||
DEFAULT_PROJECTS_BRANCH = about.__projects_branch__
|
||||
DEFAULT_BRANCH = "master"
|
||||
DEFAULT_BRANCHES = ["main", "master"]
|
||||
|
||||
|
||||
@project_cli.command("clone")
|
||||
|
|
@ -20,7 +20,7 @@ def project_clone_cli(
|
|||
name: str = Arg(..., help="The name of the template to clone"),
|
||||
dest: Optional[Path] = Arg(None, help="Where to clone the project. Defaults to current working directory", exists=False),
|
||||
repo: str = Opt(DEFAULT_REPO, "--repo", "-r", help="The repository to clone from"),
|
||||
branch: Optional[str] = Opt(None, "--branch", "-b", help="The branch to clone from"),
|
||||
branch: Optional[str] = Opt(None, "--branch", "-b", help=f"The branch to clone from. If not provided, will attempt {', '.join(DEFAULT_BRANCHES)}"),
|
||||
sparse_checkout: bool = Opt(False, "--sparse", "-S", help="Use sparse Git checkout to only check out and clone the files needed. Requires Git v22.2+.")
|
||||
# fmt: on
|
||||
):
|
||||
|
|
@ -33,9 +33,25 @@ def project_clone_cli(
|
|||
"""
|
||||
if dest is None:
|
||||
dest = Path.cwd() / Path(name).parts[-1]
|
||||
if repo == DEFAULT_REPO and branch is None:
|
||||
branch = DEFAULT_PROJECTS_BRANCH
|
||||
|
||||
if branch is None:
|
||||
# If it's a user repo, we want to default to other branch
|
||||
branch = DEFAULT_PROJECTS_BRANCH if repo == DEFAULT_REPO else DEFAULT_BRANCH
|
||||
for default_branch in DEFAULT_BRANCHES:
|
||||
if git_repo_branch_exists(repo, default_branch):
|
||||
branch = default_branch
|
||||
break
|
||||
if branch is None:
|
||||
default_branches_msg = ", ".join(f"'{b}'" for b in DEFAULT_BRANCHES)
|
||||
msg.fail(
|
||||
"No branch provided and attempted default "
|
||||
f"branches {default_branches_msg} do not exist.",
|
||||
exits=1,
|
||||
)
|
||||
else:
|
||||
if not git_repo_branch_exists(repo, branch):
|
||||
msg.fail(f"repo: {repo} (branch: {branch}) does not exist.", exits=1)
|
||||
assert isinstance(branch, str)
|
||||
project_clone(name, dest, repo=repo, branch=branch, sparse_checkout=sparse_checkout)
|
||||
|
||||
|
||||
|
|
@ -61,9 +77,9 @@ def project_clone(
|
|||
try:
|
||||
git_checkout(repo, name, dest, branch=branch, sparse=sparse_checkout)
|
||||
except subprocess.CalledProcessError:
|
||||
err = f"Could not clone '{name}' from repo '{repo_name}'"
|
||||
err = f"Could not clone '{name}' from repo '{repo_name}' (branch '{branch}')"
|
||||
msg.fail(err, exits=1)
|
||||
msg.good(f"Cloned '{name}' from {repo_name}", project_dir)
|
||||
msg.good(f"Cloned '{name}' from '{repo_name}' (branch '{branch}')", project_dir)
|
||||
if not (project_dir / PROJECT_FILE).exists():
|
||||
msg.warn(f"No {PROJECT_FILE} found in directory")
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -64,8 +64,11 @@ class SpanRenderer:
|
|||
# Set up how the text and labels will be rendered
|
||||
self.direction = DEFAULT_DIR
|
||||
self.lang = DEFAULT_LANG
|
||||
# These values are in px
|
||||
self.top_offset = options.get("top_offset", 40)
|
||||
self.top_offset_step = options.get("top_offset_step", 17)
|
||||
# This is how far under the top offset the span labels appear
|
||||
self.span_label_offset = options.get("span_label_offset", 20)
|
||||
self.offset_step = options.get("top_offset_step", 17)
|
||||
|
||||
# Set up which templates will be used
|
||||
template = options.get("template")
|
||||
|
|
@ -127,26 +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:
|
||||
|
|
@ -157,12 +190,24 @@ 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 = (
|
||||
self.top_offset
|
||||
+ self.span_label_offset
|
||||
+ (self.offset_step * (len(entities) - 1))
|
||||
)
|
||||
markup += self.span_template.format(
|
||||
text=token["text"], span_slices=slices, span_starts=starts
|
||||
text=token["text"],
|
||||
span_slices=slices,
|
||||
span_starts=starts,
|
||||
total_height=total_height,
|
||||
)
|
||||
else:
|
||||
markup += escape_html(token["text"] + " ")
|
||||
|
|
@ -171,10 +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.top_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)
|
||||
|
|
@ -182,12 +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.top_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"],
|
||||
)
|
||||
|
|
|
|||
|
|
@ -67,7 +67,7 @@ TPL_SPANS = """
|
|||
"""
|
||||
|
||||
TPL_SPAN = """
|
||||
<span style="font-weight: bold; display: inline-block; position: relative;">
|
||||
<span style="font-weight: bold; display: inline-block; position: relative; height: {total_height}px;">
|
||||
{text}
|
||||
{span_slices}
|
||||
{span_starts}
|
||||
|
|
|
|||
|
|
@ -209,6 +209,9 @@ class Warnings(metaclass=ErrorsWithCodes):
|
|||
"Only the last span group will be loaded under "
|
||||
"Doc.spans['{group_name}']. Skipping span group with values: "
|
||||
"{group_values}")
|
||||
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.")
|
||||
|
||||
|
||||
class Errors(metaclass=ErrorsWithCodes):
|
||||
|
|
@ -934,6 +937,8 @@ class Errors(metaclass=ErrorsWithCodes):
|
|||
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.")
|
||||
E1043 = ("Expected None or a value in range [{range_start}, {range_end}] for entity linker threshold, but got "
|
||||
"{value}.")
|
||||
|
||||
|
||||
# Deprecated model shortcuts, only used in errors and warnings
|
||||
|
|
|
|||
|
|
@ -2,7 +2,8 @@ from .stop_words import STOP_WORDS
|
|||
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
|
||||
from .lex_attrs import LEX_ATTRS
|
||||
from ..tokenizer_exceptions import BASE_EXCEPTIONS
|
||||
|
||||
from ..punctuation import COMBINING_DIACRITICS_TOKENIZER_INFIXES
|
||||
from ..punctuation import COMBINING_DIACRITICS_TOKENIZER_SUFFIXES
|
||||
from ...language import Language, BaseDefaults
|
||||
from ...attrs import LANG
|
||||
from ...util import update_exc
|
||||
|
|
@ -16,6 +17,8 @@ class BulgarianDefaults(BaseDefaults):
|
|||
|
||||
stop_words = STOP_WORDS
|
||||
tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
|
||||
suffixes = COMBINING_DIACRITICS_TOKENIZER_SUFFIXES
|
||||
infixes = COMBINING_DIACRITICS_TOKENIZER_INFIXES
|
||||
|
||||
|
||||
class Bulgarian(Language):
|
||||
|
|
|
|||
|
|
@ -258,6 +258,10 @@ ALPHA = group_chars(
|
|||
ALPHA_LOWER = group_chars(_lower + _uncased)
|
||||
ALPHA_UPPER = group_chars(_upper + _uncased)
|
||||
|
||||
_combining_diacritics = r"\u0300-\u036f"
|
||||
|
||||
COMBINING_DIACRITICS = _combining_diacritics
|
||||
|
||||
_units = (
|
||||
"km km² km³ m m² m³ dm dm² dm³ cm cm² cm³ mm mm² mm³ ha µm nm yd in ft "
|
||||
"kg g mg µg t lb oz m/s km/h kmh mph hPa Pa mbar mb MB kb KB gb GB tb "
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
from .char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES, LIST_CURRENCY
|
||||
from .char_classes import LIST_ICONS, HYPHENS, CURRENCY, UNITS
|
||||
from .char_classes import LIST_ICONS, HYPHENS, CURRENCY, UNITS, COMBINING_DIACRITICS
|
||||
from .char_classes import CONCAT_QUOTES, ALPHA_LOWER, ALPHA_UPPER, ALPHA, PUNCT
|
||||
|
||||
|
||||
|
|
@ -44,3 +44,23 @@ TOKENIZER_INFIXES = (
|
|||
r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
# Some languages e.g. written with the Cyrillic alphabet permit the use of diacritics
|
||||
# to mark stressed syllables in words where stress is distinctive. Such languages
|
||||
# should use the COMBINING_DIACRITICS... suffix and infix regex lists in
|
||||
# place of the standard ones.
|
||||
COMBINING_DIACRITICS_TOKENIZER_SUFFIXES = list(TOKENIZER_SUFFIXES) + [
|
||||
r"(?<=[{a}][{d}])\.".format(a=ALPHA, d=COMBINING_DIACRITICS),
|
||||
]
|
||||
|
||||
COMBINING_DIACRITICS_TOKENIZER_INFIXES = list(TOKENIZER_INFIXES) + [
|
||||
r"(?<=[{al}][{d}])\.(?=[{au}{q}])".format(
|
||||
al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES, d=COMBINING_DIACRITICS
|
||||
),
|
||||
r"(?<=[{a}][{d}]),(?=[{a}])".format(a=ALPHA, d=COMBINING_DIACRITICS),
|
||||
r"(?<=[{a}][{d}])(?:{h})(?=[{a}])".format(
|
||||
a=ALPHA, d=COMBINING_DIACRITICS, h=HYPHENS
|
||||
),
|
||||
r"(?<=[{a}][{d}])[:<>=/](?=[{a}])".format(a=ALPHA, d=COMBINING_DIACRITICS),
|
||||
]
|
||||
|
|
|
|||
|
|
@ -5,6 +5,8 @@ from .stop_words import STOP_WORDS
|
|||
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
|
||||
from .lex_attrs import LEX_ATTRS
|
||||
from .lemmatizer import RussianLemmatizer
|
||||
from ..punctuation import COMBINING_DIACRITICS_TOKENIZER_INFIXES
|
||||
from ..punctuation import COMBINING_DIACRITICS_TOKENIZER_SUFFIXES
|
||||
from ...language import Language, BaseDefaults
|
||||
|
||||
|
||||
|
|
@ -12,6 +14,8 @@ class RussianDefaults(BaseDefaults):
|
|||
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
|
||||
lex_attr_getters = LEX_ATTRS
|
||||
stop_words = STOP_WORDS
|
||||
suffixes = COMBINING_DIACRITICS_TOKENIZER_SUFFIXES
|
||||
infixes = COMBINING_DIACRITICS_TOKENIZER_INFIXES
|
||||
|
||||
|
||||
class Russian(Language):
|
||||
|
|
|
|||
|
|
@ -6,6 +6,8 @@ from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
|
|||
from .stop_words import STOP_WORDS
|
||||
from .lex_attrs import LEX_ATTRS
|
||||
from .lemmatizer import UkrainianLemmatizer
|
||||
from ..punctuation import COMBINING_DIACRITICS_TOKENIZER_INFIXES
|
||||
from ..punctuation import COMBINING_DIACRITICS_TOKENIZER_SUFFIXES
|
||||
from ...language import Language, BaseDefaults
|
||||
|
||||
|
||||
|
|
@ -13,6 +15,8 @@ class UkrainianDefaults(BaseDefaults):
|
|||
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
|
||||
lex_attr_getters = LEX_ATTRS
|
||||
stop_words = STOP_WORDS
|
||||
suffixes = COMBINING_DIACRITICS_TOKENIZER_SUFFIXES
|
||||
infixes = COMBINING_DIACRITICS_TOKENIZER_INFIXES
|
||||
|
||||
|
||||
class Ukrainian(Language):
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -86,10 +86,14 @@ cdef class Matcher:
|
|||
is a dictionary mapping attribute IDs to values, and optionally a
|
||||
quantifier operator under the key "op". The available quantifiers are:
|
||||
|
||||
'!': Negate the pattern, by requiring it to match exactly 0 times.
|
||||
'?': Make the pattern optional, by allowing it to match 0 or 1 times.
|
||||
'+': Require the pattern to match 1 or more times.
|
||||
'*': Allow the pattern to zero or more times.
|
||||
'!': Negate the pattern, by requiring it to match exactly 0 times.
|
||||
'?': Make the pattern optional, by allowing it to match 0 or 1 times.
|
||||
'+': Require the pattern to match 1 or more times.
|
||||
'*': Allow the pattern to zero or more times.
|
||||
'{n}': Require the pattern to match exactly _n_ times.
|
||||
'{n,m}': Require the pattern to match at least _n_ but not more than _m_ times.
|
||||
'{n,}': Require the pattern to match at least _n_ times.
|
||||
'{,m}': Require the pattern to match at most _m_ times.
|
||||
|
||||
The + and * operators return all possible matches (not just the greedy
|
||||
ones). However, the "greedy" argument can filter the final matches
|
||||
|
|
@ -1005,8 +1009,29 @@ def _get_operators(spec):
|
|||
return (ONE,)
|
||||
elif spec["OP"] in lookup:
|
||||
return lookup[spec["OP"]]
|
||||
#Min_max {n,m}
|
||||
elif spec["OP"].startswith("{") and spec["OP"].endswith("}"):
|
||||
# {n} --> {n,n} exactly n ONE,(n)
|
||||
# {n,m}--> {n,m} min of n, max of m ONE,(n),ZERO_ONE,(m)
|
||||
# {,m} --> {0,m} min of zero, max of m ZERO_ONE,(m)
|
||||
# {n,} --> {n,∞} min of n, max of inf ONE,(n),ZERO_PLUS
|
||||
|
||||
min_max = spec["OP"][1:-1]
|
||||
min_max = min_max if "," in min_max else f"{min_max},{min_max}"
|
||||
n, m = min_max.split(",")
|
||||
|
||||
#1. Either n or m is a blank string and the other is numeric -->isdigit
|
||||
#2. Both are numeric and n <= m
|
||||
if (not n.isdecimal() and not m.isdecimal()) or (n.isdecimal() and m.isdecimal() and int(n) > int(m)):
|
||||
keys = ", ".join(lookup.keys()) + ", {n}, {n,m}, {n,}, {,m} where n and m are integers and n <= m "
|
||||
raise ValueError(Errors.E011.format(op=spec["OP"], opts=keys))
|
||||
|
||||
# if n is empty string, zero would be used
|
||||
head = tuple(ONE for __ in range(int(n or 0)))
|
||||
tail = tuple(ZERO_ONE for __ in range(int(m) - int(n or 0))) if m else (ZERO_PLUS,)
|
||||
return head + tail
|
||||
else:
|
||||
keys = ", ".join(lookup.keys())
|
||||
keys = ", ".join(lookup.keys()) + ", {n}, {n,m}, {n,}, {,m} where n and m are integers and n <= m "
|
||||
raise ValueError(Errors.E011.format(op=spec["OP"], opts=keys))
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -1,9 +1,14 @@
|
|||
from functools import partial
|
||||
from typing import Type, Callable, TYPE_CHECKING
|
||||
from typing import Type, Callable, Dict, TYPE_CHECKING, List, Optional, Set
|
||||
import functools
|
||||
import inspect
|
||||
import types
|
||||
import warnings
|
||||
|
||||
from thinc.layers import with_nvtx_range
|
||||
from thinc.model import Model, wrap_model_recursive
|
||||
from thinc.util import use_nvtx_range
|
||||
|
||||
from ..errors import Warnings
|
||||
from ..util import registry
|
||||
|
||||
if TYPE_CHECKING:
|
||||
|
|
@ -11,29 +16,106 @@ if TYPE_CHECKING:
|
|||
from ..language import Language # noqa: F401
|
||||
|
||||
|
||||
@registry.callbacks("spacy.models_with_nvtx_range.v1")
|
||||
def create_models_with_nvtx_range(
|
||||
forward_color: int = -1, backprop_color: int = -1
|
||||
) -> Callable[["Language"], "Language"]:
|
||||
def models_with_nvtx_range(nlp):
|
||||
pipes = [
|
||||
pipe
|
||||
for _, pipe in nlp.components
|
||||
if hasattr(pipe, "is_trainable") and pipe.is_trainable
|
||||
]
|
||||
DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS = [
|
||||
"pipe",
|
||||
"predict",
|
||||
"set_annotations",
|
||||
"update",
|
||||
"rehearse",
|
||||
"get_loss",
|
||||
"initialize",
|
||||
"begin_update",
|
||||
"finish_update",
|
||||
"update",
|
||||
]
|
||||
|
||||
# We need process all models jointly to avoid wrapping callbacks twice.
|
||||
models = Model(
|
||||
"wrap_with_nvtx_range",
|
||||
forward=lambda model, X, is_train: ...,
|
||||
layers=[pipe.model for pipe in pipes],
|
||||
)
|
||||
|
||||
for node in models.walk():
|
||||
def models_with_nvtx_range(nlp, forward_color: int, backprop_color: int):
|
||||
pipes = [
|
||||
pipe
|
||||
for _, pipe in nlp.components
|
||||
if hasattr(pipe, "is_trainable") and pipe.is_trainable
|
||||
]
|
||||
|
||||
seen_models: Set[int] = set()
|
||||
for pipe in pipes:
|
||||
for node in pipe.model.walk():
|
||||
if id(node) in seen_models:
|
||||
continue
|
||||
seen_models.add(id(node))
|
||||
with_nvtx_range(
|
||||
node, forward_color=forward_color, backprop_color=backprop_color
|
||||
)
|
||||
|
||||
return nlp
|
||||
|
||||
|
||||
@registry.callbacks("spacy.models_with_nvtx_range.v1")
|
||||
def create_models_with_nvtx_range(
|
||||
forward_color: int = -1, backprop_color: int = -1
|
||||
) -> Callable[["Language"], "Language"]:
|
||||
return functools.partial(
|
||||
models_with_nvtx_range,
|
||||
forward_color=forward_color,
|
||||
backprop_color=backprop_color,
|
||||
)
|
||||
|
||||
|
||||
def nvtx_range_wrapper_for_pipe_method(self, func, *args, **kwargs):
|
||||
if isinstance(func, functools.partial):
|
||||
return func(*args, **kwargs)
|
||||
else:
|
||||
with use_nvtx_range(f"{self.name} {func.__name__}"):
|
||||
return func(*args, **kwargs)
|
||||
|
||||
|
||||
def pipes_with_nvtx_range(
|
||||
nlp, additional_pipe_functions: Optional[Dict[str, List[str]]]
|
||||
):
|
||||
for _, pipe in nlp.components:
|
||||
if additional_pipe_functions:
|
||||
extra_funcs = additional_pipe_functions.get(pipe.name, [])
|
||||
else:
|
||||
extra_funcs = []
|
||||
|
||||
for name in DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS + extra_funcs:
|
||||
func = getattr(pipe, name, None)
|
||||
if func is None:
|
||||
if name in extra_funcs:
|
||||
warnings.warn(Warnings.W121.format(method=name, pipe=pipe.name))
|
||||
continue
|
||||
|
||||
wrapped_func = functools.partial(
|
||||
types.MethodType(nvtx_range_wrapper_for_pipe_method, pipe), func
|
||||
)
|
||||
|
||||
# Try to preserve the original function signature.
|
||||
try:
|
||||
wrapped_func.__signature__ = inspect.signature(func) # type: ignore
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
setattr(
|
||||
pipe,
|
||||
name,
|
||||
wrapped_func,
|
||||
)
|
||||
except AttributeError:
|
||||
warnings.warn(Warnings.W122.format(method=name, pipe=pipe.name))
|
||||
|
||||
return nlp
|
||||
|
||||
|
||||
@registry.callbacks("spacy.models_and_pipes_with_nvtx_range.v1")
|
||||
def create_models_and_pipes_with_nvtx_range(
|
||||
forward_color: int = -1,
|
||||
backprop_color: int = -1,
|
||||
additional_pipe_functions: Optional[Dict[str, List[str]]] = None,
|
||||
) -> Callable[["Language"], "Language"]:
|
||||
def inner(nlp):
|
||||
nlp = models_with_nvtx_range(nlp, forward_color, backprop_color)
|
||||
nlp = pipes_with_nvtx_range(nlp, additional_pipe_functions)
|
||||
return nlp
|
||||
|
||||
return models_with_nvtx_range
|
||||
return inner
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
||||
|
|
|
|||
|
|
@ -56,6 +56,7 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
|
|||
"overwrite": True,
|
||||
"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
|
||||
"use_gold_ents": True,
|
||||
"threshold": None,
|
||||
},
|
||||
default_score_weights={
|
||||
"nel_micro_f": 1.0,
|
||||
|
|
@ -77,6 +78,7 @@ def make_entity_linker(
|
|||
overwrite: bool,
|
||||
scorer: Optional[Callable],
|
||||
use_gold_ents: bool,
|
||||
threshold: Optional[float] = None,
|
||||
):
|
||||
"""Construct an EntityLinker component.
|
||||
|
||||
|
|
@ -91,6 +93,10 @@ def make_entity_linker(
|
|||
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.
|
||||
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
|
||||
component must provide entity annotations.
|
||||
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):
|
||||
|
|
@ -121,6 +127,7 @@ def make_entity_linker(
|
|||
overwrite=overwrite,
|
||||
scorer=scorer,
|
||||
use_gold_ents=use_gold_ents,
|
||||
threshold=threshold,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -156,6 +163,7 @@ class EntityLinker(TrainablePipe):
|
|||
overwrite: bool = BACKWARD_OVERWRITE,
|
||||
scorer: Optional[Callable] = entity_linker_score,
|
||||
use_gold_ents: bool,
|
||||
threshold: Optional[float] = None,
|
||||
) -> None:
|
||||
"""Initialize an entity linker.
|
||||
|
||||
|
|
@ -174,9 +182,20 @@ class EntityLinker(TrainablePipe):
|
|||
Scorer.score_links.
|
||||
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
|
||||
component must provide entity annotations.
|
||||
|
||||
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
|
||||
|
|
@ -192,6 +211,7 @@ class EntityLinker(TrainablePipe):
|
|||
self.kb = empty_kb(entity_vector_length)(self.vocab)
|
||||
self.scorer = scorer
|
||||
self.use_gold_ents = use_gold_ents
|
||||
self.threshold = threshold
|
||||
|
||||
def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
|
||||
"""Define the KB of this pipe by providing a function that will
|
||||
|
|
@ -424,9 +444,8 @@ class EntityLinker(TrainablePipe):
|
|||
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 +474,11 @@ 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"
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
@ -70,7 +70,6 @@ class EntityLinker_v1(TrainablePipe):
|
|||
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.
|
||||
|
||||
DOCS: https://spacy.io/api/entitylinker#init
|
||||
"""
|
||||
self.vocab = vocab
|
||||
|
|
@ -272,7 +271,6 @@ 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 +299,6 @@ 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_)
|
||||
|
|
|
|||
|
|
@ -192,7 +192,7 @@ 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)
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -3,12 +3,13 @@ from typing import Iterable, TypeVar, TYPE_CHECKING
|
|||
from .compat import Literal
|
||||
from enum import Enum
|
||||
from pydantic import BaseModel, Field, ValidationError, validator, create_model
|
||||
from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool
|
||||
from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool, ConstrainedStr
|
||||
from pydantic.main import ModelMetaclass
|
||||
from thinc.api import Optimizer, ConfigValidationError, Model
|
||||
from thinc.config import Promise
|
||||
from collections import defaultdict
|
||||
import inspect
|
||||
import re
|
||||
|
||||
from .attrs import NAMES
|
||||
from .lookups import Lookups
|
||||
|
|
@ -198,13 +199,18 @@ class TokenPatternNumber(BaseModel):
|
|||
return v
|
||||
|
||||
|
||||
class TokenPatternOperator(str, Enum):
|
||||
class TokenPatternOperatorSimple(str, Enum):
|
||||
plus: StrictStr = StrictStr("+")
|
||||
start: StrictStr = StrictStr("*")
|
||||
star: StrictStr = StrictStr("*")
|
||||
question: StrictStr = StrictStr("?")
|
||||
exclamation: StrictStr = StrictStr("!")
|
||||
|
||||
|
||||
class TokenPatternOperatorMinMax(ConstrainedStr):
|
||||
regex = re.compile("^({\d+}|{\d+,\d*}|{\d*,\d+})$")
|
||||
|
||||
|
||||
TokenPatternOperator = Union[TokenPatternOperatorSimple, TokenPatternOperatorMinMax]
|
||||
StringValue = Union[TokenPatternString, StrictStr]
|
||||
NumberValue = Union[TokenPatternNumber, StrictInt, StrictFloat]
|
||||
UnderscoreValue = Union[
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
return 0
|
||||
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:
|
||||
chars = key.encode("utf8")
|
||||
return hash_utf8(chars, len(chars))
|
||||
# TODO: Raise an error instead
|
||||
return key
|
||||
|
||||
|
||||
cpdef hash_t hash_string(str string) except 0:
|
||||
|
|
@ -110,28 +129,36 @@ 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]
|
||||
else:
|
||||
key = string_or_id
|
||||
utf8str = <Utf8Str*>self._map.get(key)
|
||||
if utf8str is NULL:
|
||||
raise KeyError(Errors.E018.format(hash_value=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:
|
||||
return decode_Utf8Str(utf8str)
|
||||
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:
|
||||
return decode_Utf8Str(utf8str)
|
||||
|
||||
def as_int(self, key):
|
||||
"""If key is an int, return it; otherwise, get the int value."""
|
||||
|
|
@ -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:
|
||||
return True
|
||||
elif string in SYMBOLS_BY_STR:
|
||||
return True
|
||||
elif isinstance(string, str):
|
||||
key = hash_string(string)
|
||||
elif string_or_id in SYMBOLS_BY_STR:
|
||||
return True
|
||||
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
|
||||
|
|
|
|||
|
|
@ -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):
|
||||
|
|
|
|||
8
spacy/tests/lang/bg/test_tokenizer.py
Normal file
8
spacy/tests/lang/bg/test_tokenizer.py
Normal file
|
|
@ -0,0 +1,8 @@
|
|||
import pytest
|
||||
|
||||
|
||||
def test_bg_tokenizer_handles_final_diacritics(bg_tokenizer):
|
||||
text = "Ня̀маше яйца̀. Ня̀маше яйца̀."
|
||||
tokens = bg_tokenizer(text)
|
||||
assert tokens[1].text == "яйца̀"
|
||||
assert tokens[2].text == "."
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
from string import punctuation
|
||||
import pytest
|
||||
|
||||
|
||||
|
|
@ -122,3 +123,36 @@ def test_ru_tokenizer_splits_bracket_period(ru_tokenizer):
|
|||
text = "(Раз, два, три, проверка)."
|
||||
tokens = ru_tokenizer(text)
|
||||
assert tokens[len(tokens) - 1].text == "."
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"text",
|
||||
[
|
||||
"рекоменду́я подда́ть жару́. Самого́ Баргамота",
|
||||
"РЕКОМЕНДУ́Я ПОДДА́ТЬ ЖАРУ́. САМОГО́ БАРГАМОТА",
|
||||
"рекоменду̍я подда̍ть жару̍.Самого̍ Баргамота",
|
||||
"рекоменду̍я подда̍ть жару̍.'Самого̍ Баргамота",
|
||||
"рекоменду̍я подда̍ть жару̍,самого̍ Баргамота",
|
||||
"рекоменду̍я подда̍ть жару̍:самого̍ Баргамота",
|
||||
"рекоменду̍я подда̍ть жару̍. самого̍ Баргамота",
|
||||
"рекоменду̍я подда̍ть жару̍, самого̍ Баргамота",
|
||||
"рекоменду̍я подда̍ть жару̍: самого̍ Баргамота",
|
||||
"рекоменду̍я подда̍ть жару̍-самого̍ Баргамота",
|
||||
],
|
||||
)
|
||||
def test_ru_tokenizer_handles_final_diacritics(ru_tokenizer, text):
|
||||
tokens = ru_tokenizer(text)
|
||||
assert tokens[2].text in ("жару́", "ЖАРУ́", "жару̍")
|
||||
assert tokens[3].text in punctuation
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"text",
|
||||
[
|
||||
"РЕКОМЕНДУ́Я ПОДДА́ТЬ ЖАРУ́.САМОГО́ БАРГАМОТА",
|
||||
"рекоменду̍я подда̍ть жару́.самого́ Баргамота",
|
||||
],
|
||||
)
|
||||
def test_ru_tokenizer_handles_final_diacritic_and_period(ru_tokenizer, text):
|
||||
tokens = ru_tokenizer(text)
|
||||
assert tokens[2].text.lower() == "жару́.самого́"
|
||||
|
|
|
|||
|
|
@ -140,3 +140,10 @@ def test_uk_tokenizer_splits_bracket_period(uk_tokenizer):
|
|||
text = "(Раз, два, три, проверка)."
|
||||
tokens = uk_tokenizer(text)
|
||||
assert tokens[len(tokens) - 1].text == "."
|
||||
|
||||
|
||||
def test_uk_tokenizer_handles_final_diacritics(uk_tokenizer):
|
||||
text = "Хлібі́в не було́. Хлібі́в не було́."
|
||||
tokens = uk_tokenizer(text)
|
||||
assert tokens[2].text == "було́"
|
||||
assert tokens[3].text == "."
|
||||
|
|
|
|||
|
|
@ -316,6 +316,20 @@ def test_dependency_matcher_precedence_ops(en_vocab, op, num_matches):
|
|||
("the", "brown", "$--", 0),
|
||||
("brown", "the", "$--", 1),
|
||||
("brown", "brown", "$--", 0),
|
||||
("quick", "fox", "<++", 1),
|
||||
("quick", "over", "<++", 0),
|
||||
("over", "jumped", "<++", 0),
|
||||
("the", "fox", "<++", 2),
|
||||
("brown", "fox", "<--", 0),
|
||||
("fox", "jumped", "<--", 0),
|
||||
("fox", "over", "<--", 1),
|
||||
("jumped", "over", ">++", 1),
|
||||
("fox", "lazy", ">++", 0),
|
||||
("over", "the", ">++", 0),
|
||||
("brown", "fox", ">--", 0),
|
||||
("fox", "brown", ">--", 1),
|
||||
("jumped", "fox", ">--", 1),
|
||||
("fox", "the", ">--", 2),
|
||||
],
|
||||
)
|
||||
def test_dependency_matcher_ops(en_vocab, doc, left, right, op, num_matches):
|
||||
|
|
|
|||
|
|
@ -687,3 +687,38 @@ def test_matcher_ent_iob_key(en_vocab):
|
|||
assert matches[0] == "Maria"
|
||||
assert matches[1] == "Maria Esperanza"
|
||||
assert matches[2] == "Esperanza"
|
||||
|
||||
|
||||
def test_matcher_min_max_operator(en_vocab):
|
||||
# Exactly n matches {n}
|
||||
doc = Doc(
|
||||
en_vocab,
|
||||
words=["foo", "bar", "foo", "foo", "bar", "foo", "foo", "foo", "bar", "bar"],
|
||||
)
|
||||
matcher = Matcher(en_vocab)
|
||||
pattern = [{"ORTH": "foo", "OP": "{3}"}]
|
||||
matcher.add("TEST", [pattern])
|
||||
|
||||
matches1 = [doc[start:end].text for _, start, end in matcher(doc)]
|
||||
assert len(matches1) == 1
|
||||
|
||||
# At least n matches {n,}
|
||||
matcher = Matcher(en_vocab)
|
||||
pattern = [{"ORTH": "foo", "OP": "{2,}"}]
|
||||
matcher.add("TEST", [pattern])
|
||||
matches2 = [doc[start:end].text for _, start, end in matcher(doc)]
|
||||
assert len(matches2) == 4
|
||||
|
||||
# At most m matches {,m}
|
||||
matcher = Matcher(en_vocab)
|
||||
pattern = [{"ORTH": "foo", "OP": "{,2}"}]
|
||||
matcher.add("TEST", [pattern])
|
||||
matches3 = [doc[start:end].text for _, start, end in matcher(doc)]
|
||||
assert len(matches3) == 9
|
||||
|
||||
# At least n matches and most m matches {n,m}
|
||||
matcher = Matcher(en_vocab)
|
||||
pattern = [{"ORTH": "foo", "OP": "{2,3}"}]
|
||||
matcher.add("TEST", [pattern])
|
||||
matches4 = [doc[start:end].text for _, start, end in matcher(doc)]
|
||||
assert len(matches4) == 4
|
||||
|
|
|
|||
|
|
@ -699,6 +699,10 @@ def test_matcher_with_alignments_greedy_longest(en_vocab):
|
|||
("aaaa", "a a a a a?", [0, 1, 2, 3]),
|
||||
("aaab", "a+ a b", [0, 0, 1, 2]),
|
||||
("aaab", "a+ a+ b", [0, 0, 1, 2]),
|
||||
("aaab", "a{2,} b", [0, 0, 0, 1]),
|
||||
("aaab", "a{,3} b", [0, 0, 0, 1]),
|
||||
("aaab", "a{2} b", [0, 0, 1]),
|
||||
("aaab", "a{2,3} b", [0, 0, 0, 1]),
|
||||
]
|
||||
for string, pattern_str, result in cases:
|
||||
matcher = Matcher(en_vocab)
|
||||
|
|
@ -711,6 +715,8 @@ def test_matcher_with_alignments_greedy_longest(en_vocab):
|
|||
pattern.append({"ORTH": part[0], "OP": "*"})
|
||||
elif part.endswith("?"):
|
||||
pattern.append({"ORTH": part[0], "OP": "?"})
|
||||
elif part.endswith("}"):
|
||||
pattern.append({"ORTH": part[0], "OP": part[1:]})
|
||||
else:
|
||||
pattern.append({"ORTH": part})
|
||||
matcher.add("PATTERN", [pattern], greedy="LONGEST")
|
||||
|
|
@ -722,7 +728,7 @@ def test_matcher_with_alignments_greedy_longest(en_vocab):
|
|||
assert expected == result, (string, pattern_str, s, e, n_matches)
|
||||
|
||||
|
||||
def test_matcher_with_alignments_nongreedy(en_vocab):
|
||||
def test_matcher_with_alignments_non_greedy(en_vocab):
|
||||
cases = [
|
||||
(0, "aaab", "a* b", [[0, 1], [0, 0, 1], [0, 0, 0, 1], [1]]),
|
||||
(1, "baab", "b a* b", [[0, 1, 1, 2]]),
|
||||
|
|
@ -752,6 +758,10 @@ def test_matcher_with_alignments_nongreedy(en_vocab):
|
|||
(15, "aaaa", "a a a a a?", [[0, 1, 2, 3]]),
|
||||
(16, "aaab", "a+ a b", [[0, 1, 2], [0, 0, 1, 2]]),
|
||||
(17, "aaab", "a+ a+ b", [[0, 1, 2], [0, 0, 1, 2]]),
|
||||
(18, "aaab", "a{2,} b", [[0, 0, 1], [0, 0, 0, 1]]),
|
||||
(19, "aaab", "a{3} b", [[0, 0, 0, 1]]),
|
||||
(20, "aaab", "a{2} b", [[0, 0, 1]]),
|
||||
(21, "aaab", "a{2,3} b", [[0, 0, 1], [0, 0, 0, 1]]),
|
||||
]
|
||||
for case_id, string, pattern_str, results in cases:
|
||||
matcher = Matcher(en_vocab)
|
||||
|
|
@ -764,6 +774,8 @@ def test_matcher_with_alignments_nongreedy(en_vocab):
|
|||
pattern.append({"ORTH": part[0], "OP": "*"})
|
||||
elif part.endswith("?"):
|
||||
pattern.append({"ORTH": part[0], "OP": "?"})
|
||||
elif part.endswith("}"):
|
||||
pattern.append({"ORTH": part[0], "OP": part[1:]})
|
||||
else:
|
||||
pattern.append({"ORTH": part})
|
||||
|
||||
|
|
|
|||
|
|
@ -14,6 +14,14 @@ TEST_PATTERNS = [
|
|||
('[{"TEXT": "foo"}, {"LOWER": "bar"}]', 1, 1),
|
||||
([{"ENT_IOB": "foo"}], 1, 1),
|
||||
([1, 2, 3], 3, 1),
|
||||
([{"TEXT": "foo", "OP": "{,}"}], 1, 1),
|
||||
([{"TEXT": "foo", "OP": "{,4}4"}], 1, 1),
|
||||
([{"TEXT": "foo", "OP": "{a,3}"}], 1, 1),
|
||||
([{"TEXT": "foo", "OP": "{a}"}], 1, 1),
|
||||
([{"TEXT": "foo", "OP": "{,a}"}], 1, 1),
|
||||
([{"TEXT": "foo", "OP": "{1,2,3}"}], 1, 1),
|
||||
([{"TEXT": "foo", "OP": "{1, 3}"}], 1, 1),
|
||||
([{"TEXT": "foo", "OP": "{-2}"}], 1, 1),
|
||||
# Bad patterns flagged outside of Matcher
|
||||
([{"_": {"foo": "bar", "baz": {"IN": "foo"}}}], 2, 0), # prev: (1, 0)
|
||||
# Bad patterns not flagged with minimal checks
|
||||
|
|
@ -38,6 +46,7 @@ TEST_PATTERNS = [
|
|||
([{"SENT_START": True}], 0, 0),
|
||||
([{"ENT_ID": "STRING"}], 0, 0),
|
||||
([{"ENT_KB_ID": "STRING"}], 0, 0),
|
||||
([{"TEXT": "ha", "OP": "{3}"}], 0, 0),
|
||||
]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from typing import Callable, Iterable
|
||||
from typing import Callable, Iterable, Dict, Any
|
||||
|
||||
import pytest
|
||||
from numpy.testing import assert_equal
|
||||
|
|
@ -207,7 +207,7 @@ def test_no_entities():
|
|||
nlp.add_pipe("sentencizer", first=True)
|
||||
|
||||
# this will run the pipeline on the examples and shouldn't crash
|
||||
results = nlp.evaluate(train_examples)
|
||||
nlp.evaluate(train_examples)
|
||||
|
||||
|
||||
def test_partial_links():
|
||||
|
|
@ -1063,7 +1063,7 @@ def test_no_gold_ents(patterns):
|
|||
"entity_linker", config={"use_gold_ents": False}, last=True
|
||||
)
|
||||
entity_linker.set_kb(create_kb)
|
||||
assert entity_linker.use_gold_ents == False
|
||||
assert entity_linker.use_gold_ents is False
|
||||
|
||||
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
||||
for i in range(2):
|
||||
|
|
@ -1074,7 +1074,7 @@ def test_no_gold_ents(patterns):
|
|||
nlp.add_pipe("sentencizer", first=True)
|
||||
|
||||
# this will run the pipeline on the examples and shouldn't crash
|
||||
results = nlp.evaluate(train_examples)
|
||||
nlp.evaluate(train_examples)
|
||||
|
||||
|
||||
@pytest.mark.issue(9575)
|
||||
|
|
@ -1114,4 +1114,61 @@ def test_tokenization_mismatch():
|
|||
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
||||
|
||||
nlp.add_pipe("sentencizer", first=True)
|
||||
results = nlp.evaluate(train_examples)
|
||||
nlp.evaluate(train_examples)
|
||||
|
||||
|
||||
# fmt: off
|
||||
@pytest.mark.parametrize(
|
||||
"meet_threshold,config",
|
||||
[
|
||||
(False, {"@architectures": "spacy.EntityLinker.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL}),
|
||||
(True, {"@architectures": "spacy.EntityLinker.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL}),
|
||||
],
|
||||
)
|
||||
# fmt: on
|
||||
def test_threshold(meet_threshold: bool, config: Dict[str, Any]):
|
||||
"""Tests abstention threshold.
|
||||
meet_threshold (bool): Whether to configure NEL setup so that confidence threshold is met.
|
||||
config (Dict[str, Any]): NEL architecture config.
|
||||
"""
|
||||
nlp = English()
|
||||
nlp.add_pipe("sentencizer")
|
||||
text = "Mahler's Symphony No. 8 was beautiful."
|
||||
entities = [(0, 6, "PERSON")]
|
||||
links = {(0, 6): {"Q7304": 1.0}}
|
||||
sent_starts = [1, -1, 0, 0, 0, 0, 0, 0, 0]
|
||||
entity_id = "Q7304"
|
||||
doc = nlp(text)
|
||||
train_examples = [
|
||||
Example.from_dict(
|
||||
doc, {"entities": entities, "links": links, "sent_starts": sent_starts}
|
||||
)
|
||||
]
|
||||
|
||||
def create_kb(vocab):
|
||||
# create artificial KB
|
||||
mykb = KnowledgeBase(vocab, entity_vector_length=3)
|
||||
mykb.add_entity(entity=entity_id, freq=12, entity_vector=[6, -4, 3])
|
||||
mykb.add_alias(
|
||||
alias="Mahler",
|
||||
entities=[entity_id],
|
||||
probabilities=[1 if meet_threshold else 0.01],
|
||||
)
|
||||
return mykb
|
||||
|
||||
# Create the Entity Linker component and add it to the pipeline
|
||||
entity_linker = nlp.add_pipe(
|
||||
"entity_linker",
|
||||
last=True,
|
||||
config={"threshold": 0.99, "model": config},
|
||||
)
|
||||
entity_linker.set_kb(create_kb) # type: ignore
|
||||
nlp.initialize(get_examples=lambda: train_examples)
|
||||
|
||||
# Add a custom rule-based component to mimick NER
|
||||
ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
|
||||
ruler.add_patterns([{"label": "PERSON", "pattern": [{"LOWER": "mahler"}]}]) # type: ignore
|
||||
doc = nlp(text)
|
||||
|
||||
assert len(doc.ents) == 1
|
||||
assert doc.ents[0].kb_id_ == entity_id if meet_threshold else EntityLinker.NIL
|
||||
|
|
|
|||
|
|
@ -589,7 +589,6 @@ def test_string_to_list_intify(value):
|
|||
assert string_to_list(value, intify=True) == [1, 2, 3]
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Temporarily skip for dev version")
|
||||
def test_download_compatibility():
|
||||
spec = SpecifierSet("==" + about.__version__)
|
||||
spec.prereleases = False
|
||||
|
|
@ -600,7 +599,6 @@ def test_download_compatibility():
|
|||
assert get_minor_version(about.__version__) == get_minor_version(version)
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Temporarily skip for dev version")
|
||||
def test_validate_compatibility_table():
|
||||
spec = SpecifierSet("==" + about.__version__)
|
||||
spec.prereleases = False
|
||||
|
|
|
|||
|
|
@ -60,12 +60,11 @@ def test_readers():
|
|||
assert isinstance(extra_corpus, Callable)
|
||||
|
||||
|
||||
# TODO: enable IMDB test once Stanford servers are back up and running
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.parametrize(
|
||||
"reader,additional_config",
|
||||
[
|
||||
# ("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 10}),
|
||||
("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 10}),
|
||||
("ml_datasets.dbpedia.v1", {"train_limit": 10, "dev_limit": 10}),
|
||||
("ml_datasets.cmu_movies.v1", {"limit": 10, "freq_cutoff": 200, "split": 0.8}),
|
||||
],
|
||||
|
|
|
|||
|
|
@ -679,6 +679,31 @@ def test_projectivize(en_tokenizer):
|
|||
assert proj_heads == [3, 2, 3, 3, 3]
|
||||
assert nonproj_heads == [3, 2, 3, 3, 2]
|
||||
|
||||
# Test single token documents
|
||||
doc = en_tokenizer("Conrail")
|
||||
heads = [0]
|
||||
deps = ["dep"]
|
||||
example = Example.from_dict(doc, {"heads": heads, "deps": deps})
|
||||
proj_heads, proj_labels = example.get_aligned_parse(projectivize=True)
|
||||
assert proj_heads == heads
|
||||
assert proj_labels == deps
|
||||
|
||||
# Test documents with no alignments
|
||||
doc_a = Doc(
|
||||
doc.vocab, words=["Double-Jointed"], spaces=[False], deps=["ROOT"], heads=[0]
|
||||
)
|
||||
doc_b = Doc(
|
||||
doc.vocab,
|
||||
words=["Double", "-", "Jointed"],
|
||||
spaces=[True, True, True],
|
||||
deps=["amod", "punct", "ROOT"],
|
||||
heads=[2, 2, 2],
|
||||
)
|
||||
example = Example(doc_a, doc_b)
|
||||
proj_heads, proj_deps = example.get_aligned_parse(projectivize=True)
|
||||
assert proj_heads == [None]
|
||||
assert proj_deps == [None]
|
||||
|
||||
|
||||
def test_iob_to_biluo():
|
||||
good_iob = ["O", "O", "B-LOC", "I-LOC", "O", "B-PERSON"]
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import pytest
|
||||
import numpy
|
||||
from spacy.tokens import Doc
|
||||
from spacy.vocab import Vocab
|
||||
|
||||
from ..util import get_cosine, add_vecs_to_vocab
|
||||
|
||||
|
|
@ -71,19 +72,17 @@ def test_vectors_similarity_DD(vocab, vectors):
|
|||
def test_vectors_similarity_TD(vocab, vectors):
|
||||
[(word1, vec1), (word2, vec2)] = vectors
|
||||
doc = Doc(vocab, words=[word1, word2])
|
||||
with pytest.warns(UserWarning):
|
||||
assert isinstance(doc.similarity(doc[0]), float)
|
||||
assert isinstance(doc[0].similarity(doc), float)
|
||||
assert doc.similarity(doc[0]) == doc[0].similarity(doc)
|
||||
assert isinstance(doc.similarity(doc[0]), float)
|
||||
assert isinstance(doc[0].similarity(doc), float)
|
||||
assert doc.similarity(doc[0]) == doc[0].similarity(doc)
|
||||
|
||||
|
||||
def test_vectors_similarity_TS(vocab, vectors):
|
||||
[(word1, vec1), (word2, vec2)] = vectors
|
||||
doc = Doc(vocab, words=[word1, word2])
|
||||
with pytest.warns(UserWarning):
|
||||
assert isinstance(doc[:2].similarity(doc[0]), float)
|
||||
assert isinstance(doc[0].similarity(doc[-2]), float)
|
||||
assert doc[:2].similarity(doc[0]) == doc[0].similarity(doc[:2])
|
||||
assert isinstance(doc[:2].similarity(doc[0]), float)
|
||||
assert isinstance(doc[0].similarity(doc[:2]), float)
|
||||
assert doc[:2].similarity(doc[0]) == doc[0].similarity(doc[:2])
|
||||
|
||||
|
||||
def test_vectors_similarity_DS(vocab, vectors):
|
||||
|
|
@ -91,3 +90,21 @@ def test_vectors_similarity_DS(vocab, vectors):
|
|||
doc = Doc(vocab, words=[word1, word2])
|
||||
assert isinstance(doc.similarity(doc[:2]), float)
|
||||
assert doc.similarity(doc[:2]) == doc[:2].similarity(doc)
|
||||
|
||||
|
||||
def test_vectors_similarity_no_vectors():
|
||||
vocab = Vocab()
|
||||
doc1 = Doc(vocab, words=["a", "b"])
|
||||
doc2 = Doc(vocab, words=["c", "d", "e"])
|
||||
with pytest.warns(UserWarning):
|
||||
doc1.similarity(doc2)
|
||||
with pytest.warns(UserWarning):
|
||||
doc1.similarity(doc2[1])
|
||||
with pytest.warns(UserWarning):
|
||||
doc1.similarity(doc2[:2])
|
||||
with pytest.warns(UserWarning):
|
||||
doc2.similarity(doc1)
|
||||
with pytest.warns(UserWarning):
|
||||
doc2[1].similarity(doc1)
|
||||
with pytest.warns(UserWarning):
|
||||
doc2[:2].similarity(doc1)
|
||||
|
|
|
|||
|
|
@ -318,17 +318,15 @@ def test_vectors_lexeme_doc_similarity(vocab, text):
|
|||
@pytest.mark.parametrize("text", [["apple", "orange", "juice"]])
|
||||
def test_vectors_span_span_similarity(vocab, text):
|
||||
doc = Doc(vocab, words=text)
|
||||
with pytest.warns(UserWarning):
|
||||
assert doc[0:2].similarity(doc[1:3]) == doc[1:3].similarity(doc[0:2])
|
||||
assert -1.0 < doc[0:2].similarity(doc[1:3]) < 1.0
|
||||
assert doc[0:2].similarity(doc[1:3]) == doc[1:3].similarity(doc[0:2])
|
||||
assert -1.0 < doc[0:2].similarity(doc[1:3]) < 1.0
|
||||
|
||||
|
||||
@pytest.mark.parametrize("text", [["apple", "orange", "juice"]])
|
||||
def test_vectors_span_doc_similarity(vocab, text):
|
||||
doc = Doc(vocab, words=text)
|
||||
with pytest.warns(UserWarning):
|
||||
assert doc[0:2].similarity(doc) == doc.similarity(doc[0:2])
|
||||
assert -1.0 < doc[0:2].similarity(doc) < 1.0
|
||||
assert doc[0:2].similarity(doc) == doc.similarity(doc[0:2])
|
||||
assert -1.0 < doc[0:2].similarity(doc) < 1.0
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
|
|||
|
|
@ -607,7 +607,8 @@ cdef class Doc:
|
|||
if self.vocab.vectors.n_keys == 0:
|
||||
warnings.warn(Warnings.W007.format(obj="Doc"))
|
||||
if self.vector_norm == 0 or other.vector_norm == 0:
|
||||
warnings.warn(Warnings.W008.format(obj="Doc"))
|
||||
if not self.has_vector or not other.has_vector:
|
||||
warnings.warn(Warnings.W008.format(obj="Doc"))
|
||||
return 0.0
|
||||
vector = self.vector
|
||||
xp = get_array_module(vector)
|
||||
|
|
@ -627,7 +628,7 @@ cdef class Doc:
|
|||
if "has_vector" in self.user_hooks:
|
||||
return self.user_hooks["has_vector"](self)
|
||||
elif self.vocab.vectors.size:
|
||||
return True
|
||||
return any(token.has_vector for token in self)
|
||||
elif self.tensor.size:
|
||||
return True
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -120,6 +120,10 @@ class Span:
|
|||
ent_id: int
|
||||
ent_id_: str
|
||||
@property
|
||||
def id(self) -> int: ...
|
||||
@property
|
||||
def id_(self) -> str: ...
|
||||
@property
|
||||
def orth_(self) -> str: ...
|
||||
@property
|
||||
def lemma_(self) -> str: ...
|
||||
|
|
|
|||
|
|
@ -365,7 +365,8 @@ cdef class Span:
|
|||
if self.vocab.vectors.n_keys == 0:
|
||||
warnings.warn(Warnings.W007.format(obj="Span"))
|
||||
if self.vector_norm == 0.0 or other.vector_norm == 0.0:
|
||||
warnings.warn(Warnings.W008.format(obj="Span"))
|
||||
if not self.has_vector or not other.has_vector:
|
||||
warnings.warn(Warnings.W008.format(obj="Span"))
|
||||
return 0.0
|
||||
vector = self.vector
|
||||
xp = get_array_module(vector)
|
||||
|
|
|
|||
|
|
@ -207,7 +207,8 @@ cdef class Token:
|
|||
if self.vocab.vectors.n_keys == 0:
|
||||
warnings.warn(Warnings.W007.format(obj="Token"))
|
||||
if self.vector_norm == 0 or other.vector_norm == 0:
|
||||
warnings.warn(Warnings.W008.format(obj="Token"))
|
||||
if not self.has_vector or not other.has_vector:
|
||||
warnings.warn(Warnings.W008.format(obj="Token"))
|
||||
return 0.0
|
||||
vector = self.vector
|
||||
xp = get_array_module(vector)
|
||||
|
|
|
|||
|
|
@ -249,9 +249,9 @@ cdef class Example:
|
|||
# Fetch all aligned gold token incides.
|
||||
if c2g_single_toks.shape == cand_to_gold.lengths.shape:
|
||||
# This the most likely case.
|
||||
gold_i = cand_to_gold[:].squeeze()
|
||||
gold_i = cand_to_gold[:]
|
||||
else:
|
||||
gold_i = numpy.vectorize(lambda x: cand_to_gold[int(x)][0])(c2g_single_toks).squeeze()
|
||||
gold_i = numpy.vectorize(lambda x: cand_to_gold[int(x)][0], otypes='i')(c2g_single_toks)
|
||||
|
||||
# Fetch indices of all gold heads for the aligned gold tokens.
|
||||
heads = numpy.asarray(heads, dtype='i')
|
||||
|
|
@ -261,7 +261,7 @@ cdef class Example:
|
|||
# gold tokens (and are aligned to a single candidate token).
|
||||
g2c_len_heads = gold_to_cand.lengths[gold_head_i]
|
||||
g2c_len_heads = numpy.where(g2c_len_heads == 1)[0]
|
||||
g2c_i = numpy.vectorize(lambda x: gold_to_cand[int(x)][0])(gold_head_i[g2c_len_heads]).squeeze()
|
||||
g2c_i = numpy.vectorize(lambda x: gold_to_cand[int(x)][0], otypes='i')(gold_head_i[g2c_len_heads]).squeeze()
|
||||
|
||||
# Update head/dep alignments with the above.
|
||||
aligned_heads = numpy.full((self.x.length), None)
|
||||
|
|
|
|||
|
|
@ -336,10 +336,10 @@ cdef class Vectors:
|
|||
xp = get_array_module(self.data)
|
||||
if key is not None:
|
||||
key = get_string_id(key)
|
||||
return self.key2row.get(key, -1)
|
||||
return self.key2row.get(int(key), -1)
|
||||
elif keys is not None:
|
||||
keys = [get_string_id(key) for key in keys]
|
||||
rows = [self.key2row.get(key, -1) for key in keys]
|
||||
rows = [self.key2row.get(int(key), -1) for key in keys]
|
||||
return xp.asarray(rows, dtype="i")
|
||||
else:
|
||||
row2key = {row: key for key, row in self.key2row.items()}
|
||||
|
|
|
|||
|
|
@ -62,7 +62,7 @@ of relations, see the usage guide on
|
|||
|
||||
</Infobox>
|
||||
|
||||
### Operators
|
||||
### Operators {#operators}
|
||||
|
||||
The following operators are supported by the `DependencyMatcher`, most of which
|
||||
come directly from
|
||||
|
|
@ -82,6 +82,11 @@ come directly from
|
|||
| `A $- B` | `B` is a left immediate sibling of `A`, i.e. `A` and `B` have the same parent and `A.i == B.i + 1`. |
|
||||
| `A $++ B` | `B` is a right sibling of `A`, i.e. `A` and `B` have the same parent and `A.i < B.i`. |
|
||||
| `A $-- B` | `B` is a left sibling of `A`, i.e. `A` and `B` have the same parent and `A.i > B.i`. |
|
||||
| `A >++ B` | `B` is a right child of `A`, i.e. `A` is a parent of `B` and `A.i < B.i` _(not in Semgrex)_. |
|
||||
| `A >-- B` | `B` is a left child of `A`, i.e. `A` is a parent of `B` and `A.i > B.i` _(not in Semgrex)_. |
|
||||
| `A <++ B` | `B` is a right parent of `A`, i.e. `A` is a child of `B` and `A.i < B.i` _(not in Semgrex)_. |
|
||||
| `A <-- B` | `B` is a left parent of `A`, i.e. `A` is a child of `B` and `A.i > B.i` _(not in Semgrex)_. |
|
||||
|
||||
|
||||
## DependencyMatcher.\_\_init\_\_ {#init tag="method"}
|
||||
|
||||
|
|
|
|||
|
|
@ -47,22 +47,24 @@ architectures and their arguments and hyperparameters.
|
|||
> "model": DEFAULT_NEL_MODEL,
|
||||
> "entity_vector_length": 64,
|
||||
> "get_candidates": {'@misc': 'spacy.CandidateGenerator.v1'},
|
||||
> "threshold": None,
|
||||
> }
|
||||
> nlp.add_pipe("entity_linker", config=config)
|
||||
> ```
|
||||
|
||||
| Setting | Description |
|
||||
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
|
||||
| `n_sents` | The number of neighbouring sentences to take into account. Defaults to 0. ~~int~~ |
|
||||
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
|
||||
| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
|
||||
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
|
||||
| `entity_vector_length` | Size of encoding vectors in the KB. Defaults to `64`. ~~int~~ |
|
||||
| `use_gold_ents` | Whether to copy entities from the gold docs or not. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~int~~ |
|
||||
| `get_candidates` | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
|
||||
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
|
||||
| Setting | Description |
|
||||
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
|
||||
| `n_sents` | The number of neighbouring sentences to take into account. Defaults to 0. ~~int~~ |
|
||||
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
|
||||
| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
|
||||
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
|
||||
| `entity_vector_length` | Size of encoding vectors in the KB. Defaults to `64`. ~~int~~ |
|
||||
| `use_gold_ents` | Whether to copy entities from the gold docs or not. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~int~~ |
|
||||
| `get_candidates` | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
|
||||
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
|
||||
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
|
||||
|
||||
```python
|
||||
%%GITHUB_SPACY/spacy/pipeline/entity_linker.py
|
||||
|
|
@ -95,20 +97,21 @@ custom knowledge base, you should either call
|
|||
[`set_kb`](/api/entitylinker#set_kb) or provide a `kb_loader` in the
|
||||
[`initialize`](/api/entitylinker#initialize) call.
|
||||
|
||||
| Name | Description |
|
||||
| ---------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `vocab` | The shared vocabulary. ~~Vocab~~ |
|
||||
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
|
||||
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
|
||||
| _keyword-only_ | |
|
||||
| `entity_vector_length` | Size of encoding vectors in the KB. ~~int~~ |
|
||||
| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
|
||||
| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
|
||||
| `n_sents` | The number of neighbouring sentences to take into account. ~~int~~ |
|
||||
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
|
||||
| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
|
||||
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
|
||||
| Name | Description |
|
||||
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `vocab` | The shared vocabulary. ~~Vocab~~ |
|
||||
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
|
||||
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
|
||||
| _keyword-only_ | |
|
||||
| `entity_vector_length` | Size of encoding vectors in the KB. ~~int~~ |
|
||||
| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
|
||||
| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
|
||||
| `n_sents` | The number of neighbouring sentences to take into account. ~~int~~ |
|
||||
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
|
||||
| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
|
||||
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
|
||||
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
|
||||
|
||||
## EntityLinker.\_\_call\_\_ {#call tag="method"}
|
||||
|
||||
|
|
|
|||
|
|
@ -118,7 +118,7 @@ shortcut for this and instantiate the component using its string name and
|
|||
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
|
||||
| _keyword-only_ | |
|
||||
| mode | The lemmatizer mode, e.g. `"lookup"` or `"rule"`. Defaults to `"lookup"`. ~~str~~ |
|
||||
| overwrite | Whether to overwrite existing lemmas. ~~bool~ |
|
||||
| overwrite | Whether to overwrite existing lemmas. ~~bool~~ |
|
||||
|
||||
## Lemmatizer.\_\_call\_\_ {#call tag="method"}
|
||||
|
||||
|
|
|
|||
|
|
@ -59,15 +59,20 @@ matched:
|
|||
> [
|
||||
> {"POS": "ADJ", "OP": "*"},
|
||||
> {"POS": "NOUN", "OP": "+"}
|
||||
> {"POS": "PROPN", "OP": "{2}"}
|
||||
> ]
|
||||
> ```
|
||||
|
||||
| OP | Description |
|
||||
| --- | ---------------------------------------------------------------- |
|
||||
| `!` | Negate the pattern, by requiring it to match exactly 0 times. |
|
||||
| `?` | Make the pattern optional, by allowing it to match 0 or 1 times. |
|
||||
| `+` | Require the pattern to match 1 or more times. |
|
||||
| `*` | Allow the pattern to match 0 or more times. |
|
||||
| OP | Description |
|
||||
|---------|------------------------------------------------------------------------|
|
||||
| `!` | Negate the pattern, by requiring it to match exactly 0 times. |
|
||||
| `?` | Make the pattern optional, by allowing it to match 0 or 1 times. |
|
||||
| `+` | Require the pattern to match 1 or more times. |
|
||||
| `*` | Allow the pattern to match 0 or more times. |
|
||||
| `{n}` | Require the pattern to match exactly _n_ times. |
|
||||
| `{n,m}` | Require the pattern to match at least _n_ but not more than _m_ times. |
|
||||
| `{n,}` | Require the pattern to match at least _n_ times. |
|
||||
| `{,m}` | Require the pattern to match at most _m_ times. |
|
||||
|
||||
Token patterns can also map to a **dictionary of properties** instead of a
|
||||
single value to indicate whether the expected value is a member of a list or how
|
||||
|
|
@ -194,7 +199,7 @@ will be overwritten.
|
|||
> [{"LOWER": "hello"}, {"LOWER": "world"}],
|
||||
> [{"ORTH": "Google"}, {"ORTH": "Maps"}]
|
||||
> ]
|
||||
> matcher.add("TEST_PATTERNS", patterns)
|
||||
> matcher.add("TEST_PATTERNS", patterns, on_match=on_match)
|
||||
> doc = nlp("HELLO WORLD on Google Maps.")
|
||||
> matches = matcher(doc)
|
||||
> ```
|
||||
|
|
|
|||
|
|
@ -130,8 +130,8 @@ grateful to use the work of Chainer's [CuPy](https://cupy.chainer.org) module,
|
|||
which provides a numpy-compatible interface for GPU arrays.
|
||||
|
||||
spaCy can be installed for a CUDA-compatible GPU by specifying `spacy[cuda]`,
|
||||
`spacy[cuda102]`, `spacy[cuda112]`, `spacy[cuda113]`, etc. If you know your
|
||||
CUDA version, using the more explicit specifier allows CuPy to be installed via
|
||||
`spacy[cuda102]`, `spacy[cuda112]`, `spacy[cuda113]`, etc. If you know your CUDA
|
||||
version, using the more explicit specifier allows CuPy to be installed via
|
||||
wheel, saving some compilation time. The specifiers should install
|
||||
[`cupy`](https://cupy.chainer.org).
|
||||
|
||||
|
|
@ -195,29 +195,73 @@ How to install compilers and related build tools:
|
|||
[Visual Studio Express](https://www.visualstudio.com/vs/visual-studio-express/)
|
||||
that matches the version that was used to compile your Python interpreter.
|
||||
|
||||
#### Using build constraints when compiling from source
|
||||
|
||||
If you install spaCy from source or with `pip` for platforms where there are not
|
||||
binary wheels on PyPI, you may need to use build constraints if any package in
|
||||
your environment requires an older version of `numpy`.
|
||||
|
||||
If `numpy` gets downgraded from the most recent release at any point after
|
||||
you've compiled `spacy`, you might see an error that looks like this:
|
||||
|
||||
```none
|
||||
numpy.ndarray size changed, may indicate binary incompatibility.
|
||||
```
|
||||
|
||||
To fix this, create a new virtual environment and install `spacy` and all of its
|
||||
dependencies using build constraints.
|
||||
[Build constraints](https://pip.pypa.io/en/stable/user_guide/#constraints-files)
|
||||
specify an older version of `numpy` that is only used while compiling `spacy`,
|
||||
and then your runtime environment can use any newer version of `numpy` and still
|
||||
be compatible. In addition, use `--no-cache-dir` to ignore any previously cached
|
||||
wheels so that all relevant packages are recompiled from scratch:
|
||||
|
||||
```shell
|
||||
PIP_CONSTRAINT=https://raw.githubusercontent.com/explosion/spacy/master/build-constraints.txt \
|
||||
pip install spacy --no-cache-dir
|
||||
```
|
||||
|
||||
Our build constraints currently specify the oldest supported `numpy` available
|
||||
on PyPI for `x86_64` and `aarch64`. Depending on your platform and environment,
|
||||
you may want to customize the specific versions of `numpy`. For other platforms,
|
||||
you can have a look at SciPy's
|
||||
[`oldest-supported-numpy`](https://github.com/scipy/oldest-supported-numpy/blob/main/setup.cfg)
|
||||
package to see what the oldest recommended versions of `numpy` are.
|
||||
|
||||
(_Warning_: don't use `pip install -c constraints.txt` instead of
|
||||
`PIP_CONSTRAINT`, since this isn't applied to the isolated build environments.)
|
||||
|
||||
#### Additional options for developers {#source-developers}
|
||||
|
||||
Some additional options may be useful for spaCy developers who are editing the
|
||||
source code and recompiling frequently.
|
||||
|
||||
- Install in editable mode. Changes to `.py` files will be reflected as soon as
|
||||
the files are saved, but edits to Cython files (`.pxd`, `.pyx`) will require
|
||||
the `pip install` or `python setup.py build_ext` command below to be run
|
||||
again. Before installing in editable mode, be sure you have removed any
|
||||
previous installs with `pip uninstall spacy`, which you may need to run
|
||||
multiple times to remove all traces of earlier installs.
|
||||
- Install in editable mode. Changes to `.py` files will be reflected as soon
|
||||
as the files are saved, but edits to Cython files (`.pxd`, `.pyx`) will
|
||||
require the `pip install` command below to be run again. Before installing in
|
||||
editable mode, be sure you have removed any previous installs with
|
||||
`pip uninstall spacy`, which you may need to run multiple times to remove all
|
||||
traces of earlier installs.
|
||||
|
||||
```bash
|
||||
$ pip install -r requirements.txt
|
||||
$ pip install --no-build-isolation --editable .
|
||||
```
|
||||
|
||||
- Build in parallel using `N` CPUs to speed up compilation and then install in
|
||||
editable mode:
|
||||
- Build in parallel. Starting in v3.4.0, you can specify the number of
|
||||
build jobs with the environment variable `SPACY_NUM_BUILD_JOBS`:
|
||||
|
||||
```bash
|
||||
$ pip install -r requirements.txt
|
||||
$ python setup.py build_ext --inplace -j N
|
||||
$ SPACY_NUM_BUILD_JOBS=4 pip install --no-build-isolation --editable .
|
||||
```
|
||||
|
||||
- For editable mode and parallel builds with `python setup.py` instead of `pip`
|
||||
(no longer recommended):
|
||||
|
||||
```bash
|
||||
$ pip install -r requirements.txt
|
||||
$ python setup.py build_ext --inplace -j 4
|
||||
$ python setup.py develop
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -374,12 +374,16 @@ punctuation marks, or specify optional tokens. Note that there are no nested or
|
|||
scoped quantifiers – instead, you can build those behaviors with `on_match`
|
||||
callbacks.
|
||||
|
||||
| OP | Description |
|
||||
| --- | ---------------------------------------------------------------- |
|
||||
| `!` | Negate the pattern, by requiring it to match exactly 0 times. |
|
||||
| `?` | Make the pattern optional, by allowing it to match 0 or 1 times. |
|
||||
| `+` | Require the pattern to match 1 or more times. |
|
||||
| `*` | Allow the pattern to match zero or more times. |
|
||||
| OP | Description |
|
||||
|---------|------------------------------------------------------------------------|
|
||||
| `!` | Negate the pattern, by requiring it to match exactly 0 times. |
|
||||
| `?` | Make the pattern optional, by allowing it to match 0 or 1 times. |
|
||||
| `+` | Require the pattern to match 1 or more times. |
|
||||
| `*` | Allow the pattern to match zero or more times. |
|
||||
| `{n}` | Require the pattern to match exactly _n_ times. |
|
||||
| `{n,m}` | Require the pattern to match at least _n_ but not more than _m_ times. |
|
||||
| `{n,}` | Require the pattern to match at least _n_ times. |
|
||||
| `{,m}` | Require the pattern to match at most _m_ times. |
|
||||
|
||||
> #### Example
|
||||
>
|
||||
|
|
|
|||
143
website/docs/usage/v3-4.md
Normal file
143
website/docs/usage/v3-4.md
Normal file
|
|
@ -0,0 +1,143 @@
|
|||
---
|
||||
title: What's New in v3.4
|
||||
teaser: New features and how to upgrade
|
||||
menu:
|
||||
- ['New Features', 'features']
|
||||
- ['Upgrading Notes', 'upgrading']
|
||||
---
|
||||
|
||||
## New features {#features hidden="true"}
|
||||
|
||||
spaCy v3.4 brings typing and speed improvements along with new vectors for
|
||||
English CNN pipelines and new trained pipelines for Croatian. This release also
|
||||
includes prebuilt linux aarch64 wheels for all spaCy dependencies distributed by
|
||||
Explosion.
|
||||
|
||||
### Typing improvements {#typing}
|
||||
|
||||
spaCy v3.4 supports pydantic v1.9 and mypy 0.950+ through extensive updates to
|
||||
types in Thinc v8.1.
|
||||
|
||||
### Speed improvements {#speed}
|
||||
|
||||
- For the parser, use C `saxpy`/`sgemm` provided by the `Ops` implementation in
|
||||
order to use Accelerate through `thinc-apple-ops`.
|
||||
- Improved speed of vector lookups.
|
||||
- Improved speed for `Example.get_aligned_parse` and `Example.get_aligned`.
|
||||
|
||||
## Additional features and improvements
|
||||
|
||||
- Min/max `{n,m}` operator for `Matcher` patterns.
|
||||
- Language updates:
|
||||
- Improve tokenization for Cyrillic combining diacritics.
|
||||
- Improve English tokenizer exceptions for contractions with
|
||||
this/that/these/those.
|
||||
- Updated `spacy project clone` to try both `main` and `master` branches by
|
||||
default.
|
||||
- Added confidence threshold for named entity linker.
|
||||
- Improved handling of Typer optional default values for `init_config_cli`.
|
||||
- Added cycle detection in parser projectivization methods.
|
||||
- Added counts for NER labels in `debug data`.
|
||||
- Support for adding NVTX ranges to `TrainablePipe` components.
|
||||
- Support env variable `SPACY_NUM_BUILD_JOBS` to specify the number of build
|
||||
jobs to run in parallel with `pip`.
|
||||
|
||||
## Trained pipelines {#pipelines}
|
||||
|
||||
### New trained pipelines {#new-pipelines}
|
||||
|
||||
v3.4 introduces new CPU/CNN pipelines for Croatian, which use the trainable
|
||||
lemmatizer and [floret vectors](https://github.com/explosion/floret). Due to the
|
||||
use of [Bloom embeddings](https://explosion.ai/blog/bloom-embeddings) and
|
||||
subwords, the pipelines have compact vectors with no out-of-vocabulary words.
|
||||
|
||||
| Package | UPOS | Parser LAS | NER F |
|
||||
| ----------------------------------------------- | ---: | ---------: | ----: |
|
||||
| [`hr_core_news_sm`](/models/hr#hr_core_news_sm) | 96.6 | 77.5 | 76.1 |
|
||||
| [`hr_core_news_md`](/models/hr#hr_core_news_md) | 97.3 | 80.1 | 81.8 |
|
||||
| [`hr_core_news_lg`](/models/hr#hr_core_news_lg) | 97.5 | 80.4 | 83.0 |
|
||||
|
||||
### Pipeline updates {#pipeline-updates}
|
||||
|
||||
All CNN pipelines have been extended with whitespace augmentation.
|
||||
|
||||
The English CNN pipelines have new word vectors:
|
||||
|
||||
| Package | Model Version | TAG | Parser LAS | NER F |
|
||||
| ----------------------------------------------- | ------------- | ---: | ---------: | ----: |
|
||||
| [`en_core_news_md`](/models/en#en_core_news_md) | v3.3.0 | 97.3 | 90.1 | 84.6 |
|
||||
| [`en_core_news_md`](/models/en#en_core_news_lg) | v3.4.0 | 97.2 | 90.3 | 85.5 |
|
||||
| [`en_core_news_lg`](/models/en#en_core_news_md) | v3.3.0 | 97.4 | 90.1 | 85.3 |
|
||||
| [`en_core_news_lg`](/models/en#en_core_news_lg) | v3.4.0 | 97.3 | 90.2 | 85.6 |
|
||||
|
||||
## Notes about upgrading from v3.3 {#upgrading}
|
||||
|
||||
### Doc.has_vector
|
||||
|
||||
`Doc.has_vector` now matches `Token.has_vector` and `Span.has_vector`: it
|
||||
returns `True` if at least one token in the doc has a vector rather than
|
||||
checking only whether the vocab contains vectors.
|
||||
|
||||
### Using trained pipelines with floret vectors
|
||||
|
||||
If you're using a trained pipeline for Croatian, Finnish, Korean or Swedish with
|
||||
new texts and working with `Doc` objects, you shouldn't notice any difference
|
||||
between floret vectors and default vectors.
|
||||
|
||||
If you use vectors for similarity comparisons, there are a few differences,
|
||||
mainly because a floret pipeline doesn't include any kind of frequency-based
|
||||
word list similar to the list of in-vocabulary vector keys with default vectors.
|
||||
|
||||
- If your workflow iterates over the vector keys, you should use an external
|
||||
word list instead:
|
||||
|
||||
```diff
|
||||
- lexemes = [nlp.vocab[orth] for orth in nlp.vocab.vectors]
|
||||
+ lexemes = [nlp.vocab[word] for word in external_word_list]
|
||||
```
|
||||
|
||||
- `Vectors.most_similar` is not supported because there's no fixed list of
|
||||
vectors to compare your vectors to.
|
||||
|
||||
### Pipeline package version compatibility {#version-compat}
|
||||
|
||||
> #### Using legacy implementations
|
||||
>
|
||||
> In spaCy v3, you'll still be able to load and reference legacy implementations
|
||||
> via [`spacy-legacy`](https://github.com/explosion/spacy-legacy), even if the
|
||||
> components or architectures change and newer versions are available in the
|
||||
> core library.
|
||||
|
||||
When you're loading a pipeline package trained with an earlier version of spaCy
|
||||
v3, you will see a warning telling you that the pipeline may be incompatible.
|
||||
This doesn't necessarily have to be true, but we recommend running your
|
||||
pipelines against your test suite or evaluation data to make sure there are no
|
||||
unexpected results.
|
||||
|
||||
If you're using one of the [trained pipelines](/models) we provide, you should
|
||||
run [`spacy download`](/api/cli#download) to update to the latest version. To
|
||||
see an overview of all installed packages and their compatibility, you can run
|
||||
[`spacy validate`](/api/cli#validate).
|
||||
|
||||
If you've trained your own custom pipeline and you've confirmed that it's still
|
||||
working as expected, you can update the spaCy version requirements in the
|
||||
[`meta.json`](/api/data-formats#meta):
|
||||
|
||||
```diff
|
||||
- "spacy_version": ">=3.3.0,<3.4.0",
|
||||
+ "spacy_version": ">=3.3.0,<3.5.0",
|
||||
```
|
||||
|
||||
### Updating v3.3 configs
|
||||
|
||||
To update a config from spaCy v3.3 with the new v3.4 settings, run
|
||||
[`init fill-config`](/api/cli#init-fill-config):
|
||||
|
||||
```cli
|
||||
$ python -m spacy init fill-config config-v3.3.cfg config-v3.4.cfg
|
||||
```
|
||||
|
||||
In many cases ([`spacy train`](/api/cli#train),
|
||||
[`spacy.load`](/api/top-level#spacy.load)), the new defaults will be filled in
|
||||
automatically, but you'll need to fill in the new settings to run
|
||||
[`debug config`](/api/cli#debug) and [`debug data`](/api/cli#debug-data).
|
||||
|
|
@ -162,7 +162,12 @@
|
|||
{
|
||||
"code": "hr",
|
||||
"name": "Croatian",
|
||||
"has_examples": true
|
||||
"has_examples": true,
|
||||
"models": [
|
||||
"hr_core_news_sm",
|
||||
"hr_core_news_md",
|
||||
"hr_core_news_lg"
|
||||
]
|
||||
},
|
||||
{
|
||||
"code": "hsb",
|
||||
|
|
|
|||
|
|
@ -12,7 +12,9 @@
|
|||
{ "text": "New in v3.0", "url": "/usage/v3" },
|
||||
{ "text": "New in v3.1", "url": "/usage/v3-1" },
|
||||
{ "text": "New in v3.2", "url": "/usage/v3-2" },
|
||||
{ "text": "New in v3.3", "url": "/usage/v3-3" }
|
||||
{ "text": "New in v3.2", "url": "/usage/v3-2" },
|
||||
{ "text": "New in v3.3", "url": "/usage/v3-3" },
|
||||
{ "text": "New in v3.4", "url": "/usage/v3-4" }
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@
|
|||
},
|
||||
"binderUrl": "explosion/spacy-io-binder",
|
||||
"binderBranch": "spacy.io",
|
||||
"binderVersion": "3.0",
|
||||
"binderVersion": "3.4",
|
||||
"sections": [
|
||||
{ "id": "usage", "title": "Usage Documentation", "theme": "blue" },
|
||||
{ "id": "models", "title": "Models Documentation", "theme": "blue" },
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@
|
|||
"## Set parameter `extra_info` to `True` and check also span._.description, span._.src_description, span._.normal_term, span._.other_ids"
|
||||
],
|
||||
"category": ["models", "pipeline"],
|
||||
"image": "https://raw.githubusercontent.com/Lucaterre/spacyfishing/main/docs/spacyfishing-logo-resized.png",
|
||||
"tags": ["NER", "NEL"],
|
||||
"author": "Lucas Terriel",
|
||||
"author_links": {
|
||||
|
|
@ -544,6 +545,37 @@
|
|||
"website": "https://koaning.io"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "bertopic",
|
||||
"title": "BERTopic",
|
||||
"slogan": "Leveraging BERT and c-TF-IDF to create easily interpretable topics.",
|
||||
"description": "BERTopic is a topic modeling technique that leverages embedding models and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. BERTopic supports guided, (semi-) supervised, hierarchical, and dynamic topic modeling.",
|
||||
"github": "maartengr/bertopic",
|
||||
"pip": "bertopic",
|
||||
"thumb": "https://i.imgur.com/Rx2LfBm.png",
|
||||
"image": "https://raw.githubusercontent.com/MaartenGr/BERTopic/master/images/topic_visualization.gif",
|
||||
"code_example": [
|
||||
"import spacy",
|
||||
"from bertopic import BERTopic",
|
||||
"from sklearn.datasets import fetch_20newsgroups",
|
||||
"",
|
||||
"docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']",
|
||||
"nlp = spacy.load('en_core_web_md', exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer'])",
|
||||
"",
|
||||
"topic_model = BERTopic(embedding_model=nlp)",
|
||||
"topics, probs = topic_model.fit_transform(docs)",
|
||||
"",
|
||||
"fig = topic_model.visualize_topics()",
|
||||
"fig.show()"
|
||||
],
|
||||
"category": ["visualizers", "training"],
|
||||
"author": "Maarten Grootendorst",
|
||||
"author_links": {
|
||||
"twitter": "maartengr",
|
||||
"github": "maartengr",
|
||||
"website": "https://maartengrootendorst.com"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "tokenwiser",
|
||||
"title": "tokenwiser",
|
||||
|
|
@ -749,43 +781,6 @@
|
|||
"category": ["standalone", "research"],
|
||||
"tags": ["pytorch"]
|
||||
},
|
||||
{
|
||||
"id": "NeuroNER",
|
||||
"title": "NeuroNER",
|
||||
"slogan": "Named-entity recognition using neural networks",
|
||||
"github": "Franck-Dernoncourt/NeuroNER",
|
||||
"category": ["models"],
|
||||
"pip": "pyneuroner[cpu]",
|
||||
"code_example": [
|
||||
"from neuroner import neuromodel",
|
||||
"nn = neuromodel.NeuroNER(train_model=False, use_pretrained_model=True)"
|
||||
],
|
||||
"tags": ["standalone"]
|
||||
},
|
||||
{
|
||||
"id": "NLPre",
|
||||
"title": "NLPre",
|
||||
"slogan": "Natural Language Preprocessing Library for health data and more",
|
||||
"github": "NIHOPA/NLPre",
|
||||
"pip": "nlpre",
|
||||
"code_example": [
|
||||
"from nlpre import titlecaps, dedash, identify_parenthetical_phrases",
|
||||
"from nlpre import replace_acronyms, replace_from_dictionary",
|
||||
"ABBR = identify_parenthetical_phrases()(text)",
|
||||
"parsers = [dedash(), titlecaps(), replace_acronyms(ABBR),",
|
||||
" replace_from_dictionary(prefix='MeSH_')]",
|
||||
"for f in parsers:",
|
||||
" text = f(text)",
|
||||
"print(text)"
|
||||
],
|
||||
"category": ["scientific", "biomedical"],
|
||||
"author": "Travis Hoppe",
|
||||
"author_links": {
|
||||
"github": "thoppe",
|
||||
"twitter": "metasemantic",
|
||||
"website": "http://thoppe.github.io/"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "Chatterbot",
|
||||
"title": "Chatterbot",
|
||||
|
|
@ -888,78 +883,6 @@
|
|||
"github": "shigapov"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "spacy_hunspell",
|
||||
"slogan": "Add spellchecking and spelling suggestions to your spaCy pipeline using Hunspell",
|
||||
"description": "This package uses the [spaCy 2.0 extensions](https://spacy.io/usage/processing-pipelines#extensions) to add [Hunspell](http://hunspell.github.io) support for spellchecking.",
|
||||
"github": "tokestermw/spacy_hunspell",
|
||||
"pip": "spacy_hunspell",
|
||||
"code_example": [
|
||||
"import spacy",
|
||||
"from spacy_hunspell import spaCyHunSpell",
|
||||
"",
|
||||
"nlp = spacy.load('en_core_web_sm')",
|
||||
"hunspell = spaCyHunSpell(nlp, 'mac')",
|
||||
"nlp.add_pipe(hunspell)",
|
||||
"doc = nlp('I can haz cheezeburger.')",
|
||||
"haz = doc[2]",
|
||||
"haz._.hunspell_spell # False",
|
||||
"haz._.hunspell_suggest # ['ha', 'haze', 'hazy', 'has', 'hat', 'had', 'hag', 'ham', 'hap', 'hay', 'haw', 'ha z']"
|
||||
],
|
||||
"author": "Motoki Wu",
|
||||
"author_links": {
|
||||
"github": "tokestermw",
|
||||
"twitter": "plusepsilon"
|
||||
},
|
||||
"category": ["pipeline"],
|
||||
"tags": ["spellcheck"]
|
||||
},
|
||||
{
|
||||
"id": "spacy_grammar",
|
||||
"slogan": "Language Tool style grammar handling with spaCy",
|
||||
"description": "This packages leverages the [Matcher API](https://spacy.io/docs/usage/rule-based-matching) in spaCy to quickly match on spaCy tokens not dissimilar to regex. It reads a `grammar.yml` file to load up custom patterns and returns the results inside `Doc`, `Span`, and `Token`. It is extensible through adding rules to `grammar.yml` (though currently only the simple string matching is implemented).",
|
||||
"github": "tokestermw/spacy_grammar",
|
||||
"code_example": [
|
||||
"import spacy",
|
||||
"from spacy_grammar.grammar import Grammar",
|
||||
"",
|
||||
"nlp = spacy.load('en')",
|
||||
"grammar = Grammar(nlp)",
|
||||
"nlp.add_pipe(grammar)",
|
||||
"doc = nlp('I can haz cheeseburger.')",
|
||||
"doc._.has_grammar_error # True"
|
||||
],
|
||||
"author": "Motoki Wu",
|
||||
"author_links": {
|
||||
"github": "tokestermw",
|
||||
"twitter": "plusepsilon"
|
||||
},
|
||||
"category": ["pipeline"]
|
||||
},
|
||||
{
|
||||
"id": "spacy_kenlm",
|
||||
"slogan": "KenLM extension for spaCy 2.0",
|
||||
"github": "tokestermw/spacy_kenlm",
|
||||
"pip": "spacy_kenlm",
|
||||
"code_example": [
|
||||
"import spacy",
|
||||
"from spacy_kenlm import spaCyKenLM",
|
||||
"",
|
||||
"nlp = spacy.load('en_core_web_sm')",
|
||||
"spacy_kenlm = spaCyKenLM() # default model from test.arpa",
|
||||
"nlp.add_pipe(spacy_kenlm)",
|
||||
"doc = nlp('How are you?')",
|
||||
"doc._.kenlm_score # doc score",
|
||||
"doc[:2]._.kenlm_score # span score",
|
||||
"doc[2]._.kenlm_score # token score"
|
||||
],
|
||||
"author": "Motoki Wu",
|
||||
"author_links": {
|
||||
"github": "tokestermw",
|
||||
"twitter": "plusepsilon"
|
||||
},
|
||||
"category": ["pipeline"]
|
||||
},
|
||||
{
|
||||
"id": "spacy_readability",
|
||||
"slogan": "Add text readability meta data to Doc objects",
|
||||
|
|
@ -1028,34 +951,6 @@
|
|||
},
|
||||
"category": ["pipeline"]
|
||||
},
|
||||
{
|
||||
"id": "spacy-lookup",
|
||||
"slogan": "A powerful entity matcher for very large dictionaries, using the FlashText module",
|
||||
"description": "spaCy v2.0 extension and pipeline component for adding Named Entities metadata to `Doc` objects. Detects Named Entities using dictionaries. The extension sets the custom `Doc`, `Token` and `Span` attributes `._.is_entity`, `._.entity_type`, `._.has_entities` and `._.entities`. Named Entities are matched using the python module `flashtext`, and looked up in the data provided by different dictionaries.",
|
||||
"github": "mpuig/spacy-lookup",
|
||||
"pip": "spacy-lookup",
|
||||
"code_example": [
|
||||
"import spacy",
|
||||
"from spacy_lookup import Entity",
|
||||
"",
|
||||
"nlp = spacy.load('en')",
|
||||
"entity = Entity(keywords_list=['python', 'product manager', 'java platform'])",
|
||||
"nlp.add_pipe(entity, last=True)",
|
||||
"",
|
||||
"doc = nlp(\"I am a product manager for a java and python.\")",
|
||||
"assert doc._.has_entities == True",
|
||||
"assert doc[0]._.is_entity == False",
|
||||
"assert doc[3]._.entity_desc == 'product manager'",
|
||||
"assert doc[3]._.is_entity == True",
|
||||
"",
|
||||
"print([(token.text, token._.canonical) for token in doc if token._.is_entity])"
|
||||
],
|
||||
"author": "Marc Puig",
|
||||
"author_links": {
|
||||
"github": "mpuig"
|
||||
},
|
||||
"category": ["pipeline"]
|
||||
},
|
||||
{
|
||||
"id": "spacy-iwnlp",
|
||||
"slogan": "German lemmatization with IWNLP",
|
||||
|
|
@ -1257,6 +1152,46 @@
|
|||
"category": ["pipeline", "models", "training"],
|
||||
"tags": ["pipeline", "models", "transformers"]
|
||||
},
|
||||
{
|
||||
"id": "asent",
|
||||
"title": "Asent",
|
||||
"slogan": "Fast, flexible and transparent sentiment analysis",
|
||||
"description": "Asent is a rule-based sentiment analysis library for Python made using spaCy. It is inspired by VADER, but uses a more modular ruleset, that allows the user to change e.g. the method for finding negations. Furthermore it includes visualisers to visualize the model predictions, making the model easily interpretable.",
|
||||
"github": "kennethenevoldsen/asent",
|
||||
"pip": "aseny",
|
||||
"code_example": [
|
||||
"import spacy",
|
||||
"import asent",
|
||||
"",
|
||||
"# load spacy pipeline",
|
||||
"nlp = spacy.blank('en')",
|
||||
"nlp.add_pipe('sentencizer')",
|
||||
"",
|
||||
"# add the rule-based sentiment model",
|
||||
"nlp.add_pipe('asent_en_v1')",
|
||||
"",
|
||||
"# try an example",
|
||||
"text = 'I am not very happy, but I am also not especially sad'",
|
||||
"doc = nlp(text)",
|
||||
"",
|
||||
"# print polarity of document, scaled to be between -1, and 1",
|
||||
"print(doc._.polarity)",
|
||||
"# neg=0.0 neu=0.631 pos=0.369 compound=0.7526",
|
||||
"",
|
||||
"# Naturally, a simple score can be quite unsatisfying, thus Asent implements a series of visualizer to interpret the results:",
|
||||
"asent.visualize(doc, style='prediction')",
|
||||
" # or",
|
||||
"asent.visualize(doc[:5], style='analysis')"
|
||||
],
|
||||
"thumb": "https://github.com/KennethEnevoldsen/asent/raw/main/docs/img/logo_black_font.png?raw=true",
|
||||
"author": "Kenneth Enevoldsen",
|
||||
"author_links": {
|
||||
"github": "KennethEnevoldsen",
|
||||
"website": "https://www.kennethenevoldsen.com"
|
||||
},
|
||||
"category": ["pipeline", "models"],
|
||||
"tags": ["pipeline", "models", "sentiment"]
|
||||
},
|
||||
{
|
||||
"id": "textdescriptives",
|
||||
"title": "TextDescriptives",
|
||||
|
|
@ -1322,21 +1257,6 @@
|
|||
"github": "huggingface"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "spacy-vis",
|
||||
"slogan": "A visualisation tool for spaCy using Hierplane",
|
||||
"description": "A visualiser for spaCy annotations. This visualisation uses the [Hierplane](https://allenai.github.io/hierplane/) Library to render the dependency parse from spaCy's models. It also includes visualisation of entities and POS tags within nodes.",
|
||||
"github": "DeNeutoy/spacy-vis",
|
||||
"url": "http://spacyvis.allennlp.org/spacy-parser",
|
||||
"thumb": "https://i.imgur.com/DAG9QFd.jpg",
|
||||
"image": "https://raw.githubusercontent.com/DeNeutoy/spacy-vis/master/img/example.gif",
|
||||
"author": "Mark Neumann",
|
||||
"author_links": {
|
||||
"twitter": "MarkNeumannnn",
|
||||
"github": "DeNeutoy"
|
||||
},
|
||||
"category": ["visualizers"]
|
||||
},
|
||||
{
|
||||
"id": "matcher-explorer",
|
||||
"title": "Rule-based Matcher Explorer",
|
||||
|
|
@ -2340,29 +2260,6 @@
|
|||
"youtube": "8u57WSXVpmw",
|
||||
"category": ["videos"]
|
||||
},
|
||||
{
|
||||
"id": "adam_qas",
|
||||
"title": "ADAM: Question Answering System",
|
||||
"slogan": "A question answering system that extracts answers from Wikipedia to questions posed in natural language.",
|
||||
"github": "5hirish/adam_qas",
|
||||
"pip": "qas",
|
||||
"code_example": [
|
||||
"git clone https://github.com/5hirish/adam_qas.git",
|
||||
"cd adam_qas",
|
||||
"pip install -r requirements.txt",
|
||||
"python -m qas.adam 'When was linux kernel version 4.0 released ?'"
|
||||
],
|
||||
"code_language": "bash",
|
||||
"thumb": "https://shirishkadam.files.wordpress.com/2018/04/mini_alleviate.png",
|
||||
"author": "Shirish Kadam",
|
||||
"author_links": {
|
||||
"twitter": "5hirish",
|
||||
"github": "5hirish",
|
||||
"website": "https://shirishkadam.com/"
|
||||
},
|
||||
"category": ["standalone"],
|
||||
"tags": ["question-answering", "elasticsearch"]
|
||||
},
|
||||
{
|
||||
"id": "self-attentive-parser",
|
||||
"title": "Berkeley Neural Parser",
|
||||
|
|
@ -2460,20 +2357,6 @@
|
|||
"category": ["nonpython"],
|
||||
"tags": ["javascript"]
|
||||
},
|
||||
{
|
||||
"id": "spacy-raspberry",
|
||||
"title": "spacy-raspberry",
|
||||
"slogan": "64bit Raspberry Pi image for spaCy and neuralcoref",
|
||||
"github": "boehm-e/spacy-raspberry",
|
||||
"thumb": "https://i.imgur.com/VCJMrE6.png",
|
||||
"image": "https://raw.githubusercontent.com/boehm-e/spacy-raspberry/master/imgs/preview.png",
|
||||
"author": "Erwan Boehm",
|
||||
"author_links": {
|
||||
"github": "boehm-e"
|
||||
},
|
||||
"category": ["apis"],
|
||||
"tags": ["raspberrypi"]
|
||||
},
|
||||
{
|
||||
"id": "spacy-wordnet",
|
||||
"title": "spacy-wordnet",
|
||||
|
|
@ -2544,35 +2427,6 @@
|
|||
"category": ["standalone", "pipeline"],
|
||||
"tags": ["linguistics", "computational linguistics", "conll", "conll-u"]
|
||||
},
|
||||
{
|
||||
"id": "spacy-langdetect",
|
||||
"title": "spacy-langdetect",
|
||||
"slogan": "A fully customizable language detection pipeline for spaCy",
|
||||
"description": "This module allows you to add language detection capabilites to your spaCy pipeline. Also supports custom language detectors!",
|
||||
"pip": "spacy-langdetect",
|
||||
"code_example": [
|
||||
"import spacy",
|
||||
"from spacy_langdetect import LanguageDetector",
|
||||
"nlp = spacy.load('en')",
|
||||
"nlp.add_pipe(LanguageDetector(), name='language_detector', last=True)",
|
||||
"text = 'This is an english text.'",
|
||||
"doc = nlp(text)",
|
||||
"# document level language detection. Think of it like average language of the document!",
|
||||
"print(doc._.language)",
|
||||
"# sentence level language detection",
|
||||
"for sent in doc.sents:",
|
||||
" print(sent, sent._.language)"
|
||||
],
|
||||
"code_language": "python",
|
||||
"author": "Abhijit Balaji",
|
||||
"author_links": {
|
||||
"github": "Abhijit-2592",
|
||||
"website": "https://abhijit-2592.github.io/"
|
||||
},
|
||||
"github": "Abhijit-2592/spacy-langdetect",
|
||||
"category": ["pipeline"],
|
||||
"tags": ["language-detection"]
|
||||
},
|
||||
{
|
||||
"id": "ludwig",
|
||||
"title": "Ludwig",
|
||||
|
|
@ -2873,7 +2727,7 @@
|
|||
"slogan": "Information extraction from English and German texts based on predicate logic",
|
||||
"github": "explosion/holmes-extractor",
|
||||
"url": "https://github.com/explosion/holmes-extractor",
|
||||
"description": "Holmes is a Python 3 library that supports a number of use cases involving information extraction from English and German texts, including chatbot, structural extraction, topic matching and supervised document classification. There is a [website demonstrating intelligent search based on topic matching](https://demo.holmes.prod.demos.explosion.services).",
|
||||
"description": "Holmes is a Python 3 library that supports a number of use cases involving information extraction from English and German texts, including chatbot, structural extraction, topic matching and supervised document classification. There is a [website demonstrating intelligent search based on topic matching](https://holmes-demo.explosion.services).",
|
||||
"pip": "holmes-extractor",
|
||||
"category": ["pipeline", "standalone"],
|
||||
"tags": ["chatbots", "text-processing"],
|
||||
|
|
@ -3071,35 +2925,6 @@
|
|||
],
|
||||
"author": "Stefan Daniel Dumitrescu, Andrei-Marius Avram"
|
||||
},
|
||||
{
|
||||
"id": "num_fh",
|
||||
"title": "Numeric Fused-Head",
|
||||
"slogan": "Numeric Fused-Head Identificaiton and Resolution in English",
|
||||
"description": "This package provide a wrapper for the Numeric Fused-Head in English. It provides another information layer on numbers that refer to another entity which is not obvious from the syntactic tree.",
|
||||
"github": "yanaiela/num_fh",
|
||||
"pip": "num_fh",
|
||||
"category": ["pipeline", "research"],
|
||||
"code_example": [
|
||||
"import spacy",
|
||||
"from num_fh import NFH",
|
||||
"nlp = spacy.load('en_core_web_sm')",
|
||||
"nfh = NFH(nlp)",
|
||||
"nlp.add_pipe(nfh, first=False)",
|
||||
"doc = nlp(\"I told you two, that only one of them is the one who will get 2 or 3 icecreams\")",
|
||||
"",
|
||||
"assert doc[16]._.is_nfh == True",
|
||||
"assert doc[18]._.is_nfh == False",
|
||||
"assert doc[3]._.is_deter_nfh == True",
|
||||
"assert doc[16]._.is_deter_nfh == False",
|
||||
"assert len(doc._.nfh) == 4"
|
||||
],
|
||||
"author": "Yanai Elazar",
|
||||
"author_links": {
|
||||
"github": "yanaiela",
|
||||
"twitter": "yanaiela",
|
||||
"website": "https://yanaiela.github.io"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "Healthsea",
|
||||
"title": "Healthsea",
|
||||
|
|
@ -3190,6 +3015,7 @@
|
|||
"from pysbd.utils import PySBDFactory",
|
||||
"",
|
||||
"nlp = spacy.blank('en')",
|
||||
"# Caution: works with spaCy<=2.x.x",
|
||||
"nlp.add_pipe(PySBDFactory(nlp))",
|
||||
"",
|
||||
"doc = nlp('My name is Jonas E. Smith. Please turn to p. 55.')",
|
||||
|
|
@ -4109,6 +3935,21 @@
|
|||
},
|
||||
"category": ["biomedical", "scientific", "research", "pipeline"],
|
||||
"tags": ["clinical"]
|
||||
},
|
||||
{
|
||||
"id": "sent-pattern",
|
||||
"title": "English Interpretation Sentence Pattern",
|
||||
"slogan": "English interpretation for accurate translation from English to Japanese",
|
||||
"description": "This package categorizes English sentences into one of five basic sentence patterns and identifies the subject, verb, object, and other components. The five basic sentence patterns are based on C. T. Onions's Advanced English Syntax and are frequently used when teaching English in Japan.",
|
||||
"github": "lll-lll-lll-lll/sent-pattern",
|
||||
"pip": "sent-pattern",
|
||||
"author": "Shunpei Nakayama",
|
||||
"author_links": {
|
||||
"twitter": "ExZ79575296",
|
||||
"github": "lll-lll-lll-lll"
|
||||
},
|
||||
"category": ["pipeline"],
|
||||
"tags": ["interpretation", "ja"]
|
||||
}
|
||||
],
|
||||
|
||||
|
|
|
|||
|
|
@ -120,8 +120,8 @@ const AlertSpace = ({ nightly, legacy }) => {
|
|||
}
|
||||
|
||||
const navAlert = (
|
||||
<Link to="/usage/v3-3" hidden>
|
||||
<strong>💥 Out now:</strong> spaCy v3.3
|
||||
<Link to="/usage/v3-4" hidden>
|
||||
<strong>💥 Out now:</strong> spaCy v3.4
|
||||
</Link>
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -142,10 +142,10 @@ const UniverseContent = ({ content = [], categories, theme, pageContext, mdxComp
|
|||
The Universe database is open-source and collected in a simple JSON file.
|
||||
For more details on the formats and available fields, see the documentation.
|
||||
Looking for inspiration your own spaCy plugin or extension? Check out the
|
||||
<Link to={github() + '/labels/project%20idea'} hideIcon ws>
|
||||
<InlineCode>project idea</InlineCode>
|
||||
<Link to={"https://github.com/explosion/spaCy/discussions/categories/new-features-project-ideas/"} hideIcon ws>
|
||||
project idea
|
||||
</Link>
|
||||
label on the issue tracker.
|
||||
section in Discussions.
|
||||
</p>
|
||||
|
||||
<InlineList>
|
||||
|
|
|
|||
|
|
@ -24,6 +24,8 @@ const CUDA = {
|
|||
'11.3': 'cuda113',
|
||||
'11.4': 'cuda114',
|
||||
'11.5': 'cuda115',
|
||||
'11.6': 'cuda116',
|
||||
'11.7': 'cuda117',
|
||||
}
|
||||
const LANG_EXTRAS = ['ja'] // only for languages with models
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user