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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | David Weßling |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 27.09.19 |
| GitHub username | EarlGreyT |
| Website (optional) | |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Moshe Hazoom |
| Company name (if applicable) | Amenity Analytics |
| Title or role (if applicable) | NLP Engineer |
| Date | 2019-09-15 |
| GitHub username | Hazoom |
| Website (optional) | |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [ ] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Jaydeep Borkar |
| Company name (if applicable) | Pune University, India |
| Title or role (if applicable) | CS Undergrad |
| Date | 9/26/2019 |
| GitHub username | jaydeepborkar |
| Website (optional) | http://jaydeepborkar.github.io |

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@ -1,106 +0,0 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | ------------------------- |
| Name | Sean Löfgren |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2019-09-17 |
| GitHub username | seanBE |
| Website (optional) | http://seanbe.github.io |

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@ -1,106 +0,0 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Em Zhan |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2019-09-25 |
| GitHub username | zqianem |
| Website (optional) | |

5
CONTRIBUTING.md
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@ -73,8 +73,9 @@ issue body. A few more tips:
### Issue labels
[See this page](https://github.com/explosion/spaCy/labels) for an overview of
the system we use to tag our issues and pull requests.
To distinguish issues that are opened by us, the maintainers, we usually add a
💫 to the title. [See this page](https://github.com/explosion/spaCy/labels)
for an overview of the system we use to tag our issues and pull requests.
## Contributing to the code base

16
Makefile
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@ -1,17 +1,7 @@
SHELL := /bin/bash
sha = $(shell "git" "rev-parse" "--short" "HEAD")
version = $(shell "bin/get-version.sh")
wheel = spacy-$(version)-cp36-cp36m-linux_x86_64.whl
dist/spacy.pex : dist/spacy-$(sha).pex
cp dist/spacy-$(sha).pex dist/spacy.pex
chmod a+rx dist/spacy.pex
dist/spacy-$(sha).pex : dist/$(wheel)
env3.6/bin/python -m pip install pex==1.5.3
env3.6/bin/pex pytest dist/$(wheel) -e spacy -o dist/spacy-$(sha).pex
dist/$(wheel) : setup.py spacy/*.py* spacy/*/*.py*
dist/spacy.pex : spacy/*.py* spacy/*/*.py*
python3.6 -m venv env3.6
source env3.6/bin/activate
env3.6/bin/pip install wheel
@ -19,6 +9,10 @@ dist/$(wheel) : setup.py spacy/*.py* spacy/*/*.py*
env3.6/bin/python setup.py build_ext --inplace
env3.6/bin/python setup.py sdist
env3.6/bin/python setup.py bdist_wheel
env3.6/bin/python -m pip install pex==1.5.3
env3.6/bin/pex pytest dist/*.whl -e spacy -o dist/spacy-$(sha).pex
cp dist/spacy-$(sha).pex dist/spacy.pex
chmod a+rx dist/spacy.pex
.PHONY : clean

13
README.md
View File

@ -49,12 +49,9 @@ It's commercial open-source software, released under the MIT license.
## 💬 Where to ask questions
The spaCy project is maintained by [@honnibal](https://github.com/honnibal)
and [@ines](https://github.com/ines), along with core contributors
[@svlandeg](https://github.com/svlandeg) and
[@adrianeboyd](https://github.com/adrianeboyd). Please understand that we won't
be able to provide individual support via email. We also believe that help is
much more valuable if it's shared publicly, so that more people can benefit
from it.
and [@ines](https://github.com/ines). Please understand that we won't be able
to provide individual support via email. We also believe that help is much more
valuable if it's shared publicly, so that more people can benefit from it.
| Type | Platforms |
| ------------------------ | ------------------------------------------------------ |
@ -175,8 +172,8 @@ python -m spacy download en_core_web_sm
python -m spacy download en
# pip install .tar.gz archive from path or URL
pip install /Users/you/en_core_web_sm-2.2.0.tar.gz
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz
pip install /Users/you/en_core_web_sm-2.1.0.tar.gz
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.1.0/en_core_web_sm-2.1.0.tar.gz
```
### Loading and using models

18
azure-pipelines.yml
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@ -79,24 +79,14 @@ jobs:
# Downgrading pip is necessary to prevent a wheel version incompatiblity.
# Might be fixed in the future or some other way, so investigate again.
- script: |
python -m pip install -U pip==18.1 setuptools
python -m pip install --upgrade pip==18.1
pip install -r requirements.txt
displayName: 'Install dependencies'
- script: |
python setup.py build_ext --inplace
python setup.py sdist --formats=gztar
displayName: 'Compile and build sdist'
pip install -e .
displayName: 'Build and install'
- task: DeleteFiles@1
inputs:
contents: 'spacy'
displayName: 'Delete source directory'
- bash: |
SDIST=$(python -c "import os;print(os.listdir('./dist')[-1])" 2>&1)
pip install dist/$SDIST
displayName: 'Install from sdist'
- script: python -m pytest --pyargs spacy
- script: python -m pytest --tb=native spacy
displayName: 'Run tests'

12
bin/get-version.sh
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@ -1,12 +0,0 @@
#!/usr/bin/env bash
set -e
version=$(grep "__version__ = " spacy/about.py)
version=${version/__version__ = }
version=${version/\'/}
version=${version/\'/}
version=${version/\"/}
version=${version/\"/}
echo $version

36
bin/ud/run_eval.py
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@ -7,16 +7,14 @@ import datetime
from pathlib import Path
import xml.etree.ElementTree as ET
import conll17_ud_eval
from ud_train import write_conllu
from spacy.cli.ud import conll17_ud_eval
from spacy.cli.ud.ud_train import write_conllu
from spacy.lang.lex_attrs import word_shape
from spacy.util import get_lang_class
# All languages in spaCy - in UD format (note that Norwegian is 'no' instead of 'nb')
ALL_LANGUAGES = ("af, ar, bg, bn, ca, cs, da, de, el, en, es, et, fa, fi, fr,"
"ga, he, hi, hr, hu, id, is, it, ja, kn, ko, lt, lv, mr, no,"
"nl, pl, pt, ro, ru, si, sk, sl, sq, sr, sv, ta, te, th, tl,"
"tr, tt, uk, ur, vi, zh")
ALL_LANGUAGES = "ar, ca, da, de, el, en, es, fa, fi, fr, ga, he, hi, hr, hu, id, " \
"it, ja, no, nl, pl, pt, ro, ru, sv, tr, ur, vi, zh"
# Non-parsing tasks that will be evaluated (works for default models)
EVAL_NO_PARSE = ['Tokens', 'Words', 'Lemmas', 'Sentences', 'Feats']
@ -75,10 +73,10 @@ def _contains_blinded_text(stats_xml):
tree = ET.parse(stats_xml)
root = tree.getroot()
total_tokens = int(root.find('size/total/tokens').text)
unique_forms = int(root.find('forms').get('unique'))
unique_lemmas = int(root.find('lemmas').get('unique'))
# assume the corpus is largely blinded when there are less than 1% unique tokens
return (unique_forms / total_tokens) < 0.01
return (unique_lemmas / total_tokens) < 0.01
def fetch_all_treebanks(ud_dir, languages, corpus, best_per_language):
@ -264,26 +262,22 @@ def main(out_path, ud_dir, check_parse=False, langs=ALL_LANGUAGES, exclude_train
if not exclude_trained_models:
if 'de' in models:
models['de'].append(load_model('de_core_news_sm'))
models['de'].append(load_model('de_core_news_md'))
if 'el' in models:
models['el'].append(load_model('el_core_news_sm'))
models['el'].append(load_model('el_core_news_md'))
if 'en' in models:
models['en'].append(load_model('en_core_web_sm'))
models['en'].append(load_model('en_core_web_md'))
models['en'].append(load_model('en_core_web_lg'))
if 'es' in models:
models['es'].append(load_model('es_core_news_sm'))
models['es'].append(load_model('es_core_news_md'))
if 'fr' in models:
models['fr'].append(load_model('fr_core_news_sm'))
models['fr'].append(load_model('fr_core_news_md'))
if 'pt' in models:
models['pt'].append(load_model('pt_core_news_sm'))
if 'it' in models:
models['it'].append(load_model('it_core_news_sm'))
if 'nl' in models:
models['nl'].append(load_model('nl_core_news_sm'))
if 'pt' in models:
models['pt'].append(load_model('pt_core_news_sm'))
if 'en' in models:
models['en'].append(load_model('en_core_web_sm'))
models['en'].append(load_model('en_core_web_md'))
models['en'].append(load_model('en_core_web_lg'))
if 'fr' in models:
models['fr'].append(load_model('fr_core_news_sm'))
models['fr'].append(load_model('fr_core_news_md'))
with out_path.open(mode='w', encoding='utf-8') as out_file:
run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks)

8
bin/ud/ud_run_test.py
View File

@ -109,13 +109,15 @@ def write_conllu(docs, file_):
merger = Matcher(docs[0].vocab)
merger.add("SUBTOK", None, [{"DEP": "subtok", "op": "+"}])
for i, doc in enumerate(docs):
matches = []
if doc.is_parsed:
matches = merger(doc)
matches = merger(doc)
spans = [doc[start : end + 1] for _, start, end in matches]
with doc.retokenize() as retokenizer:
for span in spans:
retokenizer.merge(span)
# TODO: This shouldn't be necessary? Should be handled in merge
for word in doc:
if word.i == word.head.i:
word.dep_ = "ROOT"
file_.write("# newdoc id = {i}\n".format(i=i))
for j, sent in enumerate(doc.sents):
file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))

105
bin/ud/ud_train.py
View File

@ -25,7 +25,7 @@ import itertools
import random
import numpy.random
import conll17_ud_eval
from . import conll17_ud_eval
from spacy import lang
from spacy.lang import zh
@ -82,8 +82,6 @@ def read_data(
head = int(head) - 1 if head != "0" else id_
sent["words"].append(word)
sent["tags"].append(tag)
sent["morphology"].append(_parse_morph_string(morph))
sent["morphology"][-1].add("POS_%s" % pos)
sent["heads"].append(head)
sent["deps"].append("ROOT" if dep == "root" else dep)
sent["spaces"].append(space_after == "_")
@ -92,12 +90,10 @@ def read_data(
if oracle_segments:
docs.append(Doc(nlp.vocab, words=sent["words"], spaces=sent["spaces"]))
golds.append(GoldParse(docs[-1], **sent))
assert golds[-1].morphology is not None
sent_annots.append(sent)
if raw_text and max_doc_length and len(sent_annots) >= max_doc_length:
doc, gold = _make_gold(nlp, None, sent_annots)
assert gold.morphology is not None
sent_annots = []
docs.append(doc)
golds.append(gold)
@ -112,17 +108,6 @@ def read_data(
return docs, golds
return docs, golds
def _parse_morph_string(morph_string):
if morph_string == '_':
return set()
output = []
replacements = {'1': 'one', '2': 'two', '3': 'three'}
for feature in morph_string.split('|'):
key, value = feature.split('=')
value = replacements.get(value, value)
value = value.split(',')[0]
output.append('%s_%s' % (key, value.lower()))
return set(output)
def read_conllu(file_):
docs = []
@ -156,8 +141,8 @@ def _make_gold(nlp, text, sent_annots, drop_deps=0.0):
flat = defaultdict(list)
sent_starts = []
for sent in sent_annots:
flat["heads"].extend(len(flat["words"])+head for head in sent["heads"])
for field in ["words", "tags", "deps", "morphology", "entities", "spaces"]:
flat["heads"].extend(len(flat["words"]) + head for head in sent["heads"])
for field in ["words", "tags", "deps", "entities", "spaces"]:
flat[field].extend(sent[field])
sent_starts.append(True)
sent_starts.extend([False] * (len(sent["words"]) - 1))
@ -229,18 +214,11 @@ def write_conllu(docs, file_):
merger = Matcher(docs[0].vocab)
merger.add("SUBTOK", None, [{"DEP": "subtok", "op": "+"}])
for i, doc in enumerate(docs):
matches = []
if doc.is_parsed:
matches = merger(doc)
matches = merger(doc)
spans = [doc[start : end + 1] for _, start, end in matches]
seen_tokens = set()
with doc.retokenize() as retokenizer:
for span in spans:
span_tokens = set(range(span.start, span.end))
if not span_tokens.intersection(seen_tokens):
retokenizer.merge(span)
seen_tokens.update(span_tokens)
retokenizer.merge(span)
file_.write("# newdoc id = {i}\n".format(i=i))
for j, sent in enumerate(doc.sents):
file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))
@ -263,29 +241,27 @@ def write_conllu(docs, file_):
def print_progress(itn, losses, ud_scores):
fields = {
"dep_loss": losses.get("parser", 0.0),
"morph_loss": losses.get("morphologizer", 0.0),
"tag_loss": losses.get("tagger", 0.0),
"words": ud_scores["Words"].f1 * 100,
"sents": ud_scores["Sentences"].f1 * 100,
"tags": ud_scores["XPOS"].f1 * 100,
"uas": ud_scores["UAS"].f1 * 100,
"las": ud_scores["LAS"].f1 * 100,
"morph": ud_scores["Feats"].f1 * 100,
}
header = ["Epoch", "P.Loss", "M.Loss", "LAS", "UAS", "TAG", "MORPH", "SENT", "WORD"]
header = ["Epoch", "Loss", "LAS", "UAS", "TAG", "SENT", "WORD"]
if itn == 0:
print("\t".join(header))
tpl = "\t".join((
"{:d}",
"{dep_loss:.1f}",
"{morph_loss:.1f}",
"{las:.1f}",
"{uas:.1f}",
"{tags:.1f}",
"{morph:.1f}",
"{sents:.1f}",
"{words:.1f}",
))
tpl = "\t".join(
(
"{:d}",
"{dep_loss:.1f}",
"{las:.1f}",
"{uas:.1f}",
"{tags:.1f}",
"{sents:.1f}",
"{words:.1f}",
)
)
print(tpl.format(itn, **fields))
@ -306,27 +282,25 @@ def get_token_conllu(token, i):
head = 0
else:
head = i + (token.head.i - token.i) + 1
features = list(token.morph)
feat_str = []
replacements = {"one": "1", "two": "2", "three": "3"}
for feat in features:
if not feat.startswith("begin") and not feat.startswith("end"):
key, value = feat.split("_", 1)
value = replacements.get(value, value)
feat_str.append("%s=%s" % (key, value.title()))
if not feat_str:
feat_str = "_"
else:
feat_str = "|".join(feat_str)
fields = [str(i+1), token.text, token.lemma_, token.pos_, token.tag_, feat_str,
str(head), token.dep_.lower(), "_", "_"]
fields = [
str(i + 1),
token.text,
token.lemma_,
token.pos_,
token.tag_,
"_",
str(head),
token.dep_.lower(),
"_",
"_",
]
lines.append("\t".join(fields))
return "\n".join(lines)
Token.set_extension("get_conllu_lines", method=get_token_conllu, force=True)
Token.set_extension("begins_fused", default=False, force=True)
Token.set_extension("inside_fused", default=False, force=True)
Token.set_extension("get_conllu_lines", method=get_token_conllu)
Token.set_extension("begins_fused", default=False)
Token.set_extension("inside_fused", default=False)
##################
@ -350,8 +324,7 @@ def load_nlp(corpus, config, vectors=None):
def initialize_pipeline(nlp, docs, golds, config, device):
nlp.add_pipe(nlp.create_pipe("tagger", config={"set_morphology": False}))
nlp.add_pipe(nlp.create_pipe("morphologizer"))
nlp.add_pipe(nlp.create_pipe("tagger"))
nlp.add_pipe(nlp.create_pipe("parser"))
if config.multitask_tag:
nlp.parser.add_multitask_objective("tag")
@ -551,12 +524,14 @@ def main(
out_path = parses_dir / corpus / "epoch-{i}.conllu".format(i=i)
with nlp.use_params(optimizer.averages):
if use_oracle_segments:
parsed_docs, scores = evaluate(nlp, paths.dev.conllu,
paths.dev.conllu, out_path)
parsed_docs, scores = evaluate(
nlp, paths.dev.conllu, paths.dev.conllu, out_path
)
else:
parsed_docs, scores = evaluate(nlp, paths.dev.text,
paths.dev.conllu, out_path)
print_progress(i, losses, scores)
parsed_docs, scores = evaluate(
nlp, paths.dev.text, paths.dev.conllu, out_path
)
print_progress(i, losses, scores)
def _render_parses(i, to_render):

0
examples/pipeline/dummy_entity_linking.py
View File

0
examples/pipeline/wikidata_entity_linking.py
View File

5
examples/training/pretrain_kb.py
View File

@ -8,8 +8,8 @@ For more details, see the documentation:
* Knowledge base: https://spacy.io/api/kb
* Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
Compatible with: spaCy v2.2
Last tested with: v2.2
Compatible with: spaCy vX.X
Last tested with: vX.X
"""
from __future__ import unicode_literals, print_function
@ -73,6 +73,7 @@ def main(vocab_path=None, model=None, output_dir=None, n_iter=50):
input_dim=INPUT_DIM,
desc_width=DESC_WIDTH,
epochs=n_iter,
threshold=0.001,
)
encoder.train(description_list=descriptions, to_print=True)

121
examples/training/textcat_example_data/CC0.txt
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@ -1,121 +0,0 @@
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## Examples of textcat training data
spacy JSON training files were generated from JSONL with:
```
python textcatjsonl_to_trainjson.py -m en file.jsonl .
```
`cooking.json` is an example with mutually-exclusive classes with two labels:
* `baking`
* `not_baking`
`jigsaw-toxic-comment.json` is an example with multiple labels per instance:
* `insult`
* `obscene`
* `severe_toxic`
* `toxic`
### Data Sources
* `cooking.jsonl`: https://cooking.stackexchange.com. The meta IDs link to the
original question as `https://cooking.stackexchange.com/questions/ID`, e.g.,
`https://cooking.stackexchange.com/questions/2` for the first instance.
* `jigsaw-toxic-comment.jsonl`: [Jigsaw Toxic Comments Classification
Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)
### Data Licenses
* `cooking.jsonl`: CC BY-SA 4.0 ([`CC_BY-SA-4.0.txt`](CC_BY-SA-4.0.txt))
* `jigsaw-toxic-comment.jsonl`:
* text: CC BY-SA 3.0 ([`CC_BY-SA-3.0.txt`](CC_BY-SA-3.0.txt))
* annotation: CC0 ([`CC0.txt`](CC0.txt))

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{"cats": {"baking": 0.0, "not_baking": 1.0}, "meta": {"id": "2"}, "text": "How should I cook bacon in an oven?\nI've heard of people cooking bacon in an oven by laying the strips out on a cookie sheet. When using this method, how long should I cook the bacon for, and at what temperature?\n"}
{"cats": {"baking": 0.0, "not_baking": 1.0}, "meta": {"id": "3"}, "text": "What is the difference between white and brown eggs?\nI always use brown extra large eggs, but I can't honestly say why I do this other than habit at this point. Are there any distinct advantages or disadvantages like flavor, shelf life, etc?\n"}
{"cats": {"baking": 0.0, "not_baking": 1.0}, "meta": {"id": "4"}, "text": "What is the difference between baking soda and baking powder?\nAnd can I use one in place of the other in certain recipes?\n"}
{"cats": {"baking": 0.0, "not_baking": 1.0}, "meta": {"id": "5"}, "text": "In a tomato sauce recipe, how can I cut the acidity?\nIt seems that every time I make a tomato sauce for pasta, the sauce is a little bit too acid for my taste. I've tried using sugar or sodium bicarbonate, but I'm not satisfied with the results.\n"}
{"cats": {"baking": 0.0, "not_baking": 1.0}, "meta": {"id": "6"}, "text": "What ingredients (available in specific regions) can I substitute for parsley?\nI have a recipe that calls for fresh parsley. I have substituted other fresh herbs for their dried equivalents but I don't have fresh or dried parsley. Is there something else (ex another dried herb) that I can use instead of parsley?\nI know it is used mainly for looks rather than taste but I have a pasta recipe that calls for 2 tablespoons of parsley in the sauce and then another 2 tablespoons on top when it is done. I know the parsley on top is more for looks but there must be something about the taste otherwise it would call for parsley within the sauce as well.\nI would especially like to hear about substitutes available in Southeast Asia and other parts of the world where the obvious answers (such as cilantro) are not widely available.\n"}
{"cats": {"baking": 0.0, "not_baking": 1.0}, "meta": {"id": "9"}, "text": "What is the internal temperature a steak should be cooked to for Rare/Medium Rare/Medium/Well?\nI'd like to know when to take my steaks off the grill and please everybody.\n"}
{"cats": {"baking": 0.0, "not_baking": 1.0}, "meta": {"id": "11"}, "text": "How should I poach an egg?\nWhat's the best method to poach an egg without it turning into an eggy soupy mess?\n"}
{"cats": {"baking": 0.0, "not_baking": 1.0}, "meta": {"id": "12"}, "text": "How can I make my Ice Cream \"creamier\"\nMy ice cream doesn't feel creamy enough. I got the recipe from Good Eats, and I can't tell if it's just the recipe or maybe that I'm just not getting my \"batter\" cold enough before I try to make it (I let it chill overnight in the refrigerator, but it doesn't always come out of the machine looking like \"soft serve\" as he said on the show - it's usually a little thinner).\nRecipe: http://www.foodnetwork.com/recipes/alton-brown/serious-vanilla-ice-cream-recipe/index.html\nThanks!\n"}
{"cats": {"baking": 1.0, "not_baking": 0.0}, "meta": {"id": "17"}, "text": "How long and at what temperature do the various parts of a chicken need to be cooked?\nI'm interested in baking thighs, legs, breasts and wings. How long do each of these items need to bake and at what temperature?\n"}
{"cats": {"baking": 1.0, "not_baking": 0.0}, "meta": {"id": "27"}, "text": "Do I need to sift flour that is labeled sifted?\nIs there really an advantage to sifting flour that I bought that was labeled 'sifted'?\n"}

2987
examples/training/textcat_example_data/jigsaw-toxic-comment.json
View File

File diff suppressed because it is too large Load Diff

10
examples/training/textcat_example_data/jigsaw-toxic-comment.jsonl
View File

@ -1,10 +0,0 @@
{"meta": {"id": "0000997932d777bf"}, "text": "Explanation\nWhy the edits made under my username Hardcore Metallica Fan were reverted? They weren't vandalisms, just closure on some GAs after I voted at New York Dolls FAC. And please don't remove the template from the talk page since I'm retired now.89.205.38.27", "cats": {"insult": 0, "obscene": 0, "severe_toxic": 0, "toxic": 0}}
{"meta": {"id": "001956c382006abd"}, "text": "I'm Sorry \n\nI'm sorry I screwed around with someones talk page. It was very bad to do. I know how having the templates on their talk page helps you assert your dominance over them. I know I should bow down to the almighty administrators. But then again, I'm going to go play outside....with your mom. 76.122.79.82", "cats": {"insult": 0, "obscene": 0, "severe_toxic": 0, "toxic": 1}}
{"meta": {"id": "0020e7119b96eeeb"}, "text": "Stupid peace of shit stop deleting my stuff asshole go die and fall in a hole go to hell!", "cats": {"insult": 1, "obscene": 1, "severe_toxic": 1, "toxic": 1}}
{"meta": {"id": "000103f0d9cfb60f"}, "text": "D'aww! He matches this background colour I'm seemingly stuck with. Thanks. (talk) 21:51, January 11, 2016 (UTC)", "cats": {"insult": 0, "obscene": 0, "severe_toxic": 0, "toxic": 0}}
{"meta": {"id": "001dc38a83d420cf"}, "text": "GET FUCKED UP. GET FUCKEEED UP. GOT A DRINK THAT YOU CANT PUT DOWN???/ GET FUCK UP GET FUCKED UP. I'M FUCKED UP RIGHT NOW!", "cats": {"insult": 0, "obscene": 1, "severe_toxic": 0, "toxic": 1}}
{"meta": {"id": "000113f07ec002fd"}, "text": "Hey man, I'm really not trying to edit war. It's just that this guy is constantly removing relevant information and talking to me through edits instead of my talk page. He seems to care more about the formatting than the actual info.", "cats": {"insult": 0, "obscene": 0, "severe_toxic": 0, "toxic": 0}}
{"meta": {"id": "0001b41b1c6bb37e"}, "text": "\"\nMore\nI can't make any real suggestions on improvement - I wondered if the section statistics should be later on, or a subsection of \"\"types of accidents\"\" -I think the references may need tidying so that they are all in the exact same format ie date format etc. I can do that later on, if no-one else does first - if you have any preferences for formatting style on references or want to do it yourself please let me know.\n\nThere appears to be a backlog on articles for review so I guess there may be a delay until a reviewer turns up. It's listed in the relevant form eg Wikipedia:Good_article_nominations#Transport \"", "cats": {"insult": 0, "obscene": 0, "severe_toxic": 0, "toxic": 0}}
{"meta": {"id": "0001d958c54c6e35"}, "text": "You, sir, are my hero. Any chance you remember what page that's on?", "cats": {"insult": 0, "obscene": 0, "severe_toxic": 0, "toxic": 0}}
{"meta": {"id": "00025465d4725e87"}, "text": "\"\n\nCongratulations from me as well, use the tools well.  · talk \"", "cats": {"insult": 0, "obscene": 0, "severe_toxic": 0, "toxic": 0}}
{"meta": {"id": "002264ea4d5f2887"}, "text": "Why can't you believe how fat Artie is? Did you see him on his recent appearence on the Tonight Show with Jay Leno? He looks absolutely AWFUL! If I had to put money on it, I'd say that Artie Lange is a can't miss candidate for the 2007 Dead pool! \n\n \nKindly keep your malicious fingers off of my above comment, . Everytime you remove it, I will repost it!!!", "cats": {"insult": 0, "obscene": 0, "severe_toxic": 0, "toxic": 1}}

53
examples/training/textcat_example_data/textcatjsonl_to_trainjson.py
View File

@ -1,53 +0,0 @@
from pathlib import Path
import plac
import spacy
from spacy.gold import docs_to_json
import srsly
import sys
@plac.annotations(
model=("Model name. Defaults to 'en'.", "option", "m", str),
input_file=("Input file (jsonl)", "positional", None, Path),
output_dir=("Output directory", "positional", None, Path),
n_texts=("Number of texts to convert", "option", "t", int),
)
def convert(model='en', input_file=None, output_dir=None, n_texts=0):
# Load model with tokenizer + sentencizer only
nlp = spacy.load(model)
nlp.disable_pipes(*nlp.pipe_names)
sentencizer = nlp.create_pipe("sentencizer")
nlp.add_pipe(sentencizer, first=True)
texts = []
cats = []
count = 0
if not input_file.exists():
print("Input file not found:", input_file)
sys.exit(1)
else:
with open(input_file) as fileh:
for line in fileh:
data = srsly.json_loads(line)
texts.append(data["text"])
cats.append(data["cats"])
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
else:
output_dir = Path(".")
docs = []
for i, doc in enumerate(nlp.pipe(texts)):
doc.cats = cats[i]
docs.append(doc)
if n_texts > 0 and count == n_texts:
break
count += 1
srsly.write_json(output_dir / input_file.with_suffix(".json"), [docs_to_json(docs)])
if __name__ == "__main__":
plac.call(convert)

4
examples/training/train_entity_linker.py
View File

@ -8,8 +8,8 @@ For more details, see the documentation:
* Training: https://spacy.io/usage/training
* Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
Compatible with: spaCy v2.2
Last tested with: v2.2
Compatible with: spaCy vX.X
Last tested with: vX.X
"""
from __future__ import unicode_literals, print_function

2
examples/training/training-data.json
View File

@ -8,7 +8,7 @@
{
"tokens": [
{
"head": 4,
"head": 44,
"dep": "prep",
"tag": "IN",
"orth": "In",

122
fabfile.py vendored
View File

@ -10,145 +10,113 @@ import sys
PWD = path.dirname(__file__)
ENV = environ["VENV_DIR"] if "VENV_DIR" in environ else ".env"
ENV = environ['VENV_DIR'] if 'VENV_DIR' in environ else '.env'
VENV_DIR = Path(PWD) / ENV
@contextlib.contextmanager
def virtualenv(name, create=False, python="/usr/bin/python3.6"):
def virtualenv(name, create=False, python='/usr/bin/python3.6'):
python = Path(python).resolve()
env_path = VENV_DIR
if create:
if env_path.exists():
shutil.rmtree(str(env_path))
local("{python} -m venv {env_path}".format(python=python, env_path=VENV_DIR))
local('{python} -m venv {env_path}'.format(python=python, env_path=VENV_DIR))
def wrapped_local(cmd, env_vars=[], capture=False, direct=False):
return local(
"source {}/bin/activate && {}".format(env_path, cmd),
shell="/bin/bash",
capture=False,
)
return local('source {}/bin/activate && {}'.format(env_path, cmd),
shell='/bin/bash', capture=False)
yield wrapped_local
def env(lang="python3.6"):
def env(lang='python3.6'):
if VENV_DIR.exists():
local("rm -rf {env}".format(env=VENV_DIR))
if lang.startswith("python3"):
local("{lang} -m venv {env}".format(lang=lang, env=VENV_DIR))
local('rm -rf {env}'.format(env=VENV_DIR))
if lang.startswith('python3'):
local('{lang} -m venv {env}'.format(lang=lang, env=VENV_DIR))
else:
local("{lang} -m pip install virtualenv --no-cache-dir".format(lang=lang))
local(
"{lang} -m virtualenv {env} --no-cache-dir".format(lang=lang, env=VENV_DIR)
)
local('{lang} -m pip install virtualenv --no-cache-dir'.format(lang=lang))
local('{lang} -m virtualenv {env} --no-cache-dir'.format(lang=lang, env=VENV_DIR))
with virtualenv(VENV_DIR) as venv_local:
print(venv_local("python --version", capture=True))
venv_local("pip install --upgrade setuptools --no-cache-dir")
venv_local("pip install pytest --no-cache-dir")
venv_local("pip install wheel --no-cache-dir")
venv_local("pip install -r requirements.txt --no-cache-dir")
venv_local("pip install pex --no-cache-dir")
print(venv_local('python --version', capture=True))
venv_local('pip install --upgrade setuptools --no-cache-dir')
venv_local('pip install pytest --no-cache-dir')
venv_local('pip install wheel --no-cache-dir')
venv_local('pip install -r requirements.txt --no-cache-dir')
venv_local('pip install pex --no-cache-dir')
def install():
with virtualenv(VENV_DIR) as venv_local:
venv_local("pip install dist/*.tar.gz")
venv_local('pip install dist/*.tar.gz')
def make():
with lcd(path.dirname(__file__)):
local(
"export PYTHONPATH=`pwd` && source .env/bin/activate && python setup.py build_ext --inplace",
shell="/bin/bash",
)
local('export PYTHONPATH=`pwd` && source .env/bin/activate && python setup.py build_ext --inplace',
shell='/bin/bash')
def sdist():
with virtualenv(VENV_DIR) as venv_local:
with lcd(path.dirname(__file__)):
local("python -m pip install -U setuptools srsly")
local("python setup.py sdist")
local('python -m pip install -U setuptools')
local('python setup.py sdist')
def wheel():
with virtualenv(VENV_DIR) as venv_local:
with lcd(path.dirname(__file__)):
venv_local("python setup.py bdist_wheel")
venv_local('python setup.py bdist_wheel')
def pex():
with virtualenv(VENV_DIR) as venv_local:
with lcd(path.dirname(__file__)):
sha = local("git rev-parse --short HEAD", capture=True)
venv_local(
"pex dist/*.whl -e spacy -o dist/spacy-%s.pex" % sha, direct=True
)
sha = local('git rev-parse --short HEAD', capture=True)
venv_local('pex dist/*.whl -e spacy -o dist/spacy-%s.pex' % sha,
direct=True)
def clean():
with lcd(path.dirname(__file__)):
local("rm -f dist/*.whl")
local("rm -f dist/*.pex")
local('rm -f dist/*.whl')
local('rm -f dist/*.pex')
with virtualenv(VENV_DIR) as venv_local:
venv_local("python setup.py clean --all")
venv_local('python setup.py clean --all')
def test():
with virtualenv(VENV_DIR) as venv_local:
with lcd(path.dirname(__file__)):
venv_local("pytest -x spacy/tests")
venv_local('pytest -x spacy/tests')
def train():
args = environ.get("SPACY_TRAIN_ARGS", "")
args = environ.get('SPACY_TRAIN_ARGS', '')
with virtualenv(VENV_DIR) as venv_local:
venv_local("spacy train {args}".format(args=args))
venv_local('spacy train {args}'.format(args=args))
def conll17(treebank_dir, experiment_dir, vectors_dir, config, corpus=""):
is_not_clean = local("git status --porcelain", capture=True)
def conll17(treebank_dir, experiment_dir, vectors_dir, config, corpus=''):
is_not_clean = local('git status --porcelain', capture=True)
if is_not_clean:
print("Repository is not clean")
print(is_not_clean)
sys.exit(1)
git_sha = local("git rev-parse --short HEAD", capture=True)
config_checksum = local("sha256sum {config}".format(config=config), capture=True)
experiment_dir = Path(experiment_dir) / "{}--{}".format(
config_checksum[:6], git_sha
)
git_sha = local('git rev-parse --short HEAD', capture=True)
config_checksum = local('sha256sum {config}'.format(config=config), capture=True)
experiment_dir = Path(experiment_dir) / '{}--{}'.format(config_checksum[:6], git_sha)
if not experiment_dir.exists():
experiment_dir.mkdir()
test_data_dir = Path(treebank_dir) / "ud-test-v2.0-conll2017"
test_data_dir = Path(treebank_dir) / 'ud-test-v2.0-conll2017'
assert test_data_dir.exists()
assert test_data_dir.is_dir()
if corpus:
corpora = [corpus]
else:
corpora = ["UD_English", "UD_Chinese", "UD_Japanese", "UD_Vietnamese"]
corpora = ['UD_English', 'UD_Chinese', 'UD_Japanese', 'UD_Vietnamese']
local(
"cp {config} {experiment_dir}/config.json".format(
config=config, experiment_dir=experiment_dir
)
)
local('cp {config} {experiment_dir}/config.json'.format(config=config, experiment_dir=experiment_dir))
with virtualenv(VENV_DIR) as venv_local:
for corpus in corpora:
venv_local(
"spacy ud-train {treebank_dir} {experiment_dir} {config} {corpus} -v {vectors_dir}".format(
treebank_dir=treebank_dir,
experiment_dir=experiment_dir,
config=config,
corpus=corpus,
vectors_dir=vectors_dir,
)
)
venv_local(
"spacy ud-run-test {test_data_dir} {experiment_dir} {corpus}".format(
test_data_dir=test_data_dir,
experiment_dir=experiment_dir,
config=config,
corpus=corpus,
)
)
venv_local('spacy ud-train {treebank_dir} {experiment_dir} {config} {corpus} -v {vectors_dir}'.format(
treebank_dir=treebank_dir, experiment_dir=experiment_dir, config=config, corpus=corpus, vectors_dir=vectors_dir))
venv_local('spacy ud-run-test {test_data_dir} {experiment_dir} {corpus}'.format(
test_data_dir=test_data_dir, experiment_dir=experiment_dir, config=config, corpus=corpus))

6
requirements.txt
View File

@ -1,8 +1,8 @@
# Our libraries
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=7.1.1,<7.2.0
blis>=0.4.0,<0.5.0
preshed>=2.0.1,<2.1.0
thinc>=7.0.8,<7.1.0
blis>=0.2.2,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.2.0,<1.1.0
srsly>=0.1.0,<1.1.0

11
setup.py
View File

@ -27,7 +27,7 @@ def is_new_osx():
return False
PACKAGE_DATA = {"": ["*.pyx", "*.pxd", "*.txt", "*.tokens", "*.json", "*.json.gz"]}
PACKAGE_DATA = {"": ["*.pyx", "*.pxd", "*.txt", "*.tokens", "*.json"]}
PACKAGES = find_packages()
@ -43,7 +43,6 @@ MOD_NAMES = [
"spacy.kb",
"spacy.morphology",
"spacy.pipeline.pipes",
"spacy.pipeline.morphologizer",
"spacy.syntax.stateclass",
"spacy.syntax._state",
"spacy.tokenizer",
@ -57,7 +56,6 @@ MOD_NAMES = [
"spacy.tokens.doc",
"spacy.tokens.span",
"spacy.tokens.token",
"spacy.tokens.morphanalysis",
"spacy.tokens._retokenize",
"spacy.matcher.matcher",
"spacy.matcher.phrasematcher",
@ -247,9 +245,9 @@ def setup_package():
"numpy>=1.15.0",
"murmurhash>=0.28.0,<1.1.0",
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"thinc>=7.1.1,<7.2.0",
"blis>=0.4.0,<0.5.0",
"preshed>=2.0.1,<2.1.0",
"thinc>=7.0.8,<7.1.0",
"blis>=0.2.2,<0.3.0",
"plac<1.0.0,>=0.9.6",
"requests>=2.13.0,<3.0.0",
"wasabi>=0.2.0,<1.1.0",
@ -283,6 +281,7 @@ def setup_package():
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",

161
spacy/_ml.py
View File

@ -15,7 +15,7 @@ from thinc.api import uniqued, wrap, noop
from thinc.api import with_square_sequences
from thinc.linear.linear import LinearModel
from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module, copy_array
from thinc.neural.util import get_array_module
from thinc.neural.optimizers import Adam
from thinc import describe
@ -286,7 +286,10 @@ def link_vectors_to_models(vocab):
if vectors.name is None:
vectors.name = VECTORS_KEY
if vectors.data.size != 0:
user_warning(Warnings.W020.format(shape=vectors.data.shape))
print(
"Warning: Unnamed vectors -- this won't allow multiple vectors "
"models to be loaded. (Shape: (%d, %d))" % vectors.data.shape
)
ops = Model.ops
for word in vocab:
if word.orth in vectors.key2row:
@ -320,9 +323,6 @@ def Tok2Vec(width, embed_size, **kwargs):
pretrained_vectors = kwargs.get("pretrained_vectors", None)
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
subword_features = kwargs.get("subword_features", True)
char_embed = kwargs.get("char_embed", False)
if char_embed:
subword_features = False
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
@ -362,14 +362,6 @@ def Tok2Vec(width, embed_size, **kwargs):
>> LN(Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH),
)
elif char_embed:
embed = concatenate_lists(
CharacterEmbed(nM=64, nC=8),
FeatureExtracter(cols) >> with_flatten(norm),
)
reduce_dimensions = LN(
Maxout(width, 64 * 8 + width, pieces=cnn_maxout_pieces)
)
else:
embed = norm
@ -377,15 +369,9 @@ def Tok2Vec(width, embed_size, **kwargs):
ExtractWindow(nW=1)
>> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
)
if char_embed:
tok2vec = embed >> with_flatten(
reduce_dimensions >> convolution ** conv_depth, pad=conv_depth
)
else:
tok2vec = FeatureExtracter(cols) >> with_flatten(
embed >> convolution ** conv_depth, pad=conv_depth
)
tok2vec = FeatureExtracter(cols) >> with_flatten(
embed >> convolution ** conv_depth, pad=conv_depth
)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
@ -518,46 +504,6 @@ def getitem(i):
return layerize(getitem_fwd)
@describe.attributes(
W=Synapses("Weights matrix", lambda obj: (obj.nO, obj.nI), lambda W, ops: None)
)
class MultiSoftmax(Affine):
"""Neural network layer that predicts several multi-class attributes at once.
For instance, we might predict one class with 6 variables, and another with 5.
We predict the 11 neurons required for this, and then softmax them such
that columns 0-6 make a probability distribution and coumns 6-11 make another.
"""
name = "multisoftmax"
def __init__(self, out_sizes, nI=None, **kwargs):
Model.__init__(self, **kwargs)
self.out_sizes = out_sizes
self.nO = sum(out_sizes)
self.nI = nI
def predict(self, input__BI):
output__BO = self.ops.affine(self.W, self.b, input__BI)
i = 0
for out_size in self.out_sizes:
self.ops.softmax(output__BO[:, i : i + out_size], inplace=True)
i += out_size
return output__BO
def begin_update(self, input__BI, drop=0.0):
output__BO = self.predict(input__BI)
def finish_update(grad__BO, sgd=None):
self.d_W += self.ops.gemm(grad__BO, input__BI, trans1=True)
self.d_b += grad__BO.sum(axis=0)
grad__BI = self.ops.gemm(grad__BO, self.W)
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return grad__BI
return output__BO, finish_update
def build_tagger_model(nr_class, **cfg):
embed_size = util.env_opt("embed_size", 2000)
if "token_vector_width" in cfg:
@ -584,33 +530,6 @@ def build_tagger_model(nr_class, **cfg):
return model
def build_morphologizer_model(class_nums, **cfg):
embed_size = util.env_opt("embed_size", 7000)
if "token_vector_width" in cfg:
token_vector_width = cfg["token_vector_width"]
else:
token_vector_width = util.env_opt("token_vector_width", 128)
pretrained_vectors = cfg.get("pretrained_vectors")
char_embed = cfg.get("char_embed", True)
with Model.define_operators({">>": chain, "+": add, "**": clone}):
if "tok2vec" in cfg:
tok2vec = cfg["tok2vec"]
else:
tok2vec = Tok2Vec(
token_vector_width,
embed_size,
char_embed=char_embed,
pretrained_vectors=pretrained_vectors,
)
softmax = with_flatten(MultiSoftmax(class_nums, token_vector_width))
softmax.out_sizes = class_nums
model = tok2vec >> softmax
model.nI = None
model.tok2vec = tok2vec
model.softmax = softmax
return model
@layerize
def SpacyVectors(docs, drop=0.0):
batch = []
@ -801,8 +720,7 @@ def concatenate_lists(*layers, **kwargs): # pragma: no cover
concat = concatenate(*layers)
def concatenate_lists_fwd(Xs, drop=0.0):
if drop is not None:
drop *= drop_factor
drop *= drop_factor
lengths = ops.asarray([len(X) for X in Xs], dtype="i")
flat_y, bp_flat_y = concat.begin_update(Xs, drop=drop)
ys = ops.unflatten(flat_y, lengths)
@ -892,67 +810,6 @@ def _replace_word(word, random_words, mask="[MASK]"):
return word
def _uniform_init(lo, hi):
def wrapped(W, ops):
copy_array(W, ops.xp.random.uniform(lo, hi, W.shape))
return wrapped
@describe.attributes(
nM=Dimension("Vector dimensions"),
nC=Dimension("Number of characters per word"),
vectors=Synapses(
"Embed matrix", lambda obj: (obj.nC, obj.nV, obj.nM), _uniform_init(-0.1, 0.1)
),
d_vectors=Gradient("vectors"),
)
class CharacterEmbed(Model):
def __init__(self, nM=None, nC=None, **kwargs):
Model.__init__(self, **kwargs)
self.nM = nM
self.nC = nC
@property
def nO(self):
return self.nM * self.nC
@property
def nV(self):
return 256
def begin_update(self, docs, drop=0.0):
if not docs:
return []
ids = []
output = []
weights = self.vectors
# This assists in indexing; it's like looping over this dimension.
# Still consider this weird witch craft...But thanks to Mark Neumann
# for the tip.
nCv = self.ops.xp.arange(self.nC)
for doc in docs:
doc_ids = doc.to_utf8_array(nr_char=self.nC)
doc_vectors = self.ops.allocate((len(doc), self.nC, self.nM))
# Let's say I have a 2d array of indices, and a 3d table of data. What numpy
# incantation do I chant to get
# output[i, j, k] == data[j, ids[i, j], k]?
doc_vectors[:, nCv] = weights[nCv, doc_ids[:, nCv]]
output.append(doc_vectors.reshape((len(doc), self.nO)))
ids.append(doc_ids)
def backprop_character_embed(d_vectors, sgd=None):
gradient = self.d_vectors
for doc_ids, d_doc_vectors in zip(ids, d_vectors):
d_doc_vectors = d_doc_vectors.reshape((len(doc_ids), self.nC, self.nM))
gradient[nCv, doc_ids[:, nCv]] += d_doc_vectors[:, nCv]
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return None
return output, backprop_character_embed
def get_cossim_loss(yh, y):
# Add a small constant to avoid 0 vectors
yh = yh + 1e-8

12
spacy/about.py
View File

@ -1,12 +1,16 @@
# inspired from:
# https://python-packaging-user-guide.readthedocs.org/en/latest/single_source_version/
# https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
# fmt: off
__title__ = "spacy"
__version__ = "2.2.0.dev15"
__summary__ = "Industrial-strength Natural Language Processing (NLP) in Python"
__version__ = "2.1.8"
__summary__ = "Industrial-strength Natural Language Processing (NLP) with Python and Cython"
__uri__ = "https://spacy.io"
__author__ = "Explosion"
__author__ = "Explosion AI"
__email__ = "contact@explosion.ai"
__license__ = "MIT"
__release__ = False
__release__ = True
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

6
spacy/attrs.pyx
View File

@ -144,12 +144,8 @@ def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):
for name, value in stringy_attrs.items():
if isinstance(name, int):
int_key = name
elif name in IDS:
int_key = IDS[name]
elif name.upper() in IDS:
int_key = IDS[name.upper()]
else:
continue
int_key = IDS[name.upper()]
if strings_map is not None and isinstance(value, basestring):
if hasattr(strings_map, 'add'):
value = strings_map.add(value)

66
spacy/cli/debug_data.py
View File

@ -34,6 +34,12 @@ BLANK_MODEL_THRESHOLD = 2000
str,
),
ignore_warnings=("Ignore warnings, only show stats and errors", "flag", "IW", bool),
ignore_validation=(
"Don't exit if JSON format validation fails",
"flag",
"IV",
bool,
),
verbose=("Print additional information and explanations", "flag", "V", bool),
no_format=("Don't pretty-print the results", "flag", "NF", bool),
)
@ -44,14 +50,10 @@ def debug_data(
base_model=None,
pipeline="tagger,parser,ner",
ignore_warnings=False,
ignore_validation=False,
verbose=False,
no_format=False,
):
"""
Analyze, debug and validate your training and development data, get useful
stats, and find problems like invalid entity annotations, cyclic
dependencies, low data labels and more.
"""
msg = Printer(pretty=not no_format, ignore_warnings=ignore_warnings)
# Make sure all files and paths exists if they are needed
@ -70,9 +72,21 @@ def debug_data(
msg.divider("Data format validation")
# TODO: Validate data format using the JSON schema
# Validate data format using the JSON schema
# TODO: update once the new format is ready
# TODO: move validation to GoldCorpus in order to be able to load from dir
train_data_errors = [] # TODO: validate_json
dev_data_errors = [] # TODO: validate_json
if not train_data_errors:
msg.good("Training data JSON format is valid")
if not dev_data_errors:
msg.good("Development data JSON format is valid")
for error in train_data_errors:
msg.fail("Training data: {}".format(error))
for error in dev_data_errors:
msg.fail("Develoment data: {}".format(error))
if (train_data_errors or dev_data_errors) and not ignore_validation:
sys.exit(1)
# Create the gold corpus to be able to better analyze data
loading_train_error_message = ""
@ -270,7 +284,7 @@ def debug_data(
if "textcat" in pipeline:
msg.divider("Text Classification")
labels = [label for label in gold_train_data["cats"]]
labels = [label for label in gold_train_data["textcat"]]
model_labels = _get_labels_from_model(nlp, "textcat")
new_labels = [l for l in labels if l not in model_labels]
existing_labels = [l for l in labels if l in model_labels]
@ -281,45 +295,13 @@ def debug_data(
)
if new_labels:
labels_with_counts = _format_labels(
gold_train_data["cats"].most_common(), counts=True
gold_train_data["textcat"].most_common(), counts=True
)
msg.text("New: {}".format(labels_with_counts), show=verbose)
if existing_labels:
msg.text(
"Existing: {}".format(_format_labels(existing_labels)), show=verbose
)
if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]):
msg.fail(
"The train and dev labels are not the same. "
"Train labels: {}. "
"Dev labels: {}.".format(
_format_labels(gold_train_data["cats"]),
_format_labels(gold_dev_data["cats"]),
)
)
if gold_train_data["n_cats_multilabel"] > 0:
msg.info(
"The train data contains instances without "
"mutually-exclusive classes. Use '--textcat-multilabel' "
"when training."
)
if gold_dev_data["n_cats_multilabel"] == 0:
msg.warn(
"Potential train/dev mismatch: the train data contains "
"instances without mutually-exclusive classes while the "
"dev data does not."
)
else:
msg.info(
"The train data contains only instances with "
"mutually-exclusive classes."
)
if gold_dev_data["n_cats_multilabel"] > 0:
msg.fail(
"Train/dev mismatch: the dev data contains instances "
"without mutually-exclusive classes while the train data "
"contains only instances with mutually-exclusive classes."
)
if "tagger" in pipeline:
msg.divider("Part-of-speech Tagging")
@ -348,7 +330,6 @@ def debug_data(
)
if "parser" in pipeline:
has_low_data_warning = False
msg.divider("Dependency Parsing")
# profile sentence length
@ -537,7 +518,6 @@ def _compile_gold(train_docs, pipeline):
"n_sents": 0,
"n_nonproj": 0,
"n_cycles": 0,
"n_cats_multilabel": 0,
"texts": set(),
}
for doc, gold in train_docs:
@ -560,8 +540,6 @@ def _compile_gold(train_docs, pipeline):
data["ner"]["-"] += 1
if "textcat" in pipeline:
data["cats"].update(gold.cats)
if list(gold.cats.values()).count(1.0) != 1:
data["n_cats_multilabel"] += 1
if "tagger" in pipeline:
data["tags"].update([x for x in gold.tags if x is not None])
if "parser" in pipeline:

27
spacy/cli/download.py
View File

@ -28,16 +28,6 @@ def download(model, direct=False, *pip_args):
can be shortcut, model name or, if --direct flag is set, full model name
with version. For direct downloads, the compatibility check will be skipped.
"""
if not require_package("spacy") and "--no-deps" not in pip_args:
msg.warn(
"Skipping model package dependencies and setting `--no-deps`. "
"You don't seem to have the spaCy package itself installed "
"(maybe because you've built from source?), so installing the "
"model dependencies would cause spaCy to be downloaded, which "
"probably isn't what you want. If the model package has other "
"dependencies, you'll have to install them manually."
)
pip_args = pip_args + ("--no-deps",)
dl_tpl = "{m}-{v}/{m}-{v}.tar.gz#egg={m}=={v}"
if direct:
components = model.split("-")
@ -82,15 +72,12 @@ def download(model, direct=False, *pip_args):
# is_package check currently fails, because pkg_resources.working_set
# is not refreshed automatically (see #3923). We're trying to work
# around this here be requiring the package explicitly.
require_package(model_name)
def require_package(name):
try:
pkg_resources.working_set.require(name)
return True
except: # noqa: E722
return False
try:
pkg_resources.working_set.require(model_name)
except: # noqa: E722
# Maybe it's possible to remove this – mostly worried about cross-
# platform and cross-Python copmpatibility here
pass
def get_json(url, desc):
@ -130,7 +117,7 @@ def get_version(model, comp):
def download_model(filename, user_pip_args=None):
download_url = about.__download_url__ + "/" + filename
pip_args = ["--no-cache-dir"]
pip_args = ["--no-cache-dir", "--no-deps"]
if user_pip_args:
pip_args.extend(user_pip_args)
cmd = [sys.executable, "-m", "pip", "install"] + pip_args + [download_url]

1
spacy/cli/evaluate.py
View File

@ -61,7 +61,6 @@ def evaluate(
"NER P": "%.2f" % scorer.ents_p,
"NER R": "%.2f" % scorer.ents_r,
"NER F": "%.2f" % scorer.ents_f,
"Textcat": "%.2f" % scorer.textcat_score,
}
msg.table(results, title="Results")

24
spacy/cli/init_model.py
View File

@ -35,13 +35,6 @@ msg = Printer()
clusters_loc=("Optional location of brown clusters data", "option", "c", str),
vectors_loc=("Optional vectors file in Word2Vec format", "option", "v", str),
prune_vectors=("Optional number of vectors to prune to", "option", "V", int),
vectors_name=(
"Optional name for the word vectors, e.g. en_core_web_lg.vectors",
"option",
"vn",
str,
),
model_name=("Optional name for the model meta", "option", "mn", str),
)
def init_model(
lang,
@ -51,8 +44,6 @@ def init_model(
jsonl_loc=None,
vectors_loc=None,
prune_vectors=-1,
vectors_name=None,
model_name=None,
):
"""
Create a new model from raw data, like word frequencies, Brown clusters
@ -84,10 +75,10 @@ def init_model(
lex_attrs = read_attrs_from_deprecated(freqs_loc, clusters_loc)
with msg.loading("Creating model..."):
nlp = create_model(lang, lex_attrs, name=model_name)
nlp = create_model(lang, lex_attrs)
msg.good("Successfully created model")
if vectors_loc is not None:
add_vectors(nlp, vectors_loc, prune_vectors, vectors_name)
add_vectors(nlp, vectors_loc, prune_vectors)
vec_added = len(nlp.vocab.vectors)
lex_added = len(nlp.vocab)
msg.good(
@ -147,7 +138,7 @@ def read_attrs_from_deprecated(freqs_loc, clusters_loc):
return lex_attrs
def create_model(lang, lex_attrs, name=None):
def create_model(lang, lex_attrs):
lang_class = get_lang_class(lang)
nlp = lang_class()
for lexeme in nlp.vocab:
@ -166,12 +157,10 @@ def create_model(lang, lex_attrs, name=None):
else:
oov_prob = DEFAULT_OOV_PROB
nlp.vocab.cfg.update({"oov_prob": oov_prob})
if name:
nlp.meta["name"] = name
return nlp
def add_vectors(nlp, vectors_loc, prune_vectors, name=None):
def add_vectors(nlp, vectors_loc, prune_vectors):
vectors_loc = ensure_path(vectors_loc)
if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open("rb")))
@ -192,10 +181,7 @@ def add_vectors(nlp, vectors_loc, prune_vectors, name=None):
lexeme.is_oov = False
if vectors_data is not None:
nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
if name is None:
nlp.vocab.vectors.name = "%s_model.vectors" % nlp.meta["lang"]
else:
nlp.vocab.vectors.name = name
nlp.vocab.vectors.name = "%s_model.vectors" % nlp.meta["lang"]
nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
if prune_vectors >= 1:
nlp.vocab.prune_vectors(prune_vectors)

259
spacy/cli/train.py
View File

@ -21,35 +21,54 @@ from .. import about
@plac.annotations(
# fmt: off
lang=("Model language", "positional", None, str),
output_path=("Output directory to store model in", "positional", None, Path),
train_path=("Location of JSON-formatted training data", "positional", None, Path),
dev_path=("Location of JSON-formatted development data", "positional", None, Path),
raw_text=("Path to jsonl file with unlabelled text documents.", "option", "rt", Path),
raw_text=(
"Path to jsonl file with unlabelled text documents.",
"option",
"rt",
Path,
),
base_model=("Name of model to update (optional)", "option", "b", str),
pipeline=("Comma-separated names of pipeline components", "option", "p", str),
vectors=("Model to load vectors from", "option", "v", str),
n_iter=("Number of iterations", "option", "n", int),
n_early_stopping=("Maximum number of training epochs without dev accuracy improvement", "option", "ne", int),
n_early_stopping=(
"Maximum number of training epochs without dev accuracy improvement",
"option",
"ne",
int,
),
n_examples=("Number of examples", "option", "ns", int),
use_gpu=("Use GPU", "option", "g", int),
version=("Model version", "option", "V", str),
meta_path=("Optional path to meta.json to use as base.", "option", "m", Path),
init_tok2vec=("Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.", "option", "t2v", Path),
parser_multitasks=("Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'", "option", "pt", str),
entity_multitasks=("Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'", "option", "et", str),
init_tok2vec=(
"Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.",
"option",
"t2v",
Path,
),
parser_multitasks=(
"Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'",
"option",
"pt",
str,
),
entity_multitasks=(
"Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'",
"option",
"et",
str,
),
noise_level=("Amount of corruption for data augmentation", "option", "nl", float),
orth_variant_level=("Amount of orthography variation for data augmentation", "option", "ovl", float),
eval_beam_widths=("Beam widths to evaluate, e.g. 4,8", "option", "bw", str),
gold_preproc=("Use gold preprocessing", "flag", "G", bool),
learn_tokens=("Make parser learn gold-standard tokenization", "flag", "T", bool),
textcat_multilabel=("Textcat classes aren't mutually exclusive (multilabel)", "flag", "TML", bool),
textcat_arch=("Textcat model architecture", "option", "ta", str),
textcat_positive_label=("Textcat positive label for binary classes with two labels", "option", "tpl", str),
verbose=("Display more information for debug", "flag", "VV", bool),
debug=("Run data diagnostics before training", "flag", "D", bool),
# fmt: on
)
def train(
lang,
@ -70,13 +89,9 @@ def train(
parser_multitasks="",
entity_multitasks="",
noise_level=0.0,
orth_variant_level=0.0,
eval_beam_widths="",
gold_preproc=False,
learn_tokens=False,
textcat_multilabel=False,
textcat_arch="bow",
textcat_positive_label=None,
verbose=False,
debug=False,
):
@ -162,37 +177,9 @@ def train(
if pipe not in nlp.pipe_names:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
else:
pipe_cfg = {}
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
else:
if pipe == "textcat":
textcat_cfg = nlp.get_pipe("textcat").cfg
base_cfg = {
"exclusive_classes": textcat_cfg["exclusive_classes"],
"architecture": textcat_cfg["architecture"],
"positive_label": textcat_cfg["positive_label"],
}
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
if base_cfg != pipe_cfg:
msg.fail(
"The base textcat model configuration does"
"not match the provided training options. "
"Existing cfg: {}, provided cfg: {}".format(
base_cfg, pipe_cfg
),
exits=1,
)
else:
msg.text("Starting with blank model '{}'".format(lang))
lang_cls = util.get_lang_class(lang)
@ -200,12 +187,6 @@ def train(
for pipe in pipeline:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
else:
pipe_cfg = {}
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
@ -246,89 +227,12 @@ def train(
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
# Verify textcat config
if "textcat" in pipeline:
textcat_labels = nlp.get_pipe("textcat").cfg["labels"]
if textcat_positive_label and textcat_positive_label not in textcat_labels:
msg.fail(
"The textcat_positive_label (tpl) '{}' does not match any "
"label in the training data.".format(textcat_positive_label),
exits=1,
)
if textcat_positive_label and len(textcat_labels) != 2:
msg.fail(
"A textcat_positive_label (tpl) '{}' was provided for training "
"data that does not appear to be a binary classification "
"problem with two labels.".format(textcat_positive_label),
exits=1,
)
train_docs = corpus.train_docs(
nlp, noise_level=noise_level, gold_preproc=gold_preproc, max_length=0
)
train_labels = set()
if textcat_multilabel:
multilabel_found = False
for text, gold in train_docs:
train_labels.update(gold.cats.keys())
if list(gold.cats.values()).count(1.0) != 1:
multilabel_found = True
if not multilabel_found and not base_model:
msg.warn(
"The textcat training instances look like they have "
"mutually-exclusive classes. Remove the flag "
"'--textcat-multilabel' to train a classifier with "
"mutually-exclusive classes."
)
if not textcat_multilabel:
for text, gold in train_docs:
train_labels.update(gold.cats.keys())
if list(gold.cats.values()).count(1.0) != 1 and not base_model:
msg.warn(
"Some textcat training instances do not have exactly "
"one positive label. Modifying training options to "
"include the flag '--textcat-multilabel' for classes "
"that are not mutually exclusive."
)
nlp.get_pipe("textcat").cfg["exclusive_classes"] = False
textcat_multilabel = True
break
if base_model and set(textcat_labels) != train_labels:
msg.fail(
"Cannot extend textcat model using data with different "
"labels. Base model labels: {}, training data labels: "
"{}.".format(textcat_labels, list(train_labels)),
exits=1,
)
if textcat_multilabel:
msg.text(
"Textcat evaluation score: ROC AUC score macro-averaged across "
"the labels '{}'".format(", ".join(textcat_labels))
)
elif textcat_positive_label and len(textcat_labels) == 2:
msg.text(
"Textcat evaluation score: F1-score for the "
"label '{}'".format(textcat_positive_label)
)
elif len(textcat_labels) > 1:
if len(textcat_labels) == 2:
msg.warn(
"If the textcat component is a binary classifier with "
"exclusive classes, provide '--textcat_positive_label' for "
"an evaluation on the positive class."
)
msg.text(
"Textcat evaluation score: F1-score macro-averaged across "
"the labels '{}'".format(", ".join(textcat_labels))
)
else:
msg.fail(
"Unsupported textcat configuration. Use `spacy debug-data` "
"for more information."
)
# fmt: off
row_head, output_stats = _configure_training_output(pipeline, use_gpu, has_beam_widths)
row_widths = [len(w) for w in row_head]
row_head = ["Itn", "Dep Loss", "NER Loss", "UAS", "NER P", "NER R", "NER F", "Tag %", "Token %", "CPU WPS", "GPU WPS"]
row_widths = [3, 10, 10, 7, 7, 7, 7, 7, 7, 7, 7]
if has_beam_widths:
row_head.insert(1, "Beam W.")
row_widths.insert(1, 7)
row_settings = {"widths": row_widths, "aligns": tuple(["r" for i in row_head]), "spacing": 2}
# fmt: on
print("")
@ -339,11 +243,7 @@ def train(
best_score = 0.0
for i in range(n_iter):
train_docs = corpus.train_docs(
nlp,
noise_level=noise_level,
orth_variant_level=orth_variant_level,
gold_preproc=gold_preproc,
max_length=0,
nlp, noise_level=noise_level, gold_preproc=gold_preproc, max_length=0
)
if raw_text:
random.shuffle(raw_text)
@ -386,7 +286,7 @@ def train(
)
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, verbose=verbose)
scorer = nlp_loaded.evaluate(dev_docs, debug)
end_time = timer()
if use_gpu < 0:
gpu_wps = None
@ -402,7 +302,7 @@ def train(
corpus.dev_docs(nlp_loaded, gold_preproc=gold_preproc)
)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, verbose=verbose)
scorer = nlp_loaded.evaluate(dev_docs)
end_time = timer()
cpu_wps = nwords / (end_time - start_time)
acc_loc = output_path / ("model%d" % i) / "accuracy.json"
@ -436,7 +336,6 @@ def train(
}
meta.setdefault("name", "model%d" % i)
meta.setdefault("version", version)
meta["labels"] = nlp.meta["labels"]
meta_loc = output_path / ("model%d" % i) / "meta.json"
srsly.write_json(meta_loc, meta)
util.set_env_log(verbose)
@ -445,19 +344,10 @@ def train(
i,
losses,
scorer.scores,
output_stats,
beam_width=beam_width if has_beam_widths else None,
cpu_wps=cpu_wps,
gpu_wps=gpu_wps,
)
if i == 0 and "textcat" in pipeline:
textcats_per_cat = scorer.scores.get("textcats_per_cat", {})
for cat, cat_score in textcats_per_cat.items():
if cat_score.get("roc_auc_score", 0) < 0:
msg.warn(
"Textcat ROC AUC score is undefined due to "
"only one value in label '{}'.".format(cat)
)
msg.row(progress, **row_settings)
# Early stopping
if n_early_stopping is not None:
@ -498,8 +388,6 @@ def _score_for_model(meta):
mean_acc.append((acc["uas"] + acc["las"]) / 2)
if "ner" in pipes:
mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]) / 3)
if "textcat" in pipes:
mean_acc.append(acc["textcat_score"])
return sum(mean_acc) / len(mean_acc)
@ -583,55 +471,40 @@ def _get_metrics(component):
return ("token_acc",)
def _configure_training_output(pipeline, use_gpu, has_beam_widths):
row_head = ["Itn"]
output_stats = []
for pipe in pipeline:
if pipe == "tagger":
row_head.extend(["Tag Loss ", " Tag % "])
output_stats.extend(["tag_loss", "tags_acc"])
elif pipe == "parser":
row_head.extend(["Dep Loss ", " UAS ", " LAS "])
output_stats.extend(["dep_loss", "uas", "las"])
elif pipe == "ner":
row_head.extend(["NER Loss ", "NER P ", "NER R ", "NER F "])
output_stats.extend(["ner_loss", "ents_p", "ents_r", "ents_f"])
elif pipe == "textcat":
row_head.extend(["Textcat Loss", "Textcat"])
output_stats.extend(["textcat_loss", "textcat_score"])
row_head.extend(["Token %", "CPU WPS"])
output_stats.extend(["token_acc", "cpu_wps"])
if use_gpu >= 0:
row_head.extend(["GPU WPS"])
output_stats.extend(["gpu_wps"])
if has_beam_widths:
row_head.insert(1, "Beam W.")
return row_head, output_stats
def _get_progress(
itn, losses, dev_scores, output_stats, beam_width=None, cpu_wps=0.0, gpu_wps=0.0
):
def _get_progress(itn, losses, dev_scores, beam_width=None, cpu_wps=0.0, gpu_wps=0.0):
scores = {}
for stat in output_stats:
scores[stat] = 0.0
for col in [
"dep_loss",
"tag_loss",
"uas",
"tags_acc",
"token_acc",
"ents_p",
"ents_r",
"ents_f",
"cpu_wps",
"gpu_wps",
]:
scores[col] = 0.0
scores["dep_loss"] = losses.get("parser", 0.0)
scores["ner_loss"] = losses.get("ner", 0.0)
scores["tag_loss"] = losses.get("tagger", 0.0)
scores["textcat_loss"] = losses.get("textcat", 0.0)
scores.update(dev_scores)
scores["cpu_wps"] = cpu_wps
scores["gpu_wps"] = gpu_wps or 0.0
scores.update(dev_scores)
formatted_scores = []
for stat in output_stats:
format_spec = "{:.3f}"
if stat.endswith("_wps"):
format_spec = "{:.0f}"
formatted_scores.append(format_spec.format(scores[stat]))
result = [itn + 1]
result.extend(formatted_scores)
result = [
itn,
"{:.3f}".format(scores["dep_loss"]),
"{:.3f}".format(scores["ner_loss"]),
"{:.3f}".format(scores["uas"]),
"{:.3f}".format(scores["ents_p"]),
"{:.3f}".format(scores["ents_r"]),
"{:.3f}".format(scores["ents_f"]),
"{:.3f}".format(scores["tags_acc"]),
"{:.3f}".format(scores["token_acc"]),
"{:.0f}".format(scores["cpu_wps"]),
"{:.0f}".format(scores["gpu_wps"]),
]
if beam_width is not None:
result.insert(1, beam_width)
return result

25
spacy/errors.py
View File

@ -84,10 +84,6 @@ class Warnings(object):
W018 = ("Entity '{entity}' already exists in the Knowledge base.")
W019 = ("Changing vectors name from {old} to {new}, to avoid clash with "
"previously loaded vectors. See Issue #3853.")
W020 = ("Unnamed vectors. This won't allow multiple vectors models to be "
"loaded. (Shape: {shape})")
W021 = ("Unexpected hash collision in PhraseMatcher. Matches may be "
"incorrect. Modify PhraseMatcher._terminal_hash to fix.")
@add_codes
@ -122,7 +118,7 @@ class Errors(object):
E011 = ("Unknown operator: '{op}'. Options: {opts}")
E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
E013 = ("Error selecting action in matcher")
E014 = ("Unknown tag ID: {tag}")
E014 = ("Uknown tag ID: {tag}")
E015 = ("Conflicting morphology exception for ({tag}, {orth}). Use "
"`force=True` to overwrite.")
E016 = ("MultitaskObjective target should be function or one of: dep, "
@ -461,25 +457,6 @@ class Errors(object):
E160 = ("Can't find language data file: {path}")
E161 = ("Found an internal inconsistency when predicting entity links. "
"This is likely a bug in spaCy, so feel free to open an issue.")
E162 = ("Cannot evaluate textcat model on data with different labels.\n"
"Labels in model: {model_labels}\nLabels in evaluation "
"data: {eval_labels}")
E163 = ("cumsum was found to be unstable: its last element does not "
"correspond to sum")
E164 = ("x is neither increasing nor decreasing: {}.")
E165 = ("Only one class present in y_true. ROC AUC score is not defined in "
"that case.")
E166 = ("Can only merge DocBins with the same pre-defined attributes.\n"
"Current DocBin: {current}\nOther DocBin: {other}")
E167 = ("Unknown morphological feature: '{feat}' ({feat_id}). This can "
"happen if the tagger was trained with a different set of "
"morphological features. If you're using a pre-trained model, make "
"sure that your models are up to date:\npython -m spacy validate")
E168 = ("Unknown field: {field}")
E169 = ("Can't find module: {module}")
E170 = ("Cannot apply transition {name}: invalid for the current state.")
E171 = ("Matcher.add received invalid on_match callback argument: expected "
"callable or None, but got: {arg_type}")
@add_codes

6
spacy/glossary.py
View File

@ -307,10 +307,4 @@ GLOSSARY = {
# https://pdfs.semanticscholar.org/5744/578cc243d92287f47448870bb426c66cc941.pdf
"PER": "Named person or family.",
"MISC": "Miscellaneous entities, e.g. events, nationalities, products or works of art",
# https://github.com/ltgoslo/norne
"EVT": "Festivals, cultural events, sports events, weather phenomena, wars, etc.",
"PROD": "Product, i.e. artificially produced entities including speeches, radio shows, programming languages, contracts, laws and ideas",
"DRV": "Words (and phrases?) that are dervied from a name, but not a name in themselves, e.g. 'Oslo-mannen' ('the man from Oslo')",
"GPE_LOC": "Geo-political entity, with a locative sense, e.g. 'John lives in Spain'",
"GPE_ORG": "Geo-political entity, with an organisation sense, e.g. 'Spain declined to meet with Belgium'",
}

1
spacy/gold.pxd
View File

@ -24,7 +24,6 @@ cdef class GoldParse:
cdef public int loss
cdef public list words
cdef public list tags
cdef public list morphology
cdef public list heads
cdef public list labels
cdef public dict orths

168
spacy/gold.pyx
View File

@ -7,7 +7,6 @@ import random
import numpy
import tempfile
import shutil
import itertools
from pathlib import Path
import srsly
@ -57,7 +56,6 @@ def tags_to_entities(tags):
def merge_sents(sents):
m_deps = [[], [], [], [], [], []]
m_brackets = []
m_cats = sents.pop()
i = 0
for (ids, words, tags, heads, labels, ner), brackets in sents:
m_deps[0].extend(id_ + i for id_ in ids)
@ -69,7 +67,6 @@ def merge_sents(sents):
m_brackets.extend((b["first"] + i, b["last"] + i, b["label"])
for b in brackets)
i += len(ids)
m_deps.append(m_cats)
return [(m_deps, m_brackets)]
@ -201,7 +198,6 @@ class GoldCorpus(object):
n = 0
i = 0
for raw_text, paragraph_tuples in self.train_tuples:
cats = paragraph_tuples.pop()
for sent_tuples, brackets in paragraph_tuples:
n += len(sent_tuples[1])
if self.limit and i >= self.limit:
@ -210,14 +206,13 @@ class GoldCorpus(object):
return n
def train_docs(self, nlp, gold_preproc=False, max_length=None,
noise_level=0.0, orth_variant_level=0.0):
noise_level=0.0):
locs = list((self.tmp_dir / 'train').iterdir())
random.shuffle(locs)
train_tuples = self.read_tuples(locs, limit=self.limit)
gold_docs = self.iter_gold_docs(nlp, train_tuples, gold_preproc,
max_length=max_length,
noise_level=noise_level,
orth_variant_level=orth_variant_level,
make_projective=True)
yield from gold_docs
@ -231,132 +226,43 @@ class GoldCorpus(object):
@classmethod
def iter_gold_docs(cls, nlp, tuples, gold_preproc, max_length=None,
noise_level=0.0, orth_variant_level=0.0, make_projective=False):
noise_level=0.0, make_projective=False):
for raw_text, paragraph_tuples in tuples:
if gold_preproc:
raw_text = None
else:
paragraph_tuples = merge_sents(paragraph_tuples)
docs, paragraph_tuples = cls._make_docs(nlp, raw_text,
paragraph_tuples, gold_preproc, noise_level=noise_level,
orth_variant_level=orth_variant_level)
docs = cls._make_docs(nlp, raw_text, paragraph_tuples, gold_preproc,
noise_level=noise_level)
golds = cls._make_golds(docs, paragraph_tuples, make_projective)
for doc, gold in zip(docs, golds):
if (not max_length) or len(doc) < max_length:
yield doc, gold
@classmethod
def _make_docs(cls, nlp, raw_text, paragraph_tuples, gold_preproc, noise_level=0.0, orth_variant_level=0.0):
def _make_docs(cls, nlp, raw_text, paragraph_tuples, gold_preproc, noise_level=0.0):
if raw_text is not None:
raw_text, paragraph_tuples = make_orth_variants(nlp, raw_text, paragraph_tuples, orth_variant_level=orth_variant_level)
raw_text = add_noise(raw_text, noise_level)
return [nlp.make_doc(raw_text)], paragraph_tuples
return [nlp.make_doc(raw_text)]
else:
docs = []
raw_text, paragraph_tuples = make_orth_variants(nlp, None, paragraph_tuples, orth_variant_level=orth_variant_level)
return [Doc(nlp.vocab, words=add_noise(sent_tuples[1], noise_level))
for (sent_tuples, brackets) in paragraph_tuples], paragraph_tuples
for (sent_tuples, brackets) in paragraph_tuples]
@classmethod
def _make_golds(cls, docs, paragraph_tuples, make_projective):
if len(docs) != len(paragraph_tuples):
n_annots = len(paragraph_tuples)
raise ValueError(Errors.E070.format(n_docs=len(docs), n_annots=n_annots))
return [GoldParse.from_annot_tuples(doc, sent_tuples,
if len(docs) == 1:
return [GoldParse.from_annot_tuples(docs[0], paragraph_tuples[0][0],
make_projective=make_projective)]
else:
return [GoldParse.from_annot_tuples(doc, sent_tuples,
make_projective=make_projective)
for doc, (sent_tuples, brackets)
in zip(docs, paragraph_tuples)]
def make_orth_variants(nlp, raw, paragraph_tuples, orth_variant_level=0.0):
if random.random() >= orth_variant_level:
return raw, paragraph_tuples
if random.random() >= 0.5:
lower = True
if raw is not None:
raw = raw.lower()
ndsv = nlp.Defaults.single_orth_variants
ndpv = nlp.Defaults.paired_orth_variants
# modify words in paragraph_tuples
variant_paragraph_tuples = []
for sent_tuples, brackets in paragraph_tuples:
ids, words, tags, heads, labels, ner, cats = sent_tuples
if lower:
words = [w.lower() for w in words]
# single variants
punct_choices = [random.choice(x["variants"]) for x in ndsv]
for word_idx in range(len(words)):
for punct_idx in range(len(ndsv)):
if tags[word_idx] in ndsv[punct_idx]["tags"] \
and words[word_idx] in ndsv[punct_idx]["variants"]:
words[word_idx] = punct_choices[punct_idx]
# paired variants
punct_choices = [random.choice(x["variants"]) for x in ndpv]
for word_idx in range(len(words)):
for punct_idx in range(len(ndpv)):
if tags[word_idx] in ndpv[punct_idx]["tags"] \
and words[word_idx] in itertools.chain.from_iterable(ndpv[punct_idx]["variants"]):
# backup option: random left vs. right from pair
pair_idx = random.choice([0, 1])
# best option: rely on paired POS tags like `` / ''
if len(ndpv[punct_idx]["tags"]) == 2:
pair_idx = ndpv[punct_idx]["tags"].index(tags[word_idx])
# next best option: rely on position in variants
# (may not be unambiguous, so order of variants matters)
else:
for pair in ndpv[punct_idx]["variants"]:
if words[word_idx] in pair:
pair_idx = pair.index(words[word_idx])
words[word_idx] = punct_choices[punct_idx][pair_idx]
variant_paragraph_tuples.append(((ids, words, tags, heads, labels, ner, cats), brackets))
# modify raw to match variant_paragraph_tuples
if raw is not None:
variants = []
for single_variants in ndsv:
variants.extend(single_variants["variants"])
for paired_variants in ndpv:
variants.extend(list(itertools.chain.from_iterable(paired_variants["variants"])))
# store variants in reverse length order to be able to prioritize
# longer matches (e.g., "---" before "--")
variants = sorted(variants, key=lambda x: len(x))
variants.reverse()
variant_raw = ""
raw_idx = 0
# add initial whitespace
while raw_idx < len(raw) and re.match("\s", raw[raw_idx]):
variant_raw += raw[raw_idx]
raw_idx += 1
for sent_tuples, brackets in variant_paragraph_tuples:
ids, words, tags, heads, labels, ner, cats = sent_tuples
for word in words:
match_found = False
# add identical word
if word not in variants and raw[raw_idx:].startswith(word):
variant_raw += word
raw_idx += len(word)
match_found = True
# add variant word
else:
for variant in variants:
if not match_found and \
raw[raw_idx:].startswith(variant):
raw_idx += len(variant)
variant_raw += word
match_found = True
# something went wrong, abort
# (add a warning message?)
if not match_found:
return raw, paragraph_tuples
# add following whitespace
while raw_idx < len(raw) and re.match("\s", raw[raw_idx]):
variant_raw += raw[raw_idx]
raw_idx += 1
return variant_raw, variant_paragraph_tuples
return raw, variant_paragraph_tuples
def add_noise(orig, noise_level):
if random.random() >= noise_level:
return orig
@ -371,8 +277,12 @@ def add_noise(orig, noise_level):
def _corrupt(c, noise_level):
if random.random() >= noise_level:
return c
elif c in [".", "'", "!", "?", ","]:
elif c == " ":
return "\n"
elif c == "\n":
return " "
elif c in [".", "'", "!", "?", ","]:
return ""
else:
return c.lower()
@ -420,10 +330,6 @@ def json_to_tuple(doc):
sents.append([
[ids, words, tags, heads, labels, ner],
sent.get("brackets", [])])
cats = {}
for cat in paragraph.get("cats", {}):
cats[cat["label"]] = cat["value"]
sents.append(cats)
if sents:
yield [paragraph.get("raw", None), sents]
@ -537,12 +443,11 @@ cdef class GoldParse:
"""
@classmethod
def from_annot_tuples(cls, doc, annot_tuples, make_projective=False):
_, words, tags, heads, deps, entities, cats = annot_tuples
_, words, tags, heads, deps, entities = annot_tuples
return cls(doc, words=words, tags=tags, heads=heads, deps=deps,
entities=entities, cats=cats,
make_projective=make_projective)
entities=entities, make_projective=make_projective)
def __init__(self, doc, annot_tuples=None, words=None, tags=None, morphology=None,
def __init__(self, doc, annot_tuples=None, words=None, tags=None,
heads=None, deps=None, entities=None, make_projective=False,
cats=None, links=None, **_):
"""Create a GoldParse.
@ -577,13 +482,11 @@ cdef class GoldParse:
if words is None:
words = [token.text for token in doc]
if tags is None:
tags = [None for _ in words]
tags = [None for _ in doc]
if heads is None:
heads = [None for _ in words]
heads = [None for token in doc]
if deps is None:
deps = [None for _ in words]
if morphology is None:
morphology = [None for _ in words]
deps = [None for _ in doc]
if entities is None:
entities = ["-" for _ in doc]
elif len(entities) == 0:
@ -595,6 +498,7 @@ cdef class GoldParse:
if not isinstance(entities[0], basestring):
# Assume we have entities specified by character offset.
entities = biluo_tags_from_offsets(doc, entities)
self.mem = Pool()
self.loss = 0
self.length = len(doc)
@ -614,7 +518,6 @@ cdef class GoldParse:
self.heads = [None] * len(doc)
self.labels = [None] * len(doc)
self.ner = [None] * len(doc)
self.morphology = [None] * len(doc)
# This needs to be done before we align the words
if make_projective and heads is not None and deps is not None:
@ -641,13 +544,11 @@ cdef class GoldParse:
self.tags[i] = "_SP"
self.heads[i] = None
self.labels[i] = None
self.ner[i] = None
self.morphology[i] = set()
self.ner[i] = "O"
if gold_i is None:
if i in i2j_multi:
self.words[i] = words[i2j_multi[i]]
self.tags[i] = tags[i2j_multi[i]]
self.morphology[i] = morphology[i2j_multi[i]]
is_last = i2j_multi[i] != i2j_multi.get(i+1)
is_first = i2j_multi[i] != i2j_multi.get(i-1)
# Set next word in multi-token span as head, until last
@ -684,7 +585,6 @@ cdef class GoldParse:
else:
self.words[i] = words[gold_i]
self.tags[i] = tags[gold_i]
self.morphology[i] = morphology[gold_i]
if heads[gold_i] is None:
self.heads[i] = None
else:
@ -692,20 +592,9 @@ cdef class GoldParse:
self.labels[i] = deps[gold_i]
self.ner[i] = entities[gold_i]
# Prevent whitespace that isn't within entities from being tagged as
# an entity.
for i in range(len(self.ner)):
if self.tags[i] == "_SP":
prev_ner = self.ner[i-1] if i >= 1 else None
next_ner = self.ner[i+1] if (i+1) < len(self.ner) else None
if prev_ner == "O" or next_ner == "O":
self.ner[i] = "O"
cycle = nonproj.contains_cycle(self.heads)
if cycle is not None:
raise ValueError(Errors.E069.format(cycle=cycle,
cycle_tokens=" ".join(["'{}'".format(self.words[tok_id]) for tok_id in cycle]),
doc_tokens=" ".join(words[:50])))
raise ValueError(Errors.E069.format(cycle=cycle, cycle_tokens=" ".join(["'{}'".format(self.words[tok_id]) for tok_id in cycle]), doc_tokens=" ".join(words[:50])))
def __len__(self):
"""Get the number of gold-standard tokens.
@ -749,10 +638,7 @@ def docs_to_json(docs, id=0):
docs = [docs]
json_doc = {"id": id, "paragraphs": []}
for i, doc in enumerate(docs):
json_para = {'raw': doc.text, "sentences": [], "cats": []}
for cat, val in doc.cats.items():
json_cat = {"label": cat, "value": val}
json_para["cats"].append(json_cat)
json_para = {'raw': doc.text, "sentences": []}
ent_offsets = [(e.start_char, e.end_char, e.label_) for e in doc.ents]
biluo_tags = biluo_tags_from_offsets(doc, ent_offsets)
for j, sent in enumerate(doc.sents):

2
spacy/kb.pyx
View File

@ -24,7 +24,7 @@ cdef class Candidate:
algorithm which will disambiguate the various candidates to the correct one.
Each candidate (alias, entity) pair is assigned to a certain prior probability.
DOCS: https://spacy.io/api/kb/#candidate_init
DOCS: https://spacy.io/api/candidate
"""
def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):

4
spacy/lang/char_classes.py
View File

@ -201,9 +201,7 @@ _ukrainian = r"а-щюяіїєґА-ЩЮЯІЇЄҐ"
_upper = LATIN_UPPER + _russian_upper + _tatar_upper + _greek_upper + _ukrainian_upper
_lower = LATIN_LOWER + _russian_lower + _tatar_lower + _greek_lower + _ukrainian_lower
_uncased = (
_bengali + _hebrew + _persian + _sinhala + _hindi + _kannada + _tamil + _telugu
)
_uncased = _bengali + _hebrew + _persian + _sinhala + _hindi + _kannada + _tamil + _telugu
ALPHA = group_chars(LATIN + _russian + _tatar + _greek + _ukrainian + _uncased)
ALPHA_LOWER = group_chars(_lower + _uncased)

14
spacy/lang/de/__init__.py
View File

@ -27,20 +27,6 @@ class GermanDefaults(Language.Defaults):
stop_words = STOP_WORDS
syntax_iterators = SYNTAX_ITERATORS
resources = {"lemma_lookup": "lemma_lookup.json"}
single_orth_variants = [
{"tags": ["$("], "variants": ["…", "..."]},
{"tags": ["$("], "variants": ["-", "—", "–", "--", "---", "——"]},
]
paired_orth_variants = [
{
"tags": ["$("],
"variants": [("'", "'"), (",", "'"), ("‚", "‘"), ("›", "‹"), ("‹", "›")],
},
{
"tags": ["$("],
"variants": [("``", "''"), ('"', '"'), ("„", "“"), ("»", "«"), ("«", "»")],
},
]
class German(Language):

6
spacy/lang/de/tag_map.py
View File

@ -10,7 +10,7 @@ TAG_MAP = {
"$,": {POS: PUNCT, "PunctType": "comm"},
"$.": {POS: PUNCT, "PunctType": "peri"},
"ADJA": {POS: ADJ},
"ADJD": {POS: ADJ},
"ADJD": {POS: ADJ, "Variant": "short"},
"ADV": {POS: ADV},
"APPO": {POS: ADP, "AdpType": "post"},
"APPR": {POS: ADP, "AdpType": "prep"},
@ -32,7 +32,7 @@ TAG_MAP = {
"PDAT": {POS: DET, "PronType": "dem"},
"PDS": {POS: PRON, "PronType": "dem"},
"PIAT": {POS: DET, "PronType": "ind|neg|tot"},
"PIDAT": {POS: DET, "PronType": "ind|neg|tot"},
"PIDAT": {POS: DET, "AdjType": "pdt", "PronType": "ind|neg|tot"},
"PIS": {POS: PRON, "PronType": "ind|neg|tot"},
"PPER": {POS: PRON, "PronType": "prs"},
"PPOSAT": {POS: DET, "Poss": "yes", "PronType": "prs"},
@ -42,7 +42,7 @@ TAG_MAP = {
"PRF": {POS: PRON, "PronType": "prs", "Reflex": "yes"},
"PTKA": {POS: PART},
"PTKANT": {POS: PART, "PartType": "res"},
"PTKNEG": {POS: PART, "Polarity": "neg"},
"PTKNEG": {POS: PART, "Polarity": "Neg"},
"PTKVZ": {POS: PART, "PartType": "vbp"},
"PTKZU": {POS: PART, "PartType": "inf"},
"PWAT": {POS: DET, "PronType": "int"},

7
spacy/lang/el/lemmatizer/__init__.py
View File

@ -46,10 +46,9 @@ class GreekLemmatizer(object):
)
return lemmas
def lookup(self, string, orth=None):
key = orth if orth is not None else string
if key in self.lookup_table:
return self.lookup_table[key]
def lookup(self, string):
if string in self.lookup_table:
return self.lookup_table[string]
return string

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

@ -38,14 +38,6 @@ class EnglishDefaults(Language.Defaults):
"lemma_index": "lemmatizer/lemma_index.json",
"lemma_exc": "lemmatizer/lemma_exc.json",
}
single_orth_variants = [
{"tags": ["NFP"], "variants": ["…", "..."]},
{"tags": [":"], "variants": ["-", "—", "–", "--", "---", "——"]},
]
paired_orth_variants = [
{"tags": ["``", "''"], "variants": [("'", "'"), ("‘", "’")]},
{"tags": ["``", "''"], "variants": [('"', '"'), ("“", "”")]},
]
class English(Language):

2
spacy/lang/en/lemmatizer/lemma_lookup.json
View File

@ -20574,7 +20574,7 @@
"lengthier": "lengthy",
"lengthiest": "lengthy",
"lengths": "length",
"lenses": "lens",
"lenses": "lense",
"lent": "lend",
"lenticels": "lenticel",
"lentils": "lentil",

53
spacy/lang/en/morph_rules.py
View File

@ -3,59 +3,55 @@ from __future__ import unicode_literals
from ...symbols import LEMMA, PRON_LEMMA
# Several entries here look pretty suspicious. These will get the POS SCONJ
# given the tag IN, when an adpositional reading seems much more likely for
# a lot of these prepositions. I'm not sure what I was running in 04395ffa4
# when I did this? It doesn't seem right.
_subordinating_conjunctions = [
"that",
"if",
"as",
"because",
# "of",
# "for",
# "before",
# "in",
"of",
"for",
"before",
"in",
"while",
# "after",
"after",
"since",
"like",
# "with",
"with",
"so",
# "to",
# "by",
# "on",
# "about",
"to",
"by",
"on",
"about",
"than",
"whether",
"although",
# "from",
"from",
"though",
# "until",
"until",
"unless",
"once",
# "without",
# "at",
# "into",
"without",
"at",
"into",
"cause",
# "over",
"over",
"upon",
"till",
"whereas",
# "beyond",
"beyond",
"whilst",
"except",
"despite",
"wether",
# "then",
"then",
"but",
"becuse",
"whie",
# "below",
# "against",
"below",
"against",
"it",
"w/out",
# "toward",
"toward",
"albeit",
"save",
"besides",
@ -67,17 +63,16 @@ _subordinating_conjunctions = [
"out",
"near",
"seince",
# "towards",
"towards",
"tho",
"sice",
"will",
]
# This seems kind of wrong too?
# _relative_pronouns = ["this", "that", "those", "these"]
_relative_pronouns = ["this", "that", "those", "these"]
MORPH_RULES = {
# "DT": {word: {"POS": "PRON"} for word in _relative_pronouns},
"DT": {word: {"POS": "PRON"} for word in _relative_pronouns},
"IN": {word: {"POS": "SCONJ"} for word in _subordinating_conjunctions},
"NN": {
"something": {"POS": "PRON"},

20
spacy/lang/en/tag_map.py
View File

@ -14,10 +14,10 @@ TAG_MAP = {
'""': {POS: PUNCT, "PunctType": "quot", "PunctSide": "fin"},
"''": {POS: PUNCT, "PunctType": "quot", "PunctSide": "fin"},
":": {POS: PUNCT},
"$": {POS: SYM},
"#": {POS: SYM},
"AFX": {POS: ADJ, "Hyph": "yes"},
"CC": {POS: CCONJ, "ConjType": "comp"},
"$": {POS: SYM, "Other": {"SymType": "currency"}},
"#": {POS: SYM, "Other": {"SymType": "numbersign"}},
"AFX": {POS: X, "Hyph": "yes"},
"CC": {POS: CCONJ, "ConjType": "coor"},
"CD": {POS: NUM, "NumType": "card"},
"DT": {POS: DET},
"EX": {POS: PRON, "AdvType": "ex"},
@ -34,7 +34,7 @@ TAG_MAP = {
"NNP": {POS: PROPN, "NounType": "prop", "Number": "sing"},
"NNPS": {POS: PROPN, "NounType": "prop", "Number": "plur"},
"NNS": {POS: NOUN, "Number": "plur"},
"PDT": {POS: DET},
"PDT": {POS: DET, "AdjType": "pdt", "PronType": "prn"},
"POS": {POS: PART, "Poss": "yes"},
"PRP": {POS: PRON, "PronType": "prs"},
"PRP$": {POS: PRON, "PronType": "prs", "Poss": "yes"},
@ -56,12 +56,12 @@ TAG_MAP = {
"VerbForm": "fin",
"Tense": "pres",
"Number": "sing",
"Person": "three",
"Person": 3,
},
"WDT": {POS: PRON},
"WP": {POS: PRON},
"WP$": {POS: PRON, "Poss": "yes"},
"WRB": {POS: ADV},
"WDT": {POS: PRON, "PronType": "int|rel"},
"WP": {POS: PRON, "PronType": "int|rel"},
"WP$": {POS: PRON, "Poss": "yes", "PronType": "int|rel"},
"WRB": {POS: ADV, "PronType": "int|rel"},
"ADD": {POS: X},
"NFP": {POS: PUNCT},
"GW": {POS: X},

9
spacy/lang/en/tokenizer_exceptions.py
View File

@ -30,7 +30,14 @@ for pron in ["i"]:
for orth in [pron, pron.title()]:
_exc[orth + "'m"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, NORM: pron, TAG: "PRP"},
{ORTH: "'m", LEMMA: "be", NORM: "am", TAG: "VBP"},
{
ORTH: "'m",
LEMMA: "be",
NORM: "am",
TAG: "VBP",
"tenspect": 1,
"number": 1,
},
]
_exc[orth + "m"] = [

6
spacy/lang/fr/lemmatizer/__init__.py
View File

@ -114,9 +114,9 @@ class FrenchLemmatizer(object):
def punct(self, string, morphology=None):
return self(string, "punct", morphology)
def lookup(self, string, orth=None):
if orth is not None and orth in self.lookup_table:
return self.lookup_table[orth][0]
def lookup(self, string):
if string in self.lookup_table:
return self.lookup_table[string][0]
return string

8
spacy/lang/hi/stop_words.py
View File

@ -2,8 +2,7 @@
from __future__ import unicode_literals
# Source: https://github.com/taranjeet/hindi-tokenizer/blob/master/stopwords.txt, https://data.mendeley.com/datasets/bsr3frvvjc/1#file-a21d5092-99d7-45d8-b044-3ae9edd391c6
# Source: https://github.com/taranjeet/hindi-tokenizer/blob/master/stopwords.txt
STOP_WORDS = set(
"""
अंदर
@ -19,7 +18,6 @@ STOP_WORDS = set(
अंदर
आदि
आप
अगर
इंहिं
इंहें
इंहों
@ -173,9 +171,6 @@ STOP_WORDS = set(
मानो
मे
में
मैं
मुझको
मेरा
यदि
यह
यहाँ
@ -232,7 +227,6 @@ STOP_WORDS = set(
है
हैं
हो
हूँ
होता
होति
होती

23
spacy/lang/ja/__init__.py
View File

@ -37,11 +37,6 @@ def resolve_pos(token):
in the sentence. This function adds information to the POS tag to
resolve ambiguous mappings.
"""
# this is only used for consecutive ascii spaces
if token.pos == "空白":
return "空白"
# TODO: This is a first take. The rules here are crude approximations.
# For many of these, full dependencies are needed to properly resolve
# PoS mappings.
@ -59,7 +54,6 @@ def detailed_tokens(tokenizer, text):
node = tokenizer.parseToNode(text)
node = node.next # first node is beginning of sentence and empty, skip it
words = []
spaces = []
while node.posid != 0:
surface = node.surface
base = surface # a default value. Updated if available later.
@ -70,20 +64,8 @@ def detailed_tokens(tokenizer, text):
# dictionary
base = parts[7]
words.append(ShortUnitWord(surface, base, pos))
# The way MeCab stores spaces is that the rlength of the next token is
# the length of that token plus any preceding whitespace, **in bytes**.
# also note that this is only for half-width / ascii spaces. Full width
# spaces just become tokens.
scount = node.next.rlength - node.next.length
spaces.append(bool(scount))
while scount > 1:
words.append(ShortUnitWord(" ", " ", "空白"))
spaces.append(False)
scount -= 1
node = node.next
return words, spaces
return words
class JapaneseTokenizer(DummyTokenizer):
@ -93,8 +75,9 @@ class JapaneseTokenizer(DummyTokenizer):
self.tokenizer.parseToNode("") # see #2901
def __call__(self, text):
dtokens, spaces = detailed_tokens(self.tokenizer, text)
dtokens = detailed_tokens(self.tokenizer, text)
words = [x.surface for x in dtokens]
spaces = [False] * len(words)
doc = Doc(self.vocab, words=words, spaces=spaces)
mecab_tags = []
for token, dtoken in zip(doc, dtokens):

4
spacy/lang/ja/tag_map.py
View File

@ -2,7 +2,7 @@
from __future__ import unicode_literals
from ...symbols import POS, PUNCT, INTJ, X, ADJ, AUX, ADP, PART, SCONJ, NOUN
from ...symbols import SYM, PRON, VERB, ADV, PROPN, NUM, DET, SPACE
from ...symbols import SYM, PRON, VERB, ADV, PROPN, NUM, DET
TAG_MAP = {
@ -21,8 +21,6 @@ TAG_MAP = {
"感動詞,一般,*,*": {POS: INTJ},
# this is specifically for unicode full-width space
"空白,*,*,*": {POS: X},
# This is used when sequential half-width spaces are present
"空白": {POS: SPACE},
"形状詞,一般,*,*": {POS: ADJ},
"形状詞,タリ,*,*": {POS: ADJ},
"形状詞,助動詞語幹,*,*": {POS: ADJ},

39
spacy/lang/ko/__init__.py
View File

@ -1,6 +1,8 @@
# encoding: utf8
from __future__ import unicode_literals, print_function
import sys
from .stop_words import STOP_WORDS
from .tag_map import TAG_MAP
from ...attrs import LANG
@ -8,12 +10,35 @@ from ...language import Language
from ...tokens import Doc
from ...compat import copy_reg
from ...util import DummyTokenizer
from ...compat import is_python3, is_python_pre_3_5
is_python_post_3_7 = is_python3 and sys.version_info[1] >= 7
# fmt: off
if is_python_pre_3_5:
from collections import namedtuple
Morpheme = namedtuple("Morpheme", "surface lemma tag")
elif is_python_post_3_7:
from dataclasses import dataclass
@dataclass(frozen=True)
class Morpheme:
surface: str
lemma: str
tag: str
else:
from typing import NamedTuple
class Morpheme(NamedTuple):
surface = str("")
lemma = str("")
tag = str("")
def try_mecab_import():
try:
from natto import MeCab
return MeCab
except ImportError:
raise ImportError(
@ -21,8 +46,6 @@ def try_mecab_import():
"[mecab-ko-dic](https://bitbucket.org/eunjeon/mecab-ko-dic), "
"and [natto-py](https://github.com/buruzaemon/natto-py)"
)
# fmt: on
@ -46,13 +69,13 @@ class KoreanTokenizer(DummyTokenizer):
def __call__(self, text):
dtokens = list(self.detailed_tokens(text))
surfaces = [dt["surface"] for dt in dtokens]
surfaces = [dt.surface for dt in dtokens]
doc = Doc(self.vocab, words=surfaces, spaces=list(check_spaces(text, surfaces)))
for token, dtoken in zip(doc, dtokens):
first_tag, sep, eomi_tags = dtoken["tag"].partition("+")
first_tag, sep, eomi_tags = dtoken.tag.partition("+")
token.tag_ = first_tag # stem(어간) or pre-final(선어말 어미)
token.lemma_ = dtoken["lemma"]
doc.user_data["full_tags"] = [dt["tag"] for dt in dtokens]
token.lemma_ = dtoken.lemma
doc.user_data["full_tags"] = [dt.tag for dt in dtokens]
return doc
def detailed_tokens(self, text):
@ -68,7 +91,7 @@ class KoreanTokenizer(DummyTokenizer):
lemma, _, remainder = expr.partition("/")
if lemma == "*":
lemma = surface
yield {"surface": surface, "lemma": lemma, "tag": tag}
yield Morpheme(surface, lemma, tag)
class KoreanDefaults(Language.Defaults):

236
spacy/lang/lt/tag_map.py
View File

@ -1605,7 +1605,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"VerbForm": "Fin",
},
@ -1613,7 +1613,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"VerbForm": "Fin",
},
@ -1621,7 +1621,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1630,7 +1630,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"VerbForm": "Fin",
},
@ -1638,7 +1638,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1647,7 +1647,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"VerbForm": "Fin",
},
@ -1655,7 +1655,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1664,7 +1664,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"VerbForm": "Fin",
},
@ -1672,7 +1672,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1681,7 +1681,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"VerbForm": "Fin",
},
@ -1689,7 +1689,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"VerbForm": "Fin",
},
@ -1697,7 +1697,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1706,7 +1706,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"VerbForm": "Fin",
},
@ -1714,7 +1714,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1723,7 +1723,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"VerbForm": "Fin",
},
@ -1731,7 +1731,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"VerbForm": "Fin",
},
@ -1739,7 +1739,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1748,7 +1748,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Imp",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"VerbForm": "Fin",
},
@ -1756,21 +1756,21 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"VerbForm": "Fin",
},
"Vgm-3---n--ns-": {
POS: VERB,
"Mood": "Cnd",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"VerbForm": "Fin",
},
"Vgm-3---n--ys-": {
POS: VERB,
"Mood": "Cnd",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1778,14 +1778,14 @@ TAG_MAP = {
"Vgm-3---y--ns-": {
POS: VERB,
"Mood": "Cnd",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"VerbForm": "Fin",
},
"Vgm-3---y--ys-": {
POS: VERB,
"Mood": "Cnd",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1794,7 +1794,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"VerbForm": "Fin",
},
@ -1802,7 +1802,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1811,7 +1811,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"VerbForm": "Fin",
},
@ -1819,7 +1819,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"VerbForm": "Fin",
},
@ -1827,7 +1827,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1836,7 +1836,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"VerbForm": "Fin",
},
@ -1844,7 +1844,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Cnd",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Reflex": "Yes",
"VerbForm": "Fin",
@ -1853,7 +1853,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -1862,7 +1862,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -1872,7 +1872,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Tense": "Past",
"VerbForm": "Fin",
@ -1881,7 +1881,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Past",
@ -1891,7 +1891,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -1900,7 +1900,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -1910,7 +1910,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Tense": "Past",
"VerbForm": "Fin",
@ -1919,7 +1919,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Past",
@ -1929,7 +1929,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -1938,7 +1938,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -1948,7 +1948,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"Tense": "Past",
"VerbForm": "Fin",
@ -1957,7 +1957,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -1966,7 +1966,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"Tense": "Past",
"VerbForm": "Fin",
@ -1974,7 +1974,7 @@ TAG_MAP = {
"Vgma3---n--ni-": {
POS: VERB,
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -1982,7 +1982,7 @@ TAG_MAP = {
"Vgma3---n--yi-": {
POS: VERB,
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -1991,7 +1991,7 @@ TAG_MAP = {
"Vgma3---y--ni-": {
POS: VERB,
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Past",
"VerbForm": "Fin",
@ -1999,7 +1999,7 @@ TAG_MAP = {
"Vgma3--y--ni-": {
POS: VERB,
"Case": "Nom",
"Person": "three",
"Person": "3",
"Tense": "Past",
"VerbForm": "Fin",
},
@ -2007,7 +2007,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -2016,7 +2016,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -2026,7 +2026,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Past",
"VerbForm": "Fin",
@ -2035,7 +2035,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Past",
@ -2045,7 +2045,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -2054,7 +2054,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -2064,7 +2064,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -2074,7 +2074,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Past",
"VerbForm": "Fin",
@ -2083,7 +2083,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Past",
@ -2093,7 +2093,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2102,7 +2102,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Fut",
@ -2112,7 +2112,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2121,7 +2121,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2130,7 +2130,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Fut",
@ -2140,7 +2140,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2149,7 +2149,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2158,7 +2158,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Fut",
@ -2168,7 +2168,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2177,7 +2177,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Fut",
@ -2187,7 +2187,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2196,7 +2196,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Fut",
@ -2205,7 +2205,7 @@ TAG_MAP = {
"Vgmf3---n--ni-": {
POS: VERB,
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2213,7 +2213,7 @@ TAG_MAP = {
"Vgmf3---y--ni-": {
POS: VERB,
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2222,7 +2222,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2231,7 +2231,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Fut",
@ -2241,7 +2241,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2250,7 +2250,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2259,7 +2259,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Fut",
@ -2269,7 +2269,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Fut",
"VerbForm": "Fin",
@ -2278,7 +2278,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Fut",
@ -2288,7 +2288,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2297,7 +2297,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Pres",
@ -2307,7 +2307,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2316,7 +2316,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Pres",
@ -2326,7 +2326,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2335,7 +2335,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2344,7 +2344,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Pres",
@ -2354,7 +2354,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2363,7 +2363,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Pres",
@ -2373,7 +2373,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2382,7 +2382,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Pres",
@ -2392,7 +2392,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2401,7 +2401,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Pres",
@ -2411,7 +2411,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2420,7 +2420,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Pres",
@ -2430,7 +2430,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Neg",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2438,7 +2438,7 @@ TAG_MAP = {
"Vgmp3---n--ni-": {
POS: VERB,
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2446,7 +2446,7 @@ TAG_MAP = {
"Vgmp3---n--yi-": {
POS: VERB,
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Pres",
@ -2455,7 +2455,7 @@ TAG_MAP = {
"Vgmp3---y--ni-": {
POS: VERB,
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2463,7 +2463,7 @@ TAG_MAP = {
"Vgmp3---y--yi-": {
POS: VERB,
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Pres",
@ -2473,7 +2473,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2482,7 +2482,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Pres",
@ -2492,7 +2492,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2501,7 +2501,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Pres",
@ -2511,7 +2511,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2520,7 +2520,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2529,7 +2529,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2538,7 +2538,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Pres",
@ -2548,7 +2548,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Pres",
"VerbForm": "Fin",
@ -2557,7 +2557,7 @@ TAG_MAP = {
POS: VERB,
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Reflex": "Yes",
"Tense": "Pres",
@ -2568,7 +2568,7 @@ TAG_MAP = {
"Aspect": "Hab",
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -2578,7 +2578,7 @@ TAG_MAP = {
"Aspect": "Hab",
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -2589,7 +2589,7 @@ TAG_MAP = {
"Aspect": "Hab",
"Mood": "Ind",
"Number": "Sing",
"Person": "one",
"Person": "1",
"Polarity": "Neg",
"Tense": "Past",
"VerbForm": "Fin",
@ -2599,7 +2599,7 @@ TAG_MAP = {
"Aspect": "Hab",
"Mood": "Ind",
"Number": "Sing",
"Person": "two",
"Person": "2",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -2608,7 +2608,7 @@ TAG_MAP = {
POS: VERB,
"Aspect": "Hab",
"Mood": "Ind",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -2618,7 +2618,7 @@ TAG_MAP = {
"Aspect": "Hab",
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -2628,7 +2628,7 @@ TAG_MAP = {
"Aspect": "Hab",
"Mood": "Ind",
"Number": "Plur",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -2639,7 +2639,7 @@ TAG_MAP = {
"Aspect": "Hab",
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",
@ -2649,7 +2649,7 @@ TAG_MAP = {
"Aspect": "Hab",
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Reflex": "Yes",
"Tense": "Past",
@ -2660,7 +2660,7 @@ TAG_MAP = {
"Aspect": "Hab",
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Neg",
"Tense": "Past",
"VerbForm": "Fin",
@ -2670,7 +2670,7 @@ TAG_MAP = {
"Aspect": "Perf",
"Mood": "Ind",
"Number": "Sing",
"Person": "three",
"Person": "3",
"Polarity": "Pos",
"Tense": "Past",
"VerbForm": "Fin",

9
spacy/lang/nl/lemmatizer/__init__.py
View File

@ -73,7 +73,7 @@ class DutchLemmatizer(object):
return [lemma[0]]
except KeyError:
pass
# string corresponds to key in lookup table
# string corresponds to key in lookup table
lookup_table = self.lookup_table
looked_up_lemma = lookup_table.get(string)
if looked_up_lemma and looked_up_lemma in lemma_index:
@ -103,12 +103,9 @@ class DutchLemmatizer(object):
# Overrides parent method so that a lowercased version of the string is
# used to search the lookup table. This is necessary because our lookup
# table consists entirely of lowercase keys.
def lookup(self, string, orth=None):
def lookup(self, string):
string = string.lower()
if orth is not None:
return self.lookup_table.get(orth, string)
else:
return self.lookup_table.get(string, string)
return self.lookup_table.get(string, string)
def noun(self, string, morphology=None):
return self(string, "noun", morphology)

4
spacy/lang/ru/lemmatizer.py
View File

@ -73,7 +73,7 @@ class RussianLemmatizer(Lemmatizer):
if (
feature in morphology
and feature in analysis_morph
and morphology[feature].lower() != analysis_morph[feature].lower()
and morphology[feature] != analysis_morph[feature]
):
break
else:
@ -115,7 +115,7 @@ class RussianLemmatizer(Lemmatizer):
def pron(self, string, morphology=None):
return self(string, "pron", morphology)
def lookup(self, string, orth=None):
def lookup(self, string):
analyses = self._morph.parse(string)
if len(analyses) == 1:
return analyses[0].normal_form

4
spacy/lang/uk/lemmatizer.py
View File

@ -70,7 +70,7 @@ class UkrainianLemmatizer(Lemmatizer):
if (
feature in morphology
and feature in analysis_morph
and morphology[feature].lower() != analysis_morph[feature].lower()
and morphology[feature] != analysis_morph[feature]
):
break
else:
@ -112,7 +112,7 @@ class UkrainianLemmatizer(Lemmatizer):
def pron(self, string, morphology=None):
return self(string, "pron", morphology)
def lookup(self, string, orth=None):
def lookup(self, string):
analyses = self._morph.parse(string)
if len(analyses) == 1:
return analyses[0].normal_form

48
spacy/language.py
View File

@ -20,7 +20,6 @@ from .pipeline import Tensorizer, EntityRecognizer, EntityLinker
from .pipeline import SimilarityHook, TextCategorizer, Sentencizer
from .pipeline import merge_noun_chunks, merge_entities, merge_subtokens
from .pipeline import EntityRuler
from .pipeline import Morphologizer
from .compat import izip, basestring_
from .gold import GoldParse
from .scorer import Scorer
@ -39,8 +38,6 @@ from . import about
class BaseDefaults(object):
@classmethod
def create_lemmatizer(cls, nlp=None, lookups=None):
if lookups is None:
lookups = cls.create_lookups(nlp=nlp)
rules, index, exc, lookup = util.get_lemma_tables(lookups)
return Lemmatizer(index, exc, rules, lookup)
@ -111,8 +108,6 @@ class BaseDefaults(object):
syntax_iterators = {}
resources = {}
writing_system = {"direction": "ltr", "has_case": True, "has_letters": True}
single_orth_variants = []
paired_orth_variants = []
class Language(object):
@ -133,7 +128,6 @@ class Language(object):
"tokenizer": lambda nlp: nlp.Defaults.create_tokenizer(nlp),
"tensorizer": lambda nlp, **cfg: Tensorizer(nlp.vocab, **cfg),
"tagger": lambda nlp, **cfg: Tagger(nlp.vocab, **cfg),
"morphologizer": lambda nlp, **cfg: Morphologizer(nlp.vocab, **cfg),
"parser": lambda nlp, **cfg: DependencyParser(nlp.vocab, **cfg),
"ner": lambda nlp, **cfg: EntityRecognizer(nlp.vocab, **cfg),
"entity_linker": lambda nlp, **cfg: EntityLinker(nlp.vocab, **cfg),
@ -257,8 +251,7 @@ class Language(object):
@property
def pipe_labels(self):
"""Get the labels set by the pipeline components, if available (if
the component exposes a labels property).
"""Get the labels set by the pipeline components, if available.
RETURNS (dict): Labels keyed by component name.
"""
@ -449,25 +442,6 @@ class Language(object):
def make_doc(self, text):
return self.tokenizer(text)
def _format_docs_and_golds(self, docs, golds):
"""Format golds and docs before update models."""
expected_keys = ("words", "tags", "heads", "deps", "entities", "cats", "links")
gold_objs = []
doc_objs = []
for doc, gold in zip(docs, golds):
if isinstance(doc, basestring_):
doc = self.make_doc(doc)
if not isinstance(gold, GoldParse):
unexpected = [k for k in gold if k not in expected_keys]
if unexpected:
err = Errors.E151.format(unexp=unexpected, exp=expected_keys)
raise ValueError(err)
gold = GoldParse(doc, **gold)
doc_objs.append(doc)
gold_objs.append(gold)
return doc_objs, gold_objs
def update(self, docs, golds, drop=0.0, sgd=None, losses=None, component_cfg=None):
"""Update the models in the pipeline.
@ -481,6 +455,7 @@ class Language(object):
DOCS: https://spacy.io/api/language#update
"""
expected_keys = ("words", "tags", "heads", "deps", "entities", "cats", "links")
if len(docs) != len(golds):
raise IndexError(Errors.E009.format(n_docs=len(docs), n_golds=len(golds)))
if len(docs) == 0:
@ -490,7 +465,21 @@ class Language(object):
self._optimizer = create_default_optimizer(Model.ops)
sgd = self._optimizer
# Allow dict of args to GoldParse, instead of GoldParse objects.
docs, golds = self._format_docs_and_golds(docs, golds)
gold_objs = []
doc_objs = []
for doc, gold in zip(docs, golds):
if isinstance(doc, basestring_):
doc = self.make_doc(doc)
if not isinstance(gold, GoldParse):
unexpected = [k for k in gold if k not in expected_keys]
if unexpected:
err = Errors.E151.format(unexp=unexpected, exp=expected_keys)
raise ValueError(err)
gold = GoldParse(doc, **gold)
doc_objs.append(doc)
gold_objs.append(gold)
golds = gold_objs
docs = doc_objs
grads = {}
def get_grads(W, dW, key=None):
@ -594,7 +583,6 @@ class Language(object):
# Populate vocab
else:
for _, annots_brackets in get_gold_tuples():
_ = annots_brackets.pop()
for annots, _ in annots_brackets:
for word in annots[1]:
_ = self.vocab[word] # noqa: F841
@ -663,7 +651,7 @@ class Language(object):
DOCS: https://spacy.io/api/language#evaluate
"""
if scorer is None:
scorer = Scorer(pipeline=self.pipeline)
scorer = Scorer()
if component_cfg is None:
component_cfg = {}
docs, golds = zip(*docs_golds)

36
spacy/lemmatizer.py
View File

@ -2,7 +2,8 @@
from __future__ import unicode_literals
from collections import OrderedDict
from .symbols import NOUN, VERB, ADJ, PUNCT, PROPN
from .symbols import POS, NOUN, VERB, ADJ, PUNCT, PROPN
from .symbols import VerbForm_inf, VerbForm_none, Number_sing, Degree_pos
class Lemmatizer(object):
@ -54,8 +55,12 @@ class Lemmatizer(object):
Check whether we're dealing with an uninflected paradigm, so we can
avoid lemmatization entirely.
"""
if morphology is None:
morphology = {}
morphology = {} if morphology is None else morphology
others = [
key
for key in morphology
if key not in (POS, "Number", "POS", "VerbForm", "Tense")
]
if univ_pos == "noun" and morphology.get("Number") == "sing":
return True
elif univ_pos == "verb" and morphology.get("VerbForm") == "inf":
@ -66,17 +71,18 @@ class Lemmatizer(object):
morphology.get("VerbForm") == "fin"
and morphology.get("Tense") == "pres"
and morphology.get("Number") is None
and not others
):
return True
elif univ_pos == "adj" and morphology.get("Degree") == "pos":
return True
elif morphology.get("VerbForm") == "inf":
elif VerbForm_inf in morphology:
return True
elif morphology.get("VerbForm") == "none":
elif VerbForm_none in morphology:
return True
elif morphology.get("VerbForm") == "inf":
elif Number_sing in morphology:
return True
elif morphology.get("Degree") == "pos":
elif Degree_pos in morphology:
return True
else:
return False
@ -93,19 +99,9 @@ class Lemmatizer(object):
def punct(self, string, morphology=None):
return self(string, "punct", morphology)
def lookup(self, string, orth=None):
"""Look up a lemma in the table, if available. If no lemma is found,
the original string is returned.
string (unicode): The original string.
orth (int): Optional hash of the string to look up. If not set, the
string will be used and hashed.
RETURNS (unicode): The lemma if the string was found, otherwise the
original string.
"""
key = orth if orth is not None else string
if key in self.lookup_table:
return self.lookup_table[key]
def lookup(self, string):
if string in self.lookup_table:
return self.lookup_table[string]
return string

159
spacy/lookups.py
View File

@ -1,13 +1,11 @@
# coding: utf-8
# coding: utf8
from __future__ import unicode_literals
import srsly
from collections import OrderedDict
from preshed.bloom import BloomFilter
from .errors import Errors
from .util import SimpleFrozenDict, ensure_path
from .strings import get_string_id
class Lookups(object):
@ -16,14 +14,16 @@ class Lookups(object):
so they can be accessed before the pipeline components are applied (e.g.
in the tokenizer and lemmatizer), as well as within the pipeline components
via doc.vocab.lookups.
Important note: At the moment, this class only performs a very basic
dictionary lookup. We're planning to replace this with a more efficient
implementation. See #3971 for details.
"""
def __init__(self):
"""Initialize the Lookups object.
RETURNS (Lookups): The newly created object.
DOCS: https://spacy.io/api/lookups#init
"""
self._tables = OrderedDict()
@ -32,7 +32,7 @@ class Lookups(object):
Lookups.has_table.
name (unicode): Name of the table.
RETURNS (bool): Whether a table of that name is in the lookups.
RETURNS (bool): Whether a table of that name exists.
"""
return self.has_table(name)
@ -51,12 +51,11 @@ class Lookups(object):
name (unicode): Unique name of table.
data (dict): Optional data to add to the table.
RETURNS (Table): The newly added table.
DOCS: https://spacy.io/api/lookups#add_table
"""
if name in self.tables:
raise ValueError(Errors.E158.format(name=name))
table = Table(name=name, data=data)
table = Table(name=name)
table.update(data)
self._tables[name] = table
return table
@ -65,8 +64,6 @@ class Lookups(object):
name (unicode): Name of the table.
RETURNS (Table): The table.
DOCS: https://spacy.io/api/lookups#get_table
"""
if name not in self._tables:
raise KeyError(Errors.E159.format(name=name, tables=self.tables))
@ -75,10 +72,8 @@ class Lookups(object):
def remove_table(self, name):
"""Remove a table. Raises an error if the table doesn't exist.
name (unicode): Name of the table to remove.
name (unicode): The name to remove.
RETURNS (Table): The removed table.
DOCS: https://spacy.io/api/lookups#remove_table
"""
if name not in self._tables:
raise KeyError(Errors.E159.format(name=name, tables=self.tables))
@ -89,57 +84,45 @@ class Lookups(object):
name (unicode): Name of the table.
RETURNS (bool): Whether a table of that name exists.
DOCS: https://spacy.io/api/lookups#has_table
"""
return name in self._tables
def to_bytes(self, **kwargs):
def to_bytes(self, exclude=tuple(), **kwargs):
"""Serialize the lookups to a bytestring.
exclude (list): String names of serialization fields to exclude.
RETURNS (bytes): The serialized Lookups.
DOCS: https://spacy.io/api/lookups#to_bytes
"""
return srsly.msgpack_dumps(self._tables)
def from_bytes(self, bytes_data, **kwargs):
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
"""Load the lookups from a bytestring.
bytes_data (bytes): The data to load.
RETURNS (Lookups): The loaded Lookups.
DOCS: https://spacy.io/api/lookups#from_bytes
exclude (list): String names of serialization fields to exclude.
RETURNS (bytes): The loaded Lookups.
"""
for key, value in srsly.msgpack_loads(bytes_data).items():
self._tables[key] = Table(key)
self._tables[key].update(value)
self._tables = OrderedDict()
msg = srsly.msgpack_loads(bytes_data)
for key, value in msg.items():
self._tables[key] = Table.from_dict(value)
return self
def to_disk(self, path, **kwargs):
"""Save the lookups to a directory as lookups.bin. Expects a path to a
directory, which will be created if it doesn't exist.
"""Save the lookups to a directory as lookups.bin.
path (unicode / Path): The file path.
DOCS: https://spacy.io/api/lookups#to_disk
"""
if len(self._tables):
path = ensure_path(path)
if not path.exists():
path.mkdir()
filepath = path / "lookups.bin"
with filepath.open("wb") as file_:
file_.write(self.to_bytes())
def from_disk(self, path, **kwargs):
"""Load lookups from a directory containing a lookups.bin. Will skip
loading if the file doesn't exist.
"""Load lookups from a directory containing a lookups.bin.
path (unicode / Path): The directory path.
path (unicode / Path): The file path.
RETURNS (Lookups): The loaded lookups.
DOCS: https://spacy.io/api/lookups#from_disk
"""
path = ensure_path(path)
filepath = path / "lookups.bin"
@ -153,118 +136,22 @@ class Lookups(object):
class Table(OrderedDict):
"""A table in the lookups. Subclass of builtin dict that implements a
slightly more consistent and unified API.
Includes a Bloom filter to speed up missed lookups.
"""
@classmethod
def from_dict(cls, data, name=None):
"""Initialize a new table from a dict.
data (dict): The dictionary.
name (unicode): Optional table name for reference.
RETURNS (Table): The newly created object.
DOCS: https://spacy.io/api/lookups#table.from_dict
"""
self = cls(name=name)
self.update(data)
return self
def __init__(self, name=None, data=None):
def __init__(self, name=None):
"""Initialize a new table.
name (unicode): Optional table name for reference.
data (dict): Initial data, used to hint Bloom Filter.
RETURNS (Table): The newly created object.
DOCS: https://spacy.io/api/lookups#table.init
"""
OrderedDict.__init__(self)
self.name = name
# Assume a default size of 1M items
self.default_size = 1e6
size = len(data) if data and len(data) > 0 else self.default_size
self.bloom = BloomFilter.from_error_rate(size)
if data:
self.update(data)
def __setitem__(self, key, value):
"""Set new key/value pair. String keys will be hashed.
key (unicode / int): The key to set.
value: The value to set.
"""
key = get_string_id(key)
OrderedDict.__setitem__(self, key, value)
self.bloom.add(key)
def set(self, key, value):
"""Set new key/value pair. String keys will be hashed.
Same as table[key] = value.
key (unicode / int): The key to set.
value: The value to set.
"""
"""Set new key/value pair. Same as table[key] = value."""
self[key] = value
def __getitem__(self, key):
"""Get the value for a given key. String keys will be hashed.
key (unicode / int): The key to get.
RETURNS: The value.
"""
key = get_string_id(key)
return OrderedDict.__getitem__(self, key)
def get(self, key, default=None):
"""Get the value for a given key. String keys will be hashed.
key (unicode / int): The key to get.
default: The default value to return.
RETURNS: The value.
"""
key = get_string_id(key)
return OrderedDict.get(self, key, default)
def __contains__(self, key):
"""Check whether a key is in the table. String keys will be hashed.
key (unicode / int): The key to check.
RETURNS (bool): Whether the key is in the table.
"""
key = get_string_id(key)
# This can give a false positive, so we need to check it after
if key not in self.bloom:
return False
return OrderedDict.__contains__(self, key)
def to_bytes(self):
"""Serialize table to a bytestring.
RETURNS (bytes): The serialized table.
DOCS: https://spacy.io/api/lookups#table.to_bytes
"""
data = [
("name", self.name),
("dict", dict(self.items())),
("bloom", self.bloom.to_bytes()),
]
return srsly.msgpack_dumps(OrderedDict(data))
def from_bytes(self, bytes_data):
"""Load a table from a bytestring.
bytes_data (bytes): The data to load.
RETURNS (Table): The loaded table.
DOCS: https://spacy.io/api/lookups#table.from_bytes
"""
loaded = srsly.msgpack_loads(bytes_data)
data = loaded.get("dict", {})
self.name = loaded["name"]
self.bloom = BloomFilter().from_bytes(loaded["bloom"])
self.clear()
self.update(data)
return self

29
spacy/matcher/matcher.pyx
View File

@ -103,8 +103,6 @@ cdef class Matcher:
*patterns (list): List of token descriptions.
"""
errors = {}
if on_match is not None and not hasattr(on_match, "__call__"):
raise ValueError(Errors.E171.format(arg_type=type(on_match)))
for i, pattern in enumerate(patterns):
if len(pattern) == 0:
raise ValueError(Errors.E012.format(key=key))
@ -164,37 +162,18 @@ cdef class Matcher:
return default
return (self._callbacks[key], self._patterns[key])
def pipe(self, docs, batch_size=1000, n_threads=-1, return_matches=False,
as_tuples=False):
def pipe(self, docs, batch_size=1000, n_threads=-1):
"""Match a stream of documents, yielding them in turn.
docs (iterable): A stream of documents.
batch_size (int): Number of documents to accumulate into a working set.
return_matches (bool): Yield the match lists along with the docs, making
results (doc, matches) tuples.
as_tuples (bool): Interpret the input stream as (doc, context) tuples,
and yield (result, context) tuples out.
If both return_matches and as_tuples are True, the output will
be a sequence of ((doc, matches), context) tuples.
YIELDS (Doc): Documents, in order.
"""
if n_threads != -1:
deprecation_warning(Warnings.W016)
if as_tuples:
for doc, context in docs:
matches = self(doc)
if return_matches:
yield ((doc, matches), context)
else:
yield (doc, context)
else:
for doc in docs:
matches = self(doc)
if return_matches:
yield (doc, matches)
else:
yield doc
for doc in docs:
self(doc)
yield doc
def __call__(self, Doc doc):
"""Find all token sequences matching the supplied pattern.

26
spacy/matcher/phrasematcher.pxd
View File

@ -1,27 +1,5 @@
from libcpp.vector cimport vector
from cymem.cymem cimport Pool
from preshed.maps cimport key_t, MapStruct
from ..typedefs cimport hash_t
from ..attrs cimport attr_id_t
from ..tokens.doc cimport Doc
from ..vocab cimport Vocab
cdef class PhraseMatcher:
cdef Vocab vocab
cdef attr_id_t attr
cdef object _callbacks
cdef object _docs
cdef bint _validate
cdef MapStruct* c_map
cdef Pool mem
cdef key_t _terminal_hash
cdef void find_matches(self, Doc doc, vector[MatchStruct] *matches) nogil
cdef struct MatchStruct:
key_t match_id
int start
int end
ctypedef vector[hash_t] hash_vec

348
spacy/matcher/phrasematcher.pyx
View File

@ -2,16 +2,28 @@
# cython: profile=True
from __future__ import unicode_literals
from libc.stdint cimport uintptr_t
from libcpp.vector cimport vector
from cymem.cymem cimport Pool
from murmurhash.mrmr cimport hash64
from preshed.maps cimport PreshMap
from preshed.maps cimport map_init, map_set, map_get, map_clear, map_iter
from ..attrs cimport ORTH, POS, TAG, DEP, LEMMA
from ..structs cimport TokenC
from ..tokens.token cimport Token
from .matcher cimport Matcher
from ..attrs cimport ORTH, POS, TAG, DEP, LEMMA, attr_id_t
from ..vocab cimport Vocab
from ..tokens.doc cimport Doc, get_token_attr
from ..typedefs cimport attr_t, hash_t
from ._schemas import TOKEN_PATTERN_SCHEMA
from ..errors import Errors, Warnings, deprecation_warning, user_warning
from ..attrs import FLAG61 as U_ENT
from ..attrs import FLAG60 as B2_ENT
from ..attrs import FLAG59 as B3_ENT
from ..attrs import FLAG58 as B4_ENT
from ..attrs import FLAG43 as L2_ENT
from ..attrs import FLAG42 as L3_ENT
from ..attrs import FLAG41 as L4_ENT
from ..attrs import FLAG42 as I3_ENT
from ..attrs import FLAG41 as I4_ENT
cdef class PhraseMatcher:
@ -21,11 +33,18 @@ cdef class PhraseMatcher:
DOCS: https://spacy.io/api/phrasematcher
USAGE: https://spacy.io/usage/rule-based-matching#phrasematcher
Adapted from FlashText: https://github.com/vi3k6i5/flashtext
MIT License (see `LICENSE`)
Copyright (c) 2017 Vikash Singh (vikash.duliajan@gmail.com)
"""
cdef Pool mem
cdef Vocab vocab
cdef Matcher matcher
cdef PreshMap phrase_ids
cdef vector[hash_vec] ent_id_matrix
cdef int max_length
cdef attr_id_t attr
cdef public object _callbacks
cdef public object _patterns
cdef public object _docs
cdef public object _validate
def __init__(self, Vocab vocab, max_length=0, attr="ORTH", validate=False):
"""Initialize the PhraseMatcher.
@ -39,17 +58,11 @@ cdef class PhraseMatcher:
"""
if max_length != 0:
deprecation_warning(Warnings.W010)
self.vocab = vocab
self._callbacks = {}
self._docs = {}
self._validate = validate
self.mem = Pool()
self.c_map = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
self._terminal_hash = 826361138722620965
map_init(self.mem, self.c_map, 8)
if isinstance(attr, (int, long)):
self.max_length = max_length
self.vocab = vocab
self.matcher = Matcher(self.vocab, validate=False)
if isinstance(attr, long):
self.attr = attr
else:
attr = attr.upper()
@ -58,15 +71,28 @@ cdef class PhraseMatcher:
if attr not in TOKEN_PATTERN_SCHEMA["items"]["properties"]:
raise ValueError(Errors.E152.format(attr=attr))
self.attr = self.vocab.strings[attr]
self.phrase_ids = PreshMap()
abstract_patterns = [
[{U_ENT: True}],
[{B2_ENT: True}, {L2_ENT: True}],
[{B3_ENT: True}, {I3_ENT: True}, {L3_ENT: True}],
[{B4_ENT: True}, {I4_ENT: True}, {I4_ENT: True, "OP": "+"}, {L4_ENT: True}],
]
self.matcher.add("Candidate", None, *abstract_patterns)
self._callbacks = {}
self._docs = {}
self._validate = validate
def __len__(self):
"""Get the number of match IDs added to the matcher.
"""Get the number of rules added to the matcher. Note that this only
returns the number of rules (identical with the number of IDs), not the
number of individual patterns.
RETURNS (int): The number of rules.
DOCS: https://spacy.io/api/phrasematcher#len
"""
return len(self._callbacks)
return len(self._docs)
def __contains__(self, key):
"""Check whether the matcher contains rules for a match ID.
@ -76,79 +102,13 @@ cdef class PhraseMatcher:
DOCS: https://spacy.io/api/phrasematcher#contains
"""
return key in self._callbacks
cdef hash_t ent_id = self.matcher._normalize_key(key)
return ent_id in self._callbacks
def __reduce__(self):
data = (self.vocab, self._docs, self._callbacks, self.attr)
data = (self.vocab, self._docs, self._callbacks)
return (unpickle_matcher, data, None, None)
def remove(self, key):
"""Remove a rule from the matcher by match ID. A KeyError is raised if
the key does not exist.
key (unicode): The match ID.
DOCS: https://spacy.io/api/phrasematcher#remove
"""
if key not in self._docs:
raise KeyError(key)
cdef MapStruct* current_node
cdef MapStruct* terminal_map
cdef MapStruct* node_pointer
cdef void* result
cdef key_t terminal_key
cdef void* value
cdef int c_i = 0
cdef vector[MapStruct*] path_nodes
cdef vector[key_t] path_keys
cdef key_t key_to_remove
for keyword in self._docs[key]:
current_node = self.c_map
for token in keyword:
result = map_get(current_node, token)
if result:
path_nodes.push_back(current_node)
path_keys.push_back(token)
current_node = <MapStruct*>result
else:
# if token is not found, break out of the loop
current_node = NULL
break
# remove the tokens from trie node if there are no other
# keywords with them
result = map_get(current_node, self._terminal_hash)
if current_node != NULL and result:
terminal_map = <MapStruct*>result
terminal_keys = []
c_i = 0
while map_iter(terminal_map, &c_i, &terminal_key, &value):
terminal_keys.append(self.vocab.strings[terminal_key])
# if this is the only remaining key, remove unnecessary paths
if terminal_keys == [key]:
while not path_nodes.empty():
node_pointer = path_nodes.back()
path_nodes.pop_back()
key_to_remove = path_keys.back()
path_keys.pop_back()
result = map_get(node_pointer, key_to_remove)
if node_pointer.filled == 1:
map_clear(node_pointer, key_to_remove)
self.mem.free(result)
else:
# more than one key means more than 1 path,
# delete not required path and keep the others
map_clear(node_pointer, key_to_remove)
self.mem.free(result)
break
# otherwise simply remove the key
else:
result = map_get(current_node, self._terminal_hash)
if result:
map_clear(<MapStruct*>result, self.vocab.strings[key])
del self._callbacks[key]
del self._docs[key]
def add(self, key, on_match, *docs):
"""Add a match-rule to the phrase-matcher. A match-rule consists of: an ID
key, an on_match callback, and one or more patterns.
@ -159,53 +119,53 @@ cdef class PhraseMatcher:
DOCS: https://spacy.io/api/phrasematcher#add
"""
_ = self.vocab[key]
self._callbacks[key] = on_match
self._docs.setdefault(key, set())
cdef MapStruct* current_node
cdef MapStruct* internal_node
cdef void* result
cdef Doc doc
cdef hash_t ent_id = self.matcher._normalize_key(key)
self._callbacks[ent_id] = on_match
self._docs[ent_id] = docs
cdef int length
cdef int i
cdef hash_t phrase_hash
cdef Pool mem = Pool()
for doc in docs:
if len(doc) == 0:
length = doc.length
if length == 0:
continue
if isinstance(doc, Doc):
if self.attr in (POS, TAG, LEMMA) and not doc.is_tagged:
raise ValueError(Errors.E155.format())
if self.attr == DEP and not doc.is_parsed:
raise ValueError(Errors.E156.format())
if self._validate and (doc.is_tagged or doc.is_parsed) \
and self.attr not in (DEP, POS, TAG, LEMMA):
string_attr = self.vocab.strings[self.attr]
user_warning(Warnings.W012.format(key=key, attr=string_attr))
keyword = self._convert_to_array(doc)
if self.attr in (POS, TAG, LEMMA) and not doc.is_tagged:
raise ValueError(Errors.E155.format())
if self.attr == DEP and not doc.is_parsed:
raise ValueError(Errors.E156.format())
if self._validate and (doc.is_tagged or doc.is_parsed) \
and self.attr not in (DEP, POS, TAG, LEMMA):
string_attr = self.vocab.strings[self.attr]
user_warning(Warnings.W012.format(key=key, attr=string_attr))
tags = get_biluo(length)
phrase_key = <attr_t*>mem.alloc(length, sizeof(attr_t))
for i, tag in enumerate(tags):
attr_value = self.get_lex_value(doc, i)
lexeme = self.vocab[attr_value]
lexeme.set_flag(tag, True)
phrase_key[i] = lexeme.orth
phrase_hash = hash64(phrase_key, length * sizeof(attr_t), 0)
if phrase_hash in self.phrase_ids:
phrase_index = self.phrase_ids[phrase_hash]
ent_id_list = self.ent_id_matrix[phrase_index]
ent_id_list.append(ent_id)
self.ent_id_matrix[phrase_index] = ent_id_list
else:
keyword = doc
self._docs[key].add(tuple(keyword))
ent_id_list = hash_vec(1)
ent_id_list[0] = ent_id
new_index = self.ent_id_matrix.size()
if new_index == 0:
# PreshMaps can not contain 0 as value, so storing a dummy at 0
self.ent_id_matrix.push_back(hash_vec(0))
new_index = 1
self.ent_id_matrix.push_back(ent_id_list)
self.phrase_ids.set(phrase_hash, <void*>new_index)
current_node = self.c_map
for token in keyword:
if token == self._terminal_hash:
user_warning(Warnings.W021)
break
result = <MapStruct*>map_get(current_node, token)
if not result:
internal_node = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
map_init(self.mem, internal_node, 8)
map_set(self.mem, current_node, token, internal_node)
result = internal_node
current_node = <MapStruct*>result
result = <MapStruct*>map_get(current_node, self._terminal_hash)
if not result:
internal_node = <MapStruct*>self.mem.alloc(1, sizeof(MapStruct))
map_init(self.mem, internal_node, 8)
map_set(self.mem, current_node, self._terminal_hash, internal_node)
result = internal_node
map_set(self.mem, <MapStruct*>result, self.vocab.strings[key], NULL)
def __call__(self, doc):
def __call__(self, Doc doc):
"""Find all sequences matching the supplied patterns on the `Doc`.
doc (Doc): The document to match over.
@ -216,63 +176,25 @@ cdef class PhraseMatcher:
DOCS: https://spacy.io/api/phrasematcher#call
"""
matches = []
if doc is None or len(doc) == 0:
# if doc is empty or None just return empty list
return matches
cdef vector[MatchStruct] c_matches
self.find_matches(doc, &c_matches)
for i in range(c_matches.size()):
matches.append((c_matches[i].match_id, c_matches[i].start, c_matches[i].end))
if self.attr == ORTH:
match_doc = doc
else:
# If we're not matching on the ORTH, match_doc will be a Doc whose
# token.orth values are the attribute values we're matching on,
# e.g. Doc(nlp.vocab, words=[token.pos_ for token in doc])
words = [self.get_lex_value(doc, i) for i in range(len(doc))]
match_doc = Doc(self.vocab, words=words)
for _, start, end in self.matcher(match_doc):
ent_ids = self.accept_match(match_doc, start, end)
if ent_ids is not None:
for ent_id in ent_ids:
matches.append((ent_id, start, end))
for i, (ent_id, start, end) in enumerate(matches):
on_match = self._callbacks.get(ent_id)
if on_match is not None:
on_match(self, doc, i, matches)
return matches
cdef void find_matches(self, Doc doc, vector[MatchStruct] *matches) nogil:
cdef MapStruct* current_node = self.c_map
cdef int start = 0
cdef int idx = 0
cdef int idy = 0
cdef key_t key
cdef void* value
cdef int i = 0
cdef MatchStruct ms
cdef void* result
while idx < doc.length:
start = idx
token = Token.get_struct_attr(&doc.c[idx], self.attr)
# look for sequences from this position
result = map_get(current_node, token)
if result:
current_node = <MapStruct*>result
idy = idx + 1
while idy < doc.length:
result = map_get(current_node, self._terminal_hash)
if result:
i = 0
while map_iter(<MapStruct*>result, &i, &key, &value):
ms = make_matchstruct(key, start, idy)
matches.push_back(ms)
inner_token = Token.get_struct_attr(&doc.c[idy], self.attr)
result = map_get(current_node, inner_token)
if result:
current_node = <MapStruct*>result
idy += 1
else:
break
else:
# end of doc reached
result = map_get(current_node, self._terminal_hash)
if result:
i = 0
while map_iter(<MapStruct*>result, &i, &key, &value):
ms = make_matchstruct(key, start, idy)
matches.push_back(ms)
current_node = self.c_map
idx += 1
def pipe(self, stream, batch_size=1000, n_threads=-1, return_matches=False,
as_tuples=False):
"""Match a stream of documents, yielding them in turn.
@ -306,21 +228,53 @@ cdef class PhraseMatcher:
else:
yield doc
def _convert_to_array(self, Doc doc):
return [Token.get_struct_attr(&doc.c[i], self.attr) for i in range(len(doc))]
def accept_match(self, Doc doc, int start, int end):
cdef int i, j
cdef Pool mem = Pool()
phrase_key = <attr_t*>mem.alloc(end-start, sizeof(attr_t))
for i, j in enumerate(range(start, end)):
phrase_key[i] = doc.c[j].lex.orth
cdef hash_t key = hash64(phrase_key, (end-start) * sizeof(attr_t), 0)
ent_index = <hash_t>self.phrase_ids.get(key)
if ent_index == 0:
return None
return self.ent_id_matrix[ent_index]
def get_lex_value(self, Doc doc, int i):
if self.attr == ORTH:
# Return the regular orth value of the lexeme
return doc.c[i].lex.orth
# Get the attribute value instead, e.g. token.pos
attr_value = get_token_attr(&doc.c[i], self.attr)
if attr_value in (0, 1):
# Value is boolean, convert to string
string_attr_value = str(attr_value)
else:
string_attr_value = self.vocab.strings[attr_value]
string_attr_name = self.vocab.strings[self.attr]
# Concatenate the attr name and value to not pollute lexeme space
# e.g. 'POS-VERB' instead of just 'VERB', which could otherwise
# create false positive matches
return "matcher:{}-{}".format(string_attr_name, string_attr_value)
def unpickle_matcher(vocab, docs, callbacks, attr):
matcher = PhraseMatcher(vocab, attr=attr)
def get_biluo(length):
if length == 0:
raise ValueError(Errors.E127)
elif length == 1:
return [U_ENT]
elif length == 2:
return [B2_ENT, L2_ENT]
elif length == 3:
return [B3_ENT, I3_ENT, L3_ENT]
else:
return [B4_ENT, I4_ENT] + [I4_ENT] * (length-3) + [L4_ENT]
def unpickle_matcher(vocab, docs, callbacks):
matcher = PhraseMatcher(vocab)
for key, specs in docs.items():
callback = callbacks.get(key, None)
matcher.add(key, callback, *specs)
return matcher
cdef MatchStruct make_matchstruct(key_t match_id, int start, int end) nogil:
cdef MatchStruct ms
ms.match_id = match_id
ms.start = start
ms.end = end
return ms

300
spacy/morphology.pxd
View File

@ -1,41 +1,301 @@
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap, PreshMapArray
from preshed.maps cimport PreshMapArray
from libc.stdint cimport uint64_t
from murmurhash cimport mrmr
from .structs cimport TokenC, MorphAnalysisC
from .structs cimport TokenC
from .strings cimport StringStore
from .typedefs cimport hash_t, attr_t, flags_t
from .typedefs cimport attr_t, flags_t
from .parts_of_speech cimport univ_pos_t
from . cimport symbols
cdef struct RichTagC:
uint64_t morph
int id
univ_pos_t pos
attr_t name
cdef struct MorphAnalysisC:
RichTagC tag
attr_t lemma
cdef class Morphology:
cdef readonly Pool mem
cdef readonly StringStore strings
cdef PreshMap tags # Keyed by hash, value is pointer to tag
cdef public object lemmatizer
cdef readonly object tag_map
cdef readonly object tag_names
cdef readonly object reverse_index
cdef readonly object exc
cdef readonly object _feat_map
cdef readonly PreshMapArray _cache
cdef readonly int n_tags
cdef public object n_tags
cdef public object reverse_index
cdef public object tag_names
cdef public object exc
cdef RichTagC* rich_tags
cdef PreshMapArray _cache
cpdef update(self, hash_t morph, features)
cdef hash_t insert(self, MorphAnalysisC tag) except 0
cdef int assign_untagged(self, TokenC* token) except -1
cdef int assign_tag(self, TokenC* token, tag) except -1
cdef int assign_tag_id(self, TokenC* token, int tag_id) except -1
cdef int _assign_tag_from_exceptions(self, TokenC* token, int tag_id) except -1
cdef int assign_feature(self, uint64_t* morph, univ_morph_t feat_id, bint value) except -1
cdef int check_feature(const MorphAnalysisC* tag, attr_t feature) nogil
cdef attr_t get_field(const MorphAnalysisC* tag, int field) nogil
cdef list list_features(const MorphAnalysisC* tag)
cdef enum univ_morph_t:
NIL = 0
Animacy_anim = symbols.Animacy_anim
Animacy_inan
Animacy_hum
Animacy_nhum
Aspect_freq
Aspect_imp
Aspect_mod
Aspect_none
Aspect_perf
Case_abe
Case_abl
Case_abs
Case_acc
Case_ade
Case_all
Case_cau
Case_com
Case_dat
Case_del
Case_dis
Case_ela
Case_ess
Case_gen
Case_ill
Case_ine
Case_ins
Case_loc
Case_lat
Case_nom
Case_par
Case_sub
Case_sup
Case_tem
Case_ter
Case_tra
Case_voc
Definite_two
Definite_def
Definite_red
Definite_cons # U20
Definite_ind
Degree_cmp
Degree_comp
Degree_none
Degree_pos
Degree_sup
Degree_abs
Degree_com
Degree_dim # du
Gender_com
Gender_fem
Gender_masc
Gender_neut
Mood_cnd
Mood_imp
Mood_ind
Mood_n
Mood_pot
Mood_sub
Mood_opt
Negative_neg
Negative_pos
Negative_yes
Polarity_neg # U20
Polarity_pos # U20
Number_com
Number_dual
Number_none
Number_plur
Number_sing
Number_ptan # bg
Number_count # bg
NumType_card
NumType_dist
NumType_frac
NumType_gen
NumType_mult
NumType_none
NumType_ord
NumType_sets
Person_one
Person_two
Person_three
Person_none
Poss_yes
PronType_advPart
PronType_art
PronType_default
PronType_dem
PronType_ind
PronType_int
PronType_neg
PronType_prs
PronType_rcp
PronType_rel
PronType_tot
PronType_clit
PronType_exc # es, ca, it, fa
Reflex_yes
Tense_fut
Tense_imp
Tense_past
Tense_pres
VerbForm_fin
VerbForm_ger
VerbForm_inf
VerbForm_none
VerbForm_part
VerbForm_partFut
VerbForm_partPast
VerbForm_partPres
VerbForm_sup
VerbForm_trans
VerbForm_conv # U20
VerbForm_gdv # la
Voice_act
Voice_cau
Voice_pass
Voice_mid # gkc
Voice_int # hb
Abbr_yes # cz, fi, sl, U
AdpType_prep # cz, U
AdpType_post # U
AdpType_voc # cz
AdpType_comprep # cz
AdpType_circ # U
AdvType_man
AdvType_loc
AdvType_tim
AdvType_deg
AdvType_cau
AdvType_mod
AdvType_sta
AdvType_ex
AdvType_adadj
ConjType_oper # cz, U
ConjType_comp # cz, U
Connegative_yes # fi
Derivation_minen # fi
Derivation_sti # fi
Derivation_inen # fi
Derivation_lainen # fi
Derivation_ja # fi
Derivation_ton # fi
Derivation_vs # fi
Derivation_ttain # fi
Derivation_ttaa # fi
Echo_rdp # U
Echo_ech # U
Foreign_foreign # cz, fi, U
Foreign_fscript # cz, fi, U
Foreign_tscript # cz, U
Foreign_yes # sl
Gender_dat_masc # bq, U
Gender_dat_fem # bq, U
Gender_erg_masc # bq
Gender_erg_fem # bq
Gender_psor_masc # cz, sl, U
Gender_psor_fem # cz, sl, U
Gender_psor_neut # sl
Hyph_yes # cz, U
InfForm_one # fi
InfForm_two # fi
InfForm_three # fi
NameType_geo # U, cz
NameType_prs # U, cz
NameType_giv # U, cz
NameType_sur # U, cz
NameType_nat # U, cz
NameType_com # U, cz
NameType_pro # U, cz
NameType_oth # U, cz
NounType_com # U
NounType_prop # U
NounType_class # U
Number_abs_sing # bq, U
Number_abs_plur # bq, U
Number_dat_sing # bq, U
Number_dat_plur # bq, U
Number_erg_sing # bq, U
Number_erg_plur # bq, U
Number_psee_sing # U
Number_psee_plur # U
Number_psor_sing # cz, fi, sl, U
Number_psor_plur # cz, fi, sl, U
NumForm_digit # cz, sl, U
NumForm_roman # cz, sl, U
NumForm_word # cz, sl, U
NumValue_one # cz, U
NumValue_two # cz, U
NumValue_three # cz, U
PartForm_pres # fi
PartForm_past # fi
PartForm_agt # fi
PartForm_neg # fi
PartType_mod # U
PartType_emp # U
PartType_res # U
PartType_inf # U
PartType_vbp # U
Person_abs_one # bq, U
Person_abs_two # bq, U
Person_abs_three # bq, U
Person_dat_one # bq, U
Person_dat_two # bq, U
Person_dat_three # bq, U
Person_erg_one # bq, U
Person_erg_two # bq, U
Person_erg_three # bq, U
Person_psor_one # fi, U
Person_psor_two # fi, U
Person_psor_three # fi, U
Polite_inf # bq, U
Polite_pol # bq, U
Polite_abs_inf # bq, U
Polite_abs_pol # bq, U
Polite_erg_inf # bq, U
Polite_erg_pol # bq, U
Polite_dat_inf # bq, U
Polite_dat_pol # bq, U
Prefix_yes # U
PrepCase_npr # cz
PrepCase_pre # U
PunctSide_ini # U
PunctSide_fin # U
PunctType_peri # U
PunctType_qest # U
PunctType_excl # U
PunctType_quot # U
PunctType_brck # U
PunctType_comm # U
PunctType_colo # U
PunctType_semi # U
PunctType_dash # U
Style_arch # cz, fi, U
Style_rare # cz, fi, U
Style_poet # cz, U
Style_norm # cz, U
Style_coll # cz, U
Style_vrnc # cz, U
Style_sing # cz, U
Style_expr # cz, U
Style_derg # cz, U
Style_vulg # cz, U
Style_yes # fi, U
StyleVariant_styleShort # cz
StyleVariant_styleBound # cz, sl
VerbType_aux # U
VerbType_cop # U
VerbType_mod # U
VerbType_light # U
cdef tag_to_json(const MorphAnalysisC* tag)

1376
spacy/morphology.pyx
View File

File diff suppressed because it is too large Load Diff

2
spacy/pipeline/__init__.py
View File

@ -3,7 +3,6 @@ from __future__ import unicode_literals
from .pipes import Tagger, DependencyParser, EntityRecognizer, EntityLinker
from .pipes import TextCategorizer, Tensorizer, Pipe, Sentencizer
from .morphologizer import Morphologizer
from .entityruler import EntityRuler
from .hooks import SentenceSegmenter, SimilarityHook
from .functions import merge_entities, merge_noun_chunks, merge_subtokens
@ -16,7 +15,6 @@ __all__ = [
"TextCategorizer",
"Tensorizer",
"Pipe",
"Morphologizer",
"EntityRuler",
"Sentencizer",
"SentenceSegmenter",

37
spacy/pipeline/entityruler.py
View File

@ -180,28 +180,21 @@ class EntityRuler(object):
DOCS: https://spacy.io/api/entityruler#add_patterns
"""
# disable the nlp components after this one in case they hadn't been initialized / deserialised yet
try:
current_index = self.nlp.pipe_names.index(self.name)
subsequent_pipes = [pipe for pipe in self.nlp.pipe_names[current_index + 1:]]
except ValueError:
subsequent_pipes = []
with self.nlp.disable_pipes(*subsequent_pipes):
for entry in patterns:
label = entry["label"]
if "id" in entry:
label = self._create_label(label, entry["id"])
pattern = entry["pattern"]
if isinstance(pattern, basestring_):
self.phrase_patterns[label].append(self.nlp(pattern))
elif isinstance(pattern, list):
self.token_patterns[label].append(pattern)
else:
raise ValueError(Errors.E097.format(pattern=pattern))
for label, patterns in self.token_patterns.items():
self.matcher.add(label, None, *patterns)
for label, patterns in self.phrase_patterns.items():
self.phrase_matcher.add(label, None, *patterns)
for entry in patterns:
label = entry["label"]
if "id" in entry:
label = self._create_label(label, entry["id"])
pattern = entry["pattern"]
if isinstance(pattern, basestring_):
self.phrase_patterns[label].append(self.nlp(pattern))
elif isinstance(pattern, list):
self.token_patterns[label].append(pattern)
else:
raise ValueError(Errors.E097.format(pattern=pattern))
for label, patterns in self.token_patterns.items():
self.matcher.add(label, None, *patterns)
for label, patterns in self.phrase_patterns.items():
self.phrase_matcher.add(label, None, *patterns)
def _split_label(self, label):
"""Split Entity label into ent_label and ent_id if it contains self.ent_id_sep

164
spacy/pipeline/morphologizer.pyx
View File

@ -1,164 +0,0 @@
from __future__ import unicode_literals
from collections import OrderedDict, defaultdict
import numpy
cimport numpy as np
from thinc.api import chain
from thinc.neural.util import to_categorical, copy_array, get_array_module
from .. import util
from .pipes import Pipe
from .._ml import Tok2Vec, build_morphologizer_model
from .._ml import link_vectors_to_models, zero_init, flatten
from .._ml import create_default_optimizer
from ..errors import Errors, TempErrors
from ..compat import basestring_
from ..tokens.doc cimport Doc
from ..vocab cimport Vocab
from ..morphology cimport Morphology
class Morphologizer(Pipe):
name = 'morphologizer'
@classmethod
def Model(cls, **cfg):
if cfg.get('pretrained_dims') and not cfg.get('pretrained_vectors'):
raise ValueError(TempErrors.T008)
class_map = Morphology.create_class_map()
return build_morphologizer_model(class_map.field_sizes, **cfg)
def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
self.cfg = OrderedDict(sorted(cfg.items()))
self.cfg.setdefault('cnn_maxout_pieces', 2)
self._class_map = self.vocab.morphology.create_class_map()
@property
def labels(self):
return self.vocab.morphology.tag_names
@property
def tok2vec(self):
if self.model in (None, True, False):
return None
else:
return chain(self.model.tok2vec, flatten)
def __call__(self, doc):
features, tokvecs = self.predict([doc])
self.set_annotations([doc], features, tensors=tokvecs)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in util.minibatch(stream, size=batch_size):
docs = list(docs)
features, tokvecs = self.predict(docs)
self.set_annotations(docs, features, tensors=tokvecs)
yield from docs
def predict(self, docs):
if not any(len(doc) for doc in docs):
# Handle case where there are no tokens in any docs.
n_labels = self.model.nO
guesses = [self.model.ops.allocate((0, n_labels)) for doc in docs]
tokvecs = self.model.ops.allocate((0, self.model.tok2vec.nO))
return guesses, tokvecs
tokvecs = self.model.tok2vec(docs)
scores = self.model.softmax(tokvecs)
return scores, tokvecs
def set_annotations(self, docs, batch_scores, tensors=None):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef Vocab vocab = self.vocab
offsets = [self._class_map.get_field_offset(field)
for field in self._class_map.fields]
for i, doc in enumerate(docs):
doc_scores = batch_scores[i]
doc_guesses = scores_to_guesses(doc_scores, self.model.softmax.out_sizes)
# Convert the neuron indices into feature IDs.
doc_feat_ids = numpy.zeros((len(doc), len(self._class_map.fields)), dtype='i')
for j in range(len(doc)):
for k, offset in enumerate(offsets):
if doc_guesses[j, k] == 0:
doc_feat_ids[j, k] = 0
else:
doc_feat_ids[j, k] = offset + doc_guesses[j, k]
# Get the set of feature names.
feats = {self._class_map.col2info[f][2] for f in doc_feat_ids[j]}
if "NIL" in feats:
feats.remove("NIL")
# Now add the analysis, and set the hash.
doc.c[j].morph = self.vocab.morphology.add(feats)
if doc[j].morph.pos != 0:
doc.c[j].pos = doc[j].morph.pos
def update(self, docs, golds, drop=0., sgd=None, losses=None):
if losses is not None and self.name not in losses:
losses[self.name] = 0.
tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
bp_tag_scores(d_tag_scores, sgd=sgd)
if losses is not None:
losses[self.name] += loss
def get_loss(self, docs, golds, scores):
guesses = []
for doc_scores in scores:
guesses.append(scores_to_guesses(doc_scores, self.model.softmax.out_sizes))
guesses = self.model.ops.xp.vstack(guesses)
scores = self.model.ops.xp.vstack(scores)
if not isinstance(scores, numpy.ndarray):
scores = scores.get()
if not isinstance(guesses, numpy.ndarray):
guesses = guesses.get()
cdef int idx = 0
# Do this on CPU, as we can't vectorize easily.
target = numpy.zeros(scores.shape, dtype='f')
field_sizes = self.model.softmax.out_sizes
for doc, gold in zip(docs, golds):
for t, features in enumerate(gold.morphology):
if features is None:
target[idx] = scores[idx]
else:
gold_fields = {}
for feature in features:
field = self._class_map.feat2field[feature]
gold_fields[field] = self._class_map.feat2offset[feature]
for field in self._class_map.fields:
field_id = self._class_map.field2id[field]
col_offset = self._class_map.field2col[field]
if field_id in gold_fields:
target[idx, col_offset + gold_fields[field_id]] = 1.
else:
target[idx, col_offset] = 1.
#print(doc[t])
#for col, info in enumerate(self._class_map.col2info):
# print(col, info, scores[idx, col], target[idx, col])
idx += 1
target = self.model.ops.asarray(target, dtype='f')
scores = self.model.ops.asarray(scores, dtype='f')
d_scores = scores - target
loss = (d_scores**2).sum()
d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
return float(loss), d_scores
def use_params(self, params):
with self.model.use_params(params):
yield
def scores_to_guesses(scores, out_sizes):
xp = get_array_module(scores)
guesses = xp.zeros((scores.shape[0], len(out_sizes)), dtype='i')
offset = 0
for i, size in enumerate(out_sizes):
slice_ = scores[:, offset : offset + size]
col_guesses = slice_.argmax(axis=1)
guesses[:, i] = col_guesses
offset += size
return guesses

30
spacy/pipeline/pipes.pyx
View File

@ -69,7 +69,7 @@ class Pipe(object):
predictions = self.predict([doc])
if isinstance(predictions, tuple) and len(predictions) == 2:
scores, tensors = predictions
self.set_annotations([doc], scores, tensors=tensors)
self.set_annotations([doc], scores, tensor=tensors)
else:
self.set_annotations([doc], predictions)
return doc
@ -90,7 +90,7 @@ class Pipe(object):
predictions = self.predict(docs)
if isinstance(predictions, tuple) and len(tuple) == 2:
scores, tensors = predictions
self.set_annotations(docs, scores, tensors=tensors)
self.set_annotations(docs, scores, tensor=tensors)
else:
self.set_annotations(docs, predictions)
yield from docs
@ -424,22 +424,18 @@ class Tagger(Pipe):
cdef Doc doc
cdef int idx = 0
cdef Vocab vocab = self.vocab
assign_morphology = self.cfg.get("set_morphology", True)
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
if hasattr(doc_tag_ids, "get"):
doc_tag_ids = doc_tag_ids.get()
for j, tag_id in enumerate(doc_tag_ids):
# Don't clobber preset POS tags
if doc.c[j].tag == 0:
if doc.c[j].pos == 0 and assign_morphology:
# Don't clobber preset lemmas
lemma = doc.c[j].lemma
vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
if lemma != 0 and lemma != doc.c[j].lex.orth:
doc.c[j].lemma = lemma
else:
doc.c[j].tag = self.vocab.strings[self.labels[tag_id]]
if doc.c[j].tag == 0 and doc.c[j].pos == 0:
# Don't clobber preset lemmas
lemma = doc.c[j].lemma
vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
if lemma != 0 and lemma != doc.c[j].lex.orth:
doc.c[j].lemma = lemma
idx += 1
if tensors is not None and len(tensors):
if isinstance(doc.tensor, numpy.ndarray) \
@ -504,7 +500,6 @@ class Tagger(Pipe):
orig_tag_map = dict(self.vocab.morphology.tag_map)
new_tag_map = OrderedDict()
for raw_text, annots_brackets in get_gold_tuples():
_ = annots_brackets.pop()
for annots, brackets in annots_brackets:
ids, words, tags, heads, deps, ents = annots
for tag in tags:
@ -937,6 +932,11 @@ class TextCategorizer(Pipe):
def labels(self, value):
self.cfg["labels"] = tuple(value)
def __call__(self, doc):
scores, tensors = self.predict([doc])
self.set_annotations([doc], scores, tensors=tensors)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in util.minibatch(stream, size=batch_size):
docs = list(docs)
@ -1017,10 +1017,6 @@ class TextCategorizer(Pipe):
return 1
def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs):
for raw_text, annots_brackets in get_gold_tuples():
cats = annots_brackets.pop()
for cat in cats:
self.add_label(cat)
if self.model is True:
self.cfg["pretrained_vectors"] = kwargs.get("pretrained_vectors")
self.require_labels()

385
spacy/scorer.py
View File

@ -1,10 +1,7 @@
# coding: utf8
from __future__ import division, print_function, unicode_literals
import numpy as np
from .gold import tags_to_entities, GoldParse
from .errors import Errors
class PRFScore(object):
@ -37,39 +34,10 @@ class PRFScore(object):
return 2 * ((p * r) / (p + r + 1e-100))
class ROCAUCScore(object):
"""
An AUC ROC score.
"""
def __init__(self):
self.golds = []
self.cands = []
self.saved_score = 0.0
self.saved_score_at_len = 0
def score_set(self, cand, gold):
self.cands.append(cand)
self.golds.append(gold)
@property
def score(self):
if len(self.golds) == self.saved_score_at_len:
return self.saved_score
try:
self.saved_score = _roc_auc_score(self.golds, self.cands)
# catch ValueError: Only one class present in y_true.
# ROC AUC score is not defined in that case.
except ValueError:
self.saved_score = -float("inf")
self.saved_score_at_len = len(self.golds)
return self.saved_score
class Scorer(object):
"""Compute evaluation scores."""
def __init__(self, eval_punct=False, pipeline=None):
def __init__(self, eval_punct=False):
"""Initialize the Scorer.
eval_punct (bool): Evaluate the dependency attachments to and from
@ -86,24 +54,6 @@ class Scorer(object):
self.ner = PRFScore()
self.ner_per_ents = dict()
self.eval_punct = eval_punct
self.textcat = None
self.textcat_per_cat = dict()
self.textcat_positive_label = None
self.textcat_multilabel = False
if pipeline:
for name, model in pipeline:
if name == "textcat":
self.textcat_positive_label = model.cfg.get("positive_label", None)
if self.textcat_positive_label:
self.textcat = PRFScore()
if not model.cfg.get("exclusive_classes", False):
self.textcat_multilabel = True
for label in model.cfg.get("labels", []):
self.textcat_per_cat[label] = ROCAUCScore()
else:
for label in model.cfg.get("labels", []):
self.textcat_per_cat[label] = PRFScore()
@property
def tags_acc(self):
@ -151,47 +101,10 @@ class Scorer(object):
for k, v in self.ner_per_ents.items()
}
@property
def textcat_score(self):
"""RETURNS (float): f-score on positive label for binary exclusive,
macro-averaged f-score for 3+ exclusive,
macro-averaged AUC ROC score for multilabel (-1 if undefined)
"""
if not self.textcat_multilabel:
# binary multiclass
if self.textcat_positive_label:
return self.textcat.fscore * 100
# other multiclass
return (
sum([score.fscore for label, score in self.textcat_per_cat.items()])
/ (len(self.textcat_per_cat) + 1e-100)
* 100
)
# multilabel
return max(
sum([score.score for label, score in self.textcat_per_cat.items()])
/ (len(self.textcat_per_cat) + 1e-100),
-1,
)
@property
def textcats_per_cat(self):
"""RETURNS (dict): Scores per textcat label.
"""
if not self.textcat_multilabel:
return {
k: {"p": v.precision * 100, "r": v.recall * 100, "f": v.fscore * 100}
for k, v in self.textcat_per_cat.items()
}
return {
k: {"roc_auc_score": max(v.score, -1)}
for k, v in self.textcat_per_cat.items()
}
@property
def scores(self):
"""RETURNS (dict): All scores with keys `uas`, `las`, `ents_p`,
`ents_r`, `ents_f`, `tags_acc`, `token_acc`, and `textcat_score`.
`ents_r`, `ents_f`, `tags_acc` and `token_acc`.
"""
return {
"uas": self.uas,
@ -202,8 +115,6 @@ class Scorer(object):
"ents_per_type": self.ents_per_type,
"tags_acc": self.tags_acc,
"token_acc": self.token_acc,
"textcat_score": self.textcat_score,
"textcats_per_cat": self.textcats_per_cat,
}
def score(self, doc, gold, verbose=False, punct_labels=("p", "punct")):
@ -281,301 +192,9 @@ class Scorer(object):
self.unlabelled.score_set(
set(item[:2] for item in cand_deps), set(item[:2] for item in gold_deps)
)
if (
len(gold.cats) > 0
and set(self.textcat_per_cat) == set(gold.cats)
and set(gold.cats) == set(doc.cats)
):
goldcat = max(gold.cats, key=gold.cats.get)
candcat = max(doc.cats, key=doc.cats.get)
if self.textcat_positive_label:
self.textcat.score_set(
set([self.textcat_positive_label]) & set([candcat]),
set([self.textcat_positive_label]) & set([goldcat]),
)
for label in self.textcat_per_cat:
if self.textcat_multilabel:
self.textcat_per_cat[label].score_set(
doc.cats[label], gold.cats[label]
)
else:
self.textcat_per_cat[label].score_set(
set([label]) & set([candcat]), set([label]) & set([goldcat])
)
elif len(self.textcat_per_cat) > 0:
model_labels = set(self.textcat_per_cat)
eval_labels = set(gold.cats)
raise ValueError(
Errors.E162.format(model_labels=model_labels, eval_labels=eval_labels)
)
if verbose:
gold_words = [item[1] for item in gold.orig_annot]
for w_id, h_id, dep in cand_deps - gold_deps:
print("F", gold_words[w_id], dep, gold_words[h_id])
for w_id, h_id, dep in gold_deps - cand_deps:
print("M", gold_words[w_id], dep, gold_words[h_id])
#############################################################################
#
# The following implementation of roc_auc_score() is adapted from
# scikit-learn, which is distributed under the following license:
#
# New BSD License
#
# Copyright (c) 2007–2019 The scikit-learn developers.
# All rights reserved.
#
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# a. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# b. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# c. Neither the name of the Scikit-learn Developers nor the names of
# its contributors may be used to endorse or promote products
# derived from this software without specific prior written
# permission.
#
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
# DAMAGE.
def _roc_auc_score(y_true, y_score):
"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
from prediction scores.
Note: this implementation is restricted to the binary classification task
Parameters
----------
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels or binary label indicators.
The multiclass case expects shape = [n_samples] and labels
with values in ``range(n_classes)``.
y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive
class, confidence values, or non-thresholded measure of decisions
(as returned by "decision_function" on some classifiers). For binary
y_true, y_score is supposed to be the score of the class with greater
label. The multiclass case expects shape = [n_samples, n_classes]
where the scores correspond to probability estimates.
Returns
-------
auc : float
References
----------
.. [1] `Wikipedia entry for the Receiver operating characteristic
<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
.. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
Letters, 2006, 27(8):861-874.
.. [3] `Analyzing a portion of the ROC curve. McClish, 1989
<https://www.ncbi.nlm.nih.gov/pubmed/2668680>`_
"""
if len(np.unique(y_true)) != 2:
raise ValueError(Errors.E165)
fpr, tpr, _ = _roc_curve(y_true, y_score)
return _auc(fpr, tpr)
def _roc_curve(y_true, y_score):
"""Compute Receiver operating characteristic (ROC)
Note: this implementation is restricted to the binary classification task.
Parameters
----------
y_true : array, shape = [n_samples]
True binary labels. If labels are not either {-1, 1} or {0, 1}, then
pos_label should be explicitly given.
y_score : array, shape = [n_samples]
Target scores, can either be probability estimates of the positive
class, confidence values, or non-thresholded measure of decisions
(as returned by "decision_function" on some classifiers).
Returns
-------
fpr : array, shape = [>2]
Increasing false positive rates such that element i is the false
positive rate of predictions with score >= thresholds[i].
tpr : array, shape = [>2]
Increasing true positive rates such that element i is the true
positive rate of predictions with score >= thresholds[i].
thresholds : array, shape = [n_thresholds]
Decreasing thresholds on the decision function used to compute
fpr and tpr. `thresholds[0]` represents no instances being predicted
and is arbitrarily set to `max(y_score) + 1`.
Notes
-----
Since the thresholds are sorted from low to high values, they
are reversed upon returning them to ensure they correspond to both ``fpr``
and ``tpr``, which are sorted in reversed order during their calculation.
References
----------
.. [1] `Wikipedia entry for the Receiver operating characteristic
<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
.. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
Letters, 2006, 27(8):861-874.
"""
fps, tps, thresholds = _binary_clf_curve(y_true, y_score)
# Add an extra threshold position
# to make sure that the curve starts at (0, 0)
tps = np.r_[0, tps]
fps = np.r_[0, fps]
thresholds = np.r_[thresholds[0] + 1, thresholds]
if fps[-1] <= 0:
fpr = np.repeat(np.nan, fps.shape)
else:
fpr = fps / fps[-1]
if tps[-1] <= 0:
tpr = np.repeat(np.nan, tps.shape)
else:
tpr = tps / tps[-1]
return fpr, tpr, thresholds
def _binary_clf_curve(y_true, y_score):
"""Calculate true and false positives per binary classification threshold.
Parameters
----------
y_true : array, shape = [n_samples]
True targets of binary classification
y_score : array, shape = [n_samples]
Estimated probabilities or decision function
Returns
-------
fps : array, shape = [n_thresholds]
A count of false positives, at index i being the number of negative
samples assigned a score >= thresholds[i]. The total number of
negative samples is equal to fps[-1] (thus true negatives are given by
fps[-1] - fps).
tps : array, shape = [n_thresholds <= len(np.unique(y_score))]
An increasing count of true positives, at index i being the number
of positive samples assigned a score >= thresholds[i]. The total
number of positive samples is equal to tps[-1] (thus false negatives
are given by tps[-1] - tps).
thresholds : array, shape = [n_thresholds]
Decreasing score values.
"""
pos_label = 1.0
y_true = np.ravel(y_true)
y_score = np.ravel(y_score)
# make y_true a boolean vector
y_true = y_true == pos_label
# sort scores and corresponding truth values
desc_score_indices = np.argsort(y_score, kind="mergesort")[::-1]
y_score = y_score[desc_score_indices]
y_true = y_true[desc_score_indices]
weight = 1.0
# y_score typically has many tied values. Here we extract
# the indices associated with the distinct values. We also
# concatenate a value for the end of the curve.
distinct_value_indices = np.where(np.diff(y_score))[0]
threshold_idxs = np.r_[distinct_value_indices, y_true.size - 1]
# accumulate the true positives with decreasing threshold
tps = _stable_cumsum(y_true * weight)[threshold_idxs]
fps = 1 + threshold_idxs - tps
return fps, tps, y_score[threshold_idxs]
def _stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08):
"""Use high precision for cumsum and check that final value matches sum
Parameters
----------
arr : array-like
To be cumulatively summed as flat
axis : int, optional
Axis along which the cumulative sum is computed.
The default (None) is to compute the cumsum over the flattened array.
rtol : float
Relative tolerance, see ``np.allclose``
atol : float
Absolute tolerance, see ``np.allclose``
"""
out = np.cumsum(arr, axis=axis, dtype=np.float64)
expected = np.sum(arr, axis=axis, dtype=np.float64)
if not np.all(
np.isclose(
out.take(-1, axis=axis), expected, rtol=rtol, atol=atol, equal_nan=True
)
):
raise ValueError(Errors.E163)
return out
def _auc(x, y):
"""Compute Area Under the Curve (AUC) using the trapezoidal rule
This is a general function, given points on a curve. For computing the
area under the ROC-curve, see :func:`roc_auc_score`.
Parameters
----------
x : array, shape = [n]
x coordinates. These must be either monotonic increasing or monotonic
decreasing.
y : array, shape = [n]
y coordinates.
Returns
-------
auc : float
"""
x = np.ravel(x)
y = np.ravel(y)
direction = 1
dx = np.diff(x)
if np.any(dx < 0):
if np.all(dx <= 0):
direction = -1
else:
raise ValueError(Errors.E164.format(x))
area = direction * np.trapz(y, x)
if isinstance(area, np.memmap):
# Reductions such as .sum used internally in np.trapz do not return a
# scalar by default for numpy.memmap instances contrary to
# regular numpy.ndarray instances.
area = area.dtype.type(area)
return area

16
spacy/strings.pyx
View File

@ -119,7 +119,9 @@ cdef class StringStore:
return ""
elif string_or_id in SYMBOLS_BY_STR:
return SYMBOLS_BY_STR[string_or_id]
cdef hash_t key
if isinstance(string_or_id, unicode):
key = hash_string(string_or_id)
return key
@ -137,20 +139,6 @@ cdef class StringStore:
else:
return decode_Utf8Str(utf8str)
def as_int(self, key):
"""If key is an int, return it; otherwise, get the int value."""
if not isinstance(key, basestring):
return key
else:
return self[key]
def as_string(self, key):
"""If key is a string, return it; otherwise, get the string value."""
if isinstance(key, basestring):
return key
else:
return self[key]
def add(self, string):
"""Add a string to the StringStore.

48
spacy/structs.pxd
View File

@ -78,54 +78,6 @@ cdef struct TokenC:
hash_t ent_id
cdef struct MorphAnalysisC:
univ_pos_t pos
int length
attr_t abbr
attr_t adp_type
attr_t adv_type
attr_t animacy
attr_t aspect
attr_t case
attr_t conj_type
attr_t connegative
attr_t definite
attr_t degree
attr_t derivation
attr_t echo
attr_t foreign
attr_t gender
attr_t hyph
attr_t inf_form
attr_t mood
attr_t negative
attr_t number
attr_t name_type
attr_t noun_type
attr_t num_form
attr_t num_type
attr_t num_value
attr_t part_form
attr_t part_type
attr_t person
attr_t polite
attr_t polarity
attr_t poss
attr_t prefix
attr_t prep_case
attr_t pron_type
attr_t punct_side
attr_t punct_type
attr_t reflex
attr_t style
attr_t style_variant
attr_t tense
attr_t typo
attr_t verb_form
attr_t voice
attr_t verb_type
# Internal struct, for storage and disambiguation of entities.
cdef struct KBEntryC:

1
spacy/syntax/arc_eager.pyx
View File

@ -342,7 +342,6 @@ cdef class ArcEager(TransitionSystem):
actions[RIGHT][label] = 1
actions[REDUCE][label] = 1
for raw_text, sents in kwargs.get('gold_parses', []):
_ = sents.pop()
for (ids, words, tags, heads, labels, iob), ctnts in sents:
heads, labels = nonproj.projectivize(heads, labels)
for child, head, label in zip(ids, heads, labels):

53
spacy/syntax/ner.pyx
View File

@ -66,14 +66,12 @@ cdef class BiluoPushDown(TransitionSystem):
UNIT: Counter(),
OUT: Counter()
}
actions[OUT][''] = 1 # Represents a token predicted to be outside of any entity
actions[UNIT][''] = 1 # Represents a token prohibited to be in an entity
actions[OUT][''] = 1
for entity_type in kwargs.get('entity_types', []):
for action in (BEGIN, IN, LAST, UNIT):
actions[action][entity_type] = 1
moves = ('M', 'B', 'I', 'L', 'U')
for raw_text, sents in kwargs.get('gold_parses', []):
_ = sents.pop()
for (ids, words, tags, heads, labels, biluo), _ in sents:
for i, ner_tag in enumerate(biluo):
if ner_tag != 'O' and ner_tag != '-':
@ -163,7 +161,8 @@ cdef class BiluoPushDown(TransitionSystem):
for i in range(self.n_moves):
if self.c[i].move == move and self.c[i].label == label:
return self.c[i]
raise KeyError(Errors.E022.format(name=name))
else:
raise KeyError(Errors.E022.format(name=name))
cdef Transition init_transition(self, int clas, int move, attr_t label) except *:
# TODO: Apparent Cython bug here when we try to use the Transition()
@ -267,7 +266,7 @@ cdef class Begin:
return False
elif label == 0:
return False
elif preset_ent_iob == 1:
elif preset_ent_iob == 1 or preset_ent_iob == 2:
# Ensure we don't clobber preset entities. If no entity preset,
# ent_iob is 0
return False
@ -283,8 +282,8 @@ cdef class Begin:
# Otherwise, force acceptance, even if we're across a sentence
# boundary or the token is whitespace.
return True
elif st.B_(1).ent_iob == 3:
# If the next word is B, we can't B now
elif st.B_(1).ent_iob == 2 or st.B_(1).ent_iob == 3:
# If the next word is B or O, we can't B now
return False
elif st.B_(1).sent_start == 1:
# Don't allow entities to extend across sentence boundaries
@ -327,7 +326,6 @@ cdef class In:
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef int preset_ent_iob = st.B_(0).ent_iob
cdef attr_t preset_ent_label = st.B_(0).ent_type
if label == 0:
return False
elif st.E_(0).ent_type != label:
@ -337,22 +335,13 @@ cdef class In:
elif st.B(1) == -1:
# If we're at the end, we can't I.
return False
elif preset_ent_iob == 2:
return False
elif preset_ent_iob == 3:
return False
elif st.B_(1).ent_iob == 3:
# If we know the next word is B, we can't be I (must be L)
elif st.B_(1).ent_iob == 2 or st.B_(1).ent_iob == 3:
# If we know the next word is B or O, we can't be I (must be L)
return False
elif preset_ent_iob == 1:
if st.B_(1).ent_iob in (0, 2):
# if next preset is missing or O, this can't be I (must be L)
return False
elif label != preset_ent_label:
# If label isn't right, reject
return False
else:
# Otherwise, force acceptance, even if we're across a sentence
# boundary or the token is whitespace.
return True
elif st.B(1) != -1 and st.B_(1).sent_start == 1:
# Don't allow entities to extend across sentence boundaries
return False
@ -398,24 +387,17 @@ cdef class In:
else:
return 1
cdef class Last:
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef int preset_ent_iob = st.B_(0).ent_iob
cdef attr_t preset_ent_label = st.B_(0).ent_type
if label == 0:
return False
elif not st.entity_is_open():
return False
elif preset_ent_iob == 1 and st.B_(1).ent_iob != 1:
elif st.B_(0).ent_iob == 1 and st.B_(1).ent_iob != 1:
# If a preset entity has I followed by not-I, is L
if label != preset_ent_label:
# If label isn't right, reject
return False
else:
# Otherwise, force acceptance, even if we're across a sentence
# boundary or the token is whitespace.
return True
return True
elif st.E_(0).ent_type != label:
return False
elif st.B_(1).ent_iob == 1:
@ -468,13 +450,12 @@ cdef class Unit:
cdef int preset_ent_iob = st.B_(0).ent_iob
cdef attr_t preset_ent_label = st.B_(0).ent_type
if label == 0:
# this is only allowed if it's a preset blocked annotation
if preset_ent_label == 0 and preset_ent_iob == 3:
return True
else:
return False
return False
elif st.entity_is_open():
return False
elif preset_ent_iob == 2:
# Don't clobber preset O
return False
elif st.B_(1).ent_iob == 1:
# If next token is In, we can't be Unit -- must be Begin
return False

26
spacy/syntax/nn_parser.pyx
View File

@ -135,9 +135,7 @@ cdef class Parser:
names = []
for i in range(self.moves.n_moves):
name = self.moves.move_name(self.moves.c[i].move, self.moves.c[i].label)
# Explicitly removing the internal "U-" token used for blocking entities
if name != "U-":
names.append(name)
names.append(name)
return names
nr_feature = 8
@ -163,16 +161,10 @@ cdef class Parser:
added = self.moves.add_action(action, label)
if added:
resized = True
if resized:
self._resize()
def _resize(self):
if "nr_class" in self.cfg:
if resized and "nr_class" in self.cfg:
self.cfg["nr_class"] = self.moves.n_moves
if self.model not in (True, False, None):
if self.model not in (True, False, None) and resized:
self.model.resize_output(self.moves.n_moves)
if self._rehearsal_model not in (True, False, None):
self._rehearsal_model.resize_output(self.moves.n_moves)
def add_multitask_objective(self, target):
# Defined in subclasses, to avoid circular import
@ -243,9 +235,7 @@ cdef class Parser:
if isinstance(docs, Doc):
docs = [docs]
if not any(len(doc) for doc in docs):
result = self.moves.init_batch(docs)
self._resize()
return result
return self.moves.init_batch(docs)
if beam_width < 2:
return self.greedy_parse(docs, drop=drop)
else:
@ -259,7 +249,7 @@ cdef class Parser:
# This is pretty dirty, but the NER can resize itself in init_batch,
# if labels are missing. We therefore have to check whether we need to
# expand our model output.
self._resize()
self.model.resize_output(self.moves.n_moves)
model = self.model(docs)
weights = get_c_weights(model)
for state in batch:
@ -279,7 +269,7 @@ cdef class Parser:
# This is pretty dirty, but the NER can resize itself in init_batch,
# if labels are missing. We therefore have to check whether we need to
# expand our model output.
self._resize()
self.model.resize_output(self.moves.n_moves)
model = self.model(docs)
token_ids = numpy.zeros((len(docs) * beam_width, self.nr_feature),
dtype='i', order='C')
@ -453,7 +443,8 @@ cdef class Parser:
# This is pretty dirty, but the NER can resize itself in init_batch,
# if labels are missing. We therefore have to check whether we need to
# expand our model output.
self._resize()
self.model.resize_output(self.moves.n_moves)
self._rehearsal_model.resize_output(self.moves.n_moves)
# Prepare the stepwise model, and get the callback for finishing the batch
tutor, _ = self._rehearsal_model.begin_update(docs, drop=0.0)
model, finish_update = self.model.begin_update(docs, drop=0.0)
@ -594,7 +585,6 @@ cdef class Parser:
doc_sample = []
gold_sample = []
for raw_text, annots_brackets in islice(get_gold_tuples(), 1000):
_ = annots_brackets.pop()
for annots, brackets in annots_brackets:
ids, words, tags, heads, deps, ents = annots
doc_sample.append(Doc(self.vocab, words=words))

10
spacy/syntax/transition_system.pyx
View File

@ -63,13 +63,6 @@ cdef class TransitionSystem:
cdef Doc doc
beams = []
cdef int offset = 0
# Doc objects might contain labels that we need to register actions for. We need to check for that
# *before* we create any Beam objects, because the Beam object needs the correct number of
# actions. It's sort of dumb, but the best way is to just call init_batch() -- that triggers the additions,
# and it doesn't matter that we create and discard the state objects.
self.init_batch(docs)
for doc in docs:
beam = Beam(self.n_moves, beam_width, min_density=beam_density)
beam.initialize(self.init_beam_state, doc.length, doc.c)
@ -103,7 +96,8 @@ cdef class TransitionSystem:
def apply_transition(self, StateClass state, name):
if not self.is_valid(state, name):
raise ValueError(Errors.E170.format(name=name))
raise ValueError(
"Cannot apply transition {name}: invalid for the current state.".format(name=name))
action = self.lookup_transition(name)
action.do(state.c, action.label)

6
spacy/tests/conftest.py
View File

@ -185,12 +185,6 @@ def ru_tokenizer():
return get_lang_class("ru").Defaults.create_tokenizer()
@pytest.fixture
def ru_lemmatizer():
pytest.importorskip("pymorphy2")
return get_lang_class("ru").Defaults.create_lemmatizer()
@pytest.fixture(scope="session")
def sr_tokenizer():
return get_lang_class("sr").Defaults.create_tokenizer()

25
spacy/tests/doc/test_add_entities.py
View File

@ -1,11 +1,11 @@
# coding: utf-8
from __future__ import unicode_literals
from spacy.pipeline import EntityRecognizer
from spacy.tokens import Span
import pytest
from ...pipeline import EntityRecognizer
from ..util import get_doc
from ...tokens import Span
import pytest
def test_doc_add_entities_set_ents_iob(en_vocab):
@ -16,23 +16,10 @@ def test_doc_add_entities_set_ents_iob(en_vocab):
ner(doc)
assert len(list(doc.ents)) == 0
assert [w.ent_iob_ for w in doc] == (["O"] * len(doc))
doc.ents = [(doc.vocab.strings["ANIMAL"], 3, 4)]
assert [w.ent_iob_ for w in doc] == ["O", "O", "O", "B"]
assert [w.ent_iob_ for w in doc] == ["", "", "", "B"]
doc.ents = [(doc.vocab.strings["WORD"], 0, 2)]
assert [w.ent_iob_ for w in doc] == ["B", "I", "O", "O"]
def test_ents_reset(en_vocab):
text = ["This", "is", "a", "lion"]
doc = get_doc(en_vocab, text)
ner = EntityRecognizer(en_vocab)
ner.begin_training([])
ner(doc)
assert [t.ent_iob_ for t in doc] == (["O"] * len(doc))
doc.ents = list(doc.ents)
assert [t.ent_iob_ for t in doc] == (["O"] * len(doc))
assert [w.ent_iob_ for w in doc] == ["B", "I", "", ""]
def test_add_overlapping_entities(en_vocab):

4
spacy/tests/doc/test_creation.py
View File

@ -5,13 +5,11 @@ import pytest
from spacy.vocab import Vocab
from spacy.tokens import Doc
from spacy.lemmatizer import Lemmatizer
from spacy.lookups import Table
@pytest.fixture
def lemmatizer():
lookup = Table(data={"dogs": "dog", "boxen": "box", "mice": "mouse"})
return Lemmatizer(lookup=lookup)
return Lemmatizer(lookup={"dogs": "dog", "boxen": "box", "mice": "mouse"})
@pytest.fixture

33
spacy/tests/doc/test_morphanalysis.py
View File

@ -1,33 +0,0 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
@pytest.fixture
def i_has(en_tokenizer):
doc = en_tokenizer("I has")
doc[0].tag_ = "PRP"
doc[1].tag_ = "VBZ"
return doc
def test_token_morph_id(i_has):
assert i_has[0].morph.id
assert i_has[1].morph.id != 0
assert i_has[0].morph.id != i_has[1].morph.id
def test_morph_props(i_has):
assert i_has[0].morph.pron_type == i_has.vocab.strings["PronType_prs"]
assert i_has[0].morph.pron_type_ == "PronType_prs"
assert i_has[1].morph.pron_type == 0
def test_morph_iter(i_has):
assert list(i_has[0].morph) == ["PronType_prs"]
assert list(i_has[1].morph) == ["Number_sing", "Person_three", "VerbForm_fin"]
def test_morph_get(i_has):
assert i_has[0].morph.get("pron_type") == "PronType_prs"

7
spacy/tests/lang/ja/test_tokenizer.py
View File

@ -47,10 +47,3 @@ def test_ja_tokenizer_tags(ja_tokenizer, text, expected_tags):
def test_ja_tokenizer_pos(ja_tokenizer, text, expected_pos):
pos = [token.pos_ for token in ja_tokenizer(text)]
assert pos == expected_pos
def test_extra_spaces(ja_tokenizer):
# note: three spaces after "I"
tokens = ja_tokenizer("I like cheese.")
assert tokens[1].orth_ == " "
assert tokens[2].orth_ == " "

2
spacy/tests/lang/lt/test_lemmatizer.py
View File

@ -17,4 +17,4 @@ TEST_CASES = [
@pytest.mark.parametrize("tokens,lemmas", TEST_CASES)
def test_lt_lemmatizer(lt_lemmatizer, tokens, lemmas):
assert lemmas == [lt_lemmatizer.lookup_table.get(token, token) for token in tokens]
assert lemmas == [lt_lemmatizer.lookup(token) for token in tokens]

7
spacy/tests/lang/ru/test_lemmatizer.py
View File

@ -2,10 +2,17 @@
from __future__ import unicode_literals
import pytest
from spacy.lang.ru import Russian
from ...util import get_doc
@pytest.fixture
def ru_lemmatizer():
pytest.importorskip("pymorphy2")
return Russian.Defaults.create_lemmatizer()
def test_ru_doc_lemmatization(ru_tokenizer):
words = ["мама", "мыла", "раму"]
tags = [

0
spacy/tests/lang/sr/test_exceptions.py → spacy/tests/lang/sr/test_еxceptions.py
View File

8
spacy/tests/matcher/test_matcher_api.py
View File

@ -410,11 +410,3 @@ def test_matcher_schema_token_attributes(en_vocab, pattern, text):
assert len(matcher) == 1
matches = matcher(doc)
assert len(matches) == 1
def test_matcher_valid_callback(en_vocab):
"""Test that on_match can only be None or callable."""
matcher = Matcher(en_vocab)
with pytest.raises(ValueError):
matcher.add("TEST", [], [{"TEXT": "test"}])
matcher(Doc(en_vocab, words=["test"]))

89
spacy/tests/matcher/test_phrase_matcher.py
View File

@ -8,31 +8,10 @@ from ..util import get_doc
def test_matcher_phrase_matcher(en_vocab):
doc = Doc(en_vocab, words=["Google", "Now"])
matcher = PhraseMatcher(en_vocab)
matcher.add("COMPANY", None, doc)
doc = Doc(en_vocab, words=["I", "like", "Google", "Now", "best"])
# intermediate phrase
pattern = Doc(en_vocab, words=["Google", "Now"])
matcher = PhraseMatcher(en_vocab)
matcher.add("COMPANY", None, pattern)
assert len(matcher(doc)) == 1
# initial token
pattern = Doc(en_vocab, words=["I"])
matcher = PhraseMatcher(en_vocab)
matcher.add("I", None, pattern)
assert len(matcher(doc)) == 1
# initial phrase
pattern = Doc(en_vocab, words=["I", "like"])
matcher = PhraseMatcher(en_vocab)
matcher.add("ILIKE", None, pattern)
assert len(matcher(doc)) == 1
# final token
pattern = Doc(en_vocab, words=["best"])
matcher = PhraseMatcher(en_vocab)
matcher.add("BEST", None, pattern)
assert len(matcher(doc)) == 1
# final phrase
pattern = Doc(en_vocab, words=["Now", "best"])
matcher = PhraseMatcher(en_vocab)
matcher.add("NOWBEST", None, pattern)
assert len(matcher(doc)) == 1
@ -52,68 +31,6 @@ def test_phrase_matcher_contains(en_vocab):
assert "TEST2" not in matcher
def test_phrase_matcher_repeated_add(en_vocab):
matcher = PhraseMatcher(en_vocab)
# match ID only gets added once
matcher.add("TEST", None, Doc(en_vocab, words=["like"]))
matcher.add("TEST", None, Doc(en_vocab, words=["like"]))
matcher.add("TEST", None, Doc(en_vocab, words=["like"]))
matcher.add("TEST", None, Doc(en_vocab, words=["like"]))
doc = Doc(en_vocab, words=["I", "like", "Google", "Now", "best"])
assert "TEST" in matcher
assert "TEST2" not in matcher
assert len(matcher(doc)) == 1
def test_phrase_matcher_remove(en_vocab):
matcher = PhraseMatcher(en_vocab)
matcher.add("TEST1", None, Doc(en_vocab, words=["like"]))
matcher.add("TEST2", None, Doc(en_vocab, words=["best"]))
doc = Doc(en_vocab, words=["I", "like", "Google", "Now", "best"])
assert "TEST1" in matcher
assert "TEST2" in matcher
assert "TEST3" not in matcher
assert len(matcher(doc)) == 2
matcher.remove("TEST1")
assert "TEST1" not in matcher
assert "TEST2" in matcher
assert "TEST3" not in matcher
assert len(matcher(doc)) == 1
matcher.remove("TEST2")
assert "TEST1" not in matcher
assert "TEST2" not in matcher
assert "TEST3" not in matcher
assert len(matcher(doc)) == 0
with pytest.raises(KeyError):
matcher.remove("TEST3")
assert "TEST1" not in matcher
assert "TEST2" not in matcher
assert "TEST3" not in matcher
assert len(matcher(doc)) == 0
def test_phrase_matcher_overlapping_with_remove(en_vocab):
matcher = PhraseMatcher(en_vocab)
matcher.add("TEST", None, Doc(en_vocab, words=["like"]))
# TEST2 is added alongside TEST
matcher.add("TEST2", None, Doc(en_vocab, words=["like"]))
doc = Doc(en_vocab, words=["I", "like", "Google", "Now", "best"])
assert "TEST" in matcher
assert len(matcher) == 2
assert len(matcher(doc)) == 2
# removing TEST does not remove the entry for TEST2
matcher.remove("TEST")
assert "TEST" not in matcher
assert len(matcher) == 1
assert len(matcher(doc)) == 1
assert matcher(doc)[0][0] == en_vocab.strings["TEST2"]
# removing TEST2 removes all
matcher.remove("TEST2")
assert "TEST2" not in matcher
assert len(matcher) == 0
assert len(matcher(doc)) == 0
def test_phrase_matcher_string_attrs(en_vocab):
words1 = ["I", "like", "cats"]
pos1 = ["PRON", "VERB", "NOUN"]

0
spacy/tests/morphology/__init__.py
View File

48
spacy/tests/morphology/test_morph_features.py
View File

@ -1,48 +0,0 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
from spacy.morphology import Morphology
from spacy.strings import StringStore, get_string_id
from spacy.lemmatizer import Lemmatizer
@pytest.fixture
def morphology():
return Morphology(StringStore(), {}, Lemmatizer())
def test_init(morphology):
pass
def test_add_morphology_with_string_names(morphology):
morphology.add({"Case_gen", "Number_sing"})
def test_add_morphology_with_int_ids(morphology):
morphology.add({get_string_id("Case_gen"), get_string_id("Number_sing")})
def test_add_morphology_with_mix_strings_and_ints(morphology):
morphology.add({get_string_id("PunctSide_ini"), "VerbType_aux"})
def test_morphology_tags_hash_distinctly(morphology):
tag1 = morphology.add({"PunctSide_ini", "VerbType_aux"})
tag2 = morphology.add({"Case_gen", "Number_sing"})
assert tag1 != tag2
def test_morphology_tags_hash_independent_of_order(morphology):
tag1 = morphology.add({"Case_gen", "Number_sing"})
tag2 = morphology.add({"Number_sing", "Case_gen"})
assert tag1 == tag2
def test_update_morphology_tag(morphology):
tag1 = morphology.add({"Case_gen"})
tag2 = morphology.update(tag1, {"Number_sing"})
assert tag1 != tag2
tag3 = morphology.add({"Number_sing", "Case_gen"})
assert tag2 == tag3

202
spacy/tests/parser/test_ner.py
View File

@ -2,9 +2,7 @@
from __future__ import unicode_literals
import pytest
from spacy.lang.en import English
from spacy.pipeline import EntityRecognizer, EntityRuler
from spacy.pipeline import EntityRecognizer
from spacy.vocab import Vocab
from spacy.syntax.ner import BiluoPushDown
from spacy.gold import GoldParse
@ -82,190 +80,14 @@ def test_get_oracle_moves_negative_O(tsys, vocab):
assert names
def test_oracle_moves_missing_B(en_vocab):
words = ["B", "52", "Bomber"]
biluo_tags = [None, None, "L-PRODUCT"]
doc = Doc(en_vocab, words=words)
gold = GoldParse(doc, words=words, entities=biluo_tags)
moves = BiluoPushDown(en_vocab.strings)
move_types = ("M", "B", "I", "L", "U", "O")
for tag in biluo_tags:
if tag is None:
continue
elif tag == "O":
moves.add_action(move_types.index("O"), "")
else:
action, label = tag.split("-")
moves.add_action(move_types.index("B"), label)
moves.add_action(move_types.index("I"), label)
moves.add_action(move_types.index("L"), label)
moves.add_action(move_types.index("U"), label)
moves.preprocess_gold(gold)
seq = moves.get_oracle_sequence(doc, gold)
def test_oracle_moves_whitespace(en_vocab):
words = ["production", "\n", "of", "Northrop", "\n", "Corp.", "\n", "'s", "radar"]
biluo_tags = ["O", "O", "O", "B-ORG", None, "I-ORG", "L-ORG", "O", "O"]
doc = Doc(en_vocab, words=words)
gold = GoldParse(doc, words=words, entities=biluo_tags)
moves = BiluoPushDown(en_vocab.strings)
move_types = ("M", "B", "I", "L", "U", "O")
for tag in biluo_tags:
if tag is None:
continue
elif tag == "O":
moves.add_action(move_types.index("O"), "")
else:
action, label = tag.split("-")
moves.add_action(move_types.index(action), label)
moves.preprocess_gold(gold)
moves.get_oracle_sequence(doc, gold)
def test_accept_blocked_token():
"""Test succesful blocking of tokens to be in an entity."""
# 1. test normal behaviour
nlp1 = English()
doc1 = nlp1("I live in New York")
ner1 = EntityRecognizer(doc1.vocab)
assert [token.ent_iob_ for token in doc1] == ["", "", "", "", ""]
assert [token.ent_type_ for token in doc1] == ["", "", "", "", ""]
# Add the OUT action
ner1.moves.add_action(5, "")
ner1.add_label("GPE")
# Get into the state just before "New"
state1 = ner1.moves.init_batch([doc1])[0]
ner1.moves.apply_transition(state1, "O")
ner1.moves.apply_transition(state1, "O")
ner1.moves.apply_transition(state1, "O")
# Check that B-GPE is valid.
assert ner1.moves.is_valid(state1, "B-GPE")
# 2. test blocking behaviour
nlp2 = English()
doc2 = nlp2("I live in New York")
ner2 = EntityRecognizer(doc2.vocab)
# set "New York" to a blocked entity
doc2.ents = [(0, 3, 5)]
assert [token.ent_iob_ for token in doc2] == ["", "", "", "B", "B"]
assert [token.ent_type_ for token in doc2] == ["", "", "", "", ""]
# Check that B-GPE is now invalid.
ner2.moves.add_action(4, "")
ner2.moves.add_action(5, "")
ner2.add_label("GPE")
state2 = ner2.moves.init_batch([doc2])[0]
ner2.moves.apply_transition(state2, "O")
ner2.moves.apply_transition(state2, "O")
ner2.moves.apply_transition(state2, "O")
# we can only use U- for "New"
assert not ner2.moves.is_valid(state2, "B-GPE")
assert ner2.moves.is_valid(state2, "U-")
ner2.moves.apply_transition(state2, "U-")
# we can only use U- for "York"
assert not ner2.moves.is_valid(state2, "B-GPE")
assert ner2.moves.is_valid(state2, "U-")
def test_overwrite_token():
nlp = English()
ner1 = nlp.create_pipe("ner")
nlp.add_pipe(ner1, name="ner")
nlp.begin_training()
# The untrained NER will predict O for each token
doc = nlp("I live in New York")
assert [token.ent_iob_ for token in doc] == ["O", "O", "O", "O", "O"]
assert [token.ent_type_ for token in doc] == ["", "", "", "", ""]
# Check that a new ner can overwrite O
ner2 = EntityRecognizer(doc.vocab)
ner2.moves.add_action(5, "")
ner2.add_label("GPE")
state = ner2.moves.init_batch([doc])[0]
assert ner2.moves.is_valid(state, "B-GPE")
assert ner2.moves.is_valid(state, "U-GPE")
ner2.moves.apply_transition(state, "B-GPE")
assert ner2.moves.is_valid(state, "I-GPE")
assert ner2.moves.is_valid(state, "L-GPE")
def test_ruler_before_ner():
""" Test that an NER works after an entity_ruler: the second can add annotations """
nlp = English()
# 1 : Entity Ruler - should set "this" to B and everything else to empty
ruler = EntityRuler(nlp)
patterns = [{"label": "THING", "pattern": "This"}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
# 2: untrained NER - should set everything else to O
untrained_ner = nlp.create_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.add_pipe(untrained_ner)
nlp.begin_training()
doc = nlp("This is Antti Korhonen speaking in Finland")
expected_iobs = ["B", "O", "O", "O", "O", "O", "O"]
expected_types = ["THING", "", "", "", "", "", ""]
assert [token.ent_iob_ for token in doc] == expected_iobs
assert [token.ent_type_ for token in doc] == expected_types
def test_ner_before_ruler():
""" Test that an entity_ruler works after an NER: the second can overwrite O annotations """
nlp = English()
# 1: untrained NER - should set everything to O
untrained_ner = nlp.create_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.add_pipe(untrained_ner, name="uner")
nlp.begin_training()
# 2 : Entity Ruler - should set "this" to B and keep everything else O
ruler = EntityRuler(nlp)
patterns = [{"label": "THING", "pattern": "This"}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
doc = nlp("This is Antti Korhonen speaking in Finland")
expected_iobs = ["B", "O", "O", "O", "O", "O", "O"]
expected_types = ["THING", "", "", "", "", "", ""]
assert [token.ent_iob_ for token in doc] == expected_iobs
assert [token.ent_type_ for token in doc] == expected_types
def test_block_ner():
""" Test functionality for blocking tokens so they can't be in a named entity """
# block "Antti L Korhonen" from being a named entity
nlp = English()
nlp.add_pipe(BlockerComponent1(2, 5))
untrained_ner = nlp.create_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.add_pipe(untrained_ner, name="uner")
nlp.begin_training()
doc = nlp("This is Antti L Korhonen speaking in Finland")
expected_iobs = ["O", "O", "B", "B", "B", "O", "O", "O"]
expected_types = ["", "", "", "", "", "", "", ""]
assert [token.ent_iob_ for token in doc] == expected_iobs
assert [token.ent_type_ for token in doc] == expected_types
class BlockerComponent1(object):
name = "my_blocker"
def __init__(self, start, end):
self.start = start
self.end = end
def __call__(self, doc):
doc.ents = [(0, self.start, self.end)]
return doc
def test_doc_add_entities_set_ents_iob(en_vocab):
doc = Doc(en_vocab, words=["This", "is", "a", "lion"])
ner = EntityRecognizer(en_vocab)
ner.begin_training([])
ner(doc)
assert len(list(doc.ents)) == 0
assert [w.ent_iob_ for w in doc] == (["O"] * len(doc))
doc.ents = [(doc.vocab.strings["ANIMAL"], 3, 4)]
assert [w.ent_iob_ for w in doc] == ["", "", "", "B"]
doc.ents = [(doc.vocab.strings["WORD"], 0, 2)]
assert [w.ent_iob_ for w in doc] == ["B", "I", "", ""]

2
spacy/tests/regression/test_issue1-1000.py
View File

@ -426,7 +426,7 @@ def test_issue957(en_tokenizer):
def test_issue999(train_data):
"""Test that adding entities and resuming training works passably OK.
There are two issues here:
1) We have to read labels. This isn't very nice.
1) We have to readd labels. This isn't very nice.
2) There's no way to set the learning rate for the weight update, so we
end up out-of-scale, causing it to learn too fast.
"""

2
spacy/tests/regression/test_issue1501-2000.py
View File

@ -187,7 +187,7 @@ def test_issue1799():
def test_issue1807():
"""Test vocab.set_vector also adds the word to the vocab."""
vocab = Vocab(vectors_name="test_issue1807")
vocab = Vocab()
assert "hello" not in vocab
vocab.set_vector("hello", numpy.ones((50,), dtype="f"))
assert "hello" in vocab

2
spacy/tests/regression/test_issue2501-3000.py
View File

@ -184,7 +184,7 @@ def test_issue2833(en_vocab):
def test_issue2871():
"""Test that vectors recover the correct key for spaCy reserved words."""
words = ["dog", "cat", "SUFFIX"]
vocab = Vocab(vectors_name="test_issue2871")
vocab = Vocab()
vocab.vectors.resize(shape=(3, 10))
vector_data = numpy.zeros((3, 10), dtype="f")
for word in words:

6
spacy/tests/regression/test_issue3001-3500.py
View File

@ -30,20 +30,20 @@ def test_issue3002():
def test_issue3009(en_vocab):
"""Test problem with matcher quantifiers"""
patterns = [
[{"LEMMA": "have"}, {"LOWER": "to"}, {"LOWER": "do"}, {"TAG": "IN"}],
[{"LEMMA": "have"}, {"LOWER": "to"}, {"LOWER": "do"}, {"POS": "ADP"}],
[
{"LEMMA": "have"},
{"IS_ASCII": True, "IS_PUNCT": False, "OP": "*"},
{"LOWER": "to"},
{"LOWER": "do"},
{"TAG": "IN"},
{"POS": "ADP"},
],
[
{"LEMMA": "have"},
{"IS_ASCII": True, "IS_PUNCT": False, "OP": "?"},
{"LOWER": "to"},
{"LOWER": "do"},
{"TAG": "IN"},
{"POS": "ADP"},
],
]
words = ["also", "has", "to", "do", "with"]

82
spacy/tests/regression/test_issue4042.py
View File

@ -1,82 +0,0 @@
# coding: utf8
from __future__ import unicode_literals
import spacy
from spacy.pipeline import EntityRecognizer, EntityRuler
from spacy.lang.en import English
from spacy.tokens import Span
from spacy.util import ensure_path
from ..util import make_tempdir
def test_issue4042():
"""Test that serialization of an EntityRuler before NER works fine."""
nlp = English()
# add ner pipe
ner = nlp.create_pipe("ner")
ner.add_label("SOME_LABEL")
nlp.add_pipe(ner)
nlp.begin_training()
# Add entity ruler
ruler = EntityRuler(nlp)
patterns = [
{"label": "MY_ORG", "pattern": "Apple"},
{"label": "MY_GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]},
]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler, before="ner") # works fine with "after"
doc1 = nlp("What do you think about Apple ?")
assert doc1.ents[0].label_ == "MY_ORG"
with make_tempdir() as d:
output_dir = ensure_path(d)
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
nlp2 = spacy.load(output_dir)
doc2 = nlp2("What do you think about Apple ?")
assert doc2.ents[0].label_ == "MY_ORG"
def test_issue4042_bug2():
"""
Test that serialization of an NER works fine when new labels were added.
This is the second bug of two bugs underlying the issue 4042.
"""
nlp1 = English()
vocab = nlp1.vocab
# add ner pipe
ner1 = nlp1.create_pipe("ner")
ner1.add_label("SOME_LABEL")
nlp1.add_pipe(ner1)
nlp1.begin_training()
# add a new label to the doc
doc1 = nlp1("What do you think about Apple ?")
assert len(ner1.labels) == 1
assert "SOME_LABEL" in ner1.labels
apple_ent = Span(doc1, 5, 6, label="MY_ORG")
doc1.ents = list(doc1.ents) + [apple_ent]
# reapply the NER - at this point it should resize itself
ner1(doc1)
assert len(ner1.labels) == 2
assert "SOME_LABEL" in ner1.labels
assert "MY_ORG" in ner1.labels
with make_tempdir() as d:
# assert IO goes fine
output_dir = ensure_path(d)
if not output_dir.exists():
output_dir.mkdir()
ner1.to_disk(output_dir)
nlp2 = English(vocab)
ner2 = EntityRecognizer(vocab)
ner2.from_disk(output_dir)
assert len(ner2.labels) == 2

4
spacy/tests/regression/test_issue4054.py
View File

@ -2,12 +2,12 @@
from __future__ import unicode_literals
from spacy.vocab import Vocab
import spacy
from spacy.lang.en import English
from spacy.tests.util import make_tempdir
from spacy.util import ensure_path
from ..util import make_tempdir
def test_issue4054(en_vocab):
"""Test that a new blank model can be made with a vocab from file,

42
spacy/tests/regression/test_issue4267.py
View File

@ -1,42 +0,0 @@
# coding: utf8
from __future__ import unicode_literals
import pytest
import spacy
from spacy.lang.en import English
from spacy.pipeline import EntityRuler
from spacy.tokens import Span
def test_issue4267():
""" Test that running an entity_ruler after ner gives consistent results"""
nlp = English()
ner = nlp.create_pipe("ner")
ner.add_label("PEOPLE")
nlp.add_pipe(ner)
nlp.begin_training()
assert "ner" in nlp.pipe_names
# assert that we have correct IOB annotations
doc1 = nlp("hi")
assert doc1.is_nered
for token in doc1:
assert token.ent_iob == 2
# add entity ruler and run again
ruler = EntityRuler(nlp)
patterns = [{"label": "SOFTWARE", "pattern": "spacy"}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
assert "entity_ruler" in nlp.pipe_names
assert "ner" in nlp.pipe_names
# assert that we still have correct IOB annotations
doc2 = nlp("hi")
assert doc2.is_nered
for token in doc2:
assert token.ent_iob == 2

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