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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 | Arvind Srinivasan |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2020-06-13 |
| GitHub username | arvindcheenu |
| 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:
* [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 | Jannis Rauschke |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 22.05.2020 |
| GitHub username | JannisTriesToCode |
| Website (optional) | https://twitter.com/JRauschke |

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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 | Hiroshi Matsuda |
| Company name (if applicable) | Megagon Labs, Tokyo |
| Title or role (if applicable) | Research Scientist |
| Date | June 6, 2020 |
| GitHub username | hiroshi-matsuda-rit |
| 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:
* [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 | Jones Martins |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2020-06-10 |
| GitHub username | jonesmartins |
| 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:
* [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 | Leonardo M. Rocha |
| Company name (if applicable) | |
| Title or role (if applicable) | Eng. |
| Date | 31/05/2020 |
| GitHub username | leomrocha |
| 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 | Philipp Sodmann |
| Company name (if applicable) | Empolis |
| Title or role (if applicable) | |
| Date | 2017-05-06 |
| GitHub username | theudas |
| Website (optional) | |

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name: Issue Manager
on:
schedule:
- cron: "0 0 * * *"
issue_comment:
types:
- created
- edited
issues:
types:
- labeled
jobs:
issue-manager:
runs-on: ubuntu-latest
steps:
- uses: tiangolo/issue-manager@0.2.1
with:
token: ${{ secrets.GITHUB_TOKEN }}
config: >
{
"resolved": {
"delay": "P7D",
"message": "This issue has been automatically closed because it was answered and there was no follow-up discussion.",
"remove_label_on_comment": true,
"remove_label_on_close": true
}
}

5
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@ -5,8 +5,9 @@ VENV := ./env$(PYVER)
version := $(shell "bin/get-version.sh")
dist/spacy-$(version).pex : wheelhouse/spacy-$(version).stamp
$(VENV)/bin/pex -f ./wheelhouse --no-index --disable-cache -m spacy -o $@ spacy==$(version) jsonschema spacy_lookups_data
$(VENV)/bin/pex -f ./wheelhouse --no-index --disable-cache -m spacy -o $@ spacy==$(version) jsonschema spacy-lookups-data jieba pkuseg==0.0.22 sudachipy sudachidict_core
chmod a+rx $@
cp $@ dist/spacy.pex
dist/pytest.pex : wheelhouse/pytest-*.whl
$(VENV)/bin/pex -f ./wheelhouse --no-index --disable-cache -m pytest -o $@ pytest pytest-timeout mock
@ -14,7 +15,7 @@ dist/pytest.pex : wheelhouse/pytest-*.whl
wheelhouse/spacy-$(version).stamp : $(VENV)/bin/pex setup.py spacy/*.py* spacy/*/*.py*
$(VENV)/bin/pip wheel . -w ./wheelhouse
$(VENV)/bin/pip wheel jsonschema spacy_lookups_data -w ./wheelhouse
$(VENV)/bin/pip wheel jsonschema spacy-lookups-data jieba pkuseg==0.0.22 sudachipy sudachidict_core -w ./wheelhouse
touch $@
wheelhouse/pytest-%.whl : $(VENV)/bin/pex

2
examples/training/pretrain_textcat.py
View File

@ -187,7 +187,7 @@ def evaluate_textcat(tokenizer, textcat, texts, cats):
width=("Width of CNN layers", "positional", None, int),
embed_size=("Embedding rows", "positional", None, int),
pretrain_iters=("Number of iterations to pretrain", "option", "pn", int),
train_iters=("Number of iterations to pretrain", "option", "tn", int),
train_iters=("Number of iterations to train", "option", "tn", int),
train_examples=("Number of labelled examples", "option", "eg", int),
vectors_model=("Name or path to vectors model to learn from"),
)

2
examples/training/train_intent_parser.py
View File

@ -2,7 +2,7 @@
# coding: utf-8
"""Using the parser to recognise your own semantics
spaCy's parser component can be used to trained to predict any type of tree
spaCy's parser component can be trained to predict any type of tree
structure over your input text. You can also predict trees over whole documents
or chat logs, with connections between the sentence-roots used to annotate
discourse structure. In this example, we'll build a message parser for a common

2
pyproject.toml
View File

@ -6,6 +6,6 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc==7.4.0",
"thinc==7.4.1",
]
build-backend = "setuptools.build_meta"

2
requirements.txt
View File

@ -1,7 +1,7 @@
# Our libraries
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc==7.4.0
thinc==7.4.1
blis>=0.4.0,<0.5.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.4.0,<1.1.0

9
setup.cfg
View File

@ -38,13 +38,13 @@ setup_requires =
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc==7.4.0
thinc==7.4.1
install_requires =
# Our libraries
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc==7.4.0
thinc==7.4.1
blis>=0.4.0,<0.5.0
wasabi>=0.4.0,<1.1.0
srsly>=1.0.2,<1.1.0
@ -59,7 +59,7 @@ install_requires =
[options.extras_require]
lookups =
spacy_lookups_data>=0.3.1,<0.4.0
spacy_lookups_data>=0.3.2,<0.4.0
cuda =
cupy>=5.0.0b4,<9.0.0
cuda80 =
@ -78,7 +78,8 @@ cuda102 =
cupy-cuda102>=5.0.0b4,<9.0.0
# Language tokenizers with external dependencies
ja =
fugashi>=0.1.3
sudachipy>=0.4.5
sudachidict_core>=20200330
ko =
natto-py==0.9.0
th =

2
spacy/about.py
View File

@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy"
__version__ = "2.2.4"
__version__ = "2.3.0.dev1"
__release__ = True
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

9
spacy/cli/train.py
View File

@ -15,6 +15,7 @@ import random
from .._ml import create_default_optimizer
from ..util import use_gpu as set_gpu
from ..errors import Errors
from ..gold import GoldCorpus
from ..compat import path2str
from ..lookups import Lookups
@ -182,6 +183,7 @@ def train(
msg.warn("Unable to activate GPU: {}".format(use_gpu))
msg.text("Using CPU only")
use_gpu = -1
base_components = []
if base_model:
msg.text("Starting with base model '{}'".format(base_model))
nlp = util.load_model(base_model)
@ -227,6 +229,7 @@ def train(
exits=1,
)
msg.text("Extending component from base model '{}'".format(pipe))
base_components.append(pipe)
disabled_pipes = nlp.disable_pipes(
[p for p in nlp.pipe_names if p not in pipeline]
)
@ -299,7 +302,7 @@ def train(
# Load in pretrained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
components = _load_pretrained_tok2vec(nlp, init_tok2vec, base_components)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
# Verify textcat config
@ -642,7 +645,7 @@ def _load_vectors(nlp, vectors):
util.load_model(vectors, vocab=nlp.vocab)
def _load_pretrained_tok2vec(nlp, loc):
def _load_pretrained_tok2vec(nlp, loc, base_components):
"""Load pretrained weights for the 'token-to-vector' part of the component
models, which is typically a CNN. See 'spacy pretrain'. Experimental.
"""
@ -651,6 +654,8 @@ def _load_pretrained_tok2vec(nlp, loc):
loaded = []
for name, component in nlp.pipeline:
if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
if name in base_components:
raise ValueError(Errors.E200.format(component=name))
component.tok2vec.from_bytes(weights_data)
loaded.append(name)
return loaded

27
spacy/errors.py
View File

@ -92,9 +92,9 @@ class Warnings(object):
W022 = ("Training a new part-of-speech tagger using a model with no "
"lemmatization rules or data. This means that the trained model "
"may not be able to lemmatize correctly. If this is intentional "
"or the language you're using doesn't have lemmatization data. "
"If this is surprising, make sure you have the spacy-lookups-data "
"package installed.")
"or the language you're using doesn't have lemmatization data, "
"please ignore this warning. If this is surprising, make sure you "
"have the spacy-lookups-data package installed.")
W023 = ("Multiprocessing of Language.pipe is not supported in Python 2. "
"'n_process' will be set to 1.")
W024 = ("Entity '{entity}' - Alias '{alias}' combination already exists in "
@ -115,6 +115,25 @@ class Warnings(object):
"`spacy.gold.biluo_tags_from_offsets(nlp.make_doc(text), entities)`"
" to check the alignment. Misaligned entities ('-') will be "
"ignored during training.")
W031 = ("Model '{model}' ({model_version}) requires spaCy {version} and "
"is incompatible with the current spaCy version ({current}). This "
"may lead to unexpected results or runtime errors. To resolve "
"this, download a newer compatible model or retrain your custom "
"model with the current spaCy version. For more details and "
"available updates, run: python -m spacy validate")
W032 = ("Unable to determine model compatibility for model '{model}' "
"({model_version}) with the current spaCy version ({current}). "
"This may lead to unexpected results or runtime errors. To resolve "
"this, download a newer compatible model or retrain your custom "
"model with the current spaCy version. For more details and "
"available updates, run: python -m spacy validate")
W033 = ("Training a new {model} using a model with no lexeme normalization "
"table. This may degrade the performance of the model to some "
"degree. If this is intentional or the language you're using "
"doesn't have a normalization table, please ignore this warning. "
"If this is surprising, make sure you have the spacy-lookups-data "
"package installed. The languages with lexeme normalization tables "
"are currently: da, de, el, en, id, lb, pt, ru, sr, ta, th.")
@add_codes
@ -568,6 +587,8 @@ class Errors(object):
E198 = ("Unable to return {n} most similar vectors for the current vectors "
"table, which contains {n_rows} vectors.")
E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
E200 = ("Specifying a base model with a pretrained component '{component}' "
"can not be combined with adding a pretrained Tok2Vec layer.")
@add_codes

1
spacy/gold.pyx
View File

@ -640,6 +640,7 @@ cdef class GoldParse:
representing the external IDs in a knowledge base (KB)
mapped to either 1.0 or 0.0, indicating positive and
negative examples respectively.
make_projective (bool): Whether to projectivize the dependency tree.
RETURNS (GoldParse): The newly constructed object.
"""
self.mem = Pool()

10
spacy/lang/en/tokenizer_exceptions.py
View File

@ -139,7 +139,7 @@ for pron in ["he", "she", "it"]:
# W-words, relative pronouns, prepositions etc.
for word in ["who", "what", "when", "where", "why", "how", "there", "that"]:
for word in ["who", "what", "when", "where", "why", "how", "there", "that", "this", "these", "those"]:
for orth in [word, word.title()]:
_exc[orth + "'s"] = [
{ORTH: orth, LEMMA: word, NORM: word},
@ -399,6 +399,14 @@ _other_exc = {
{ORTH: "Let", LEMMA: "let", NORM: "let"},
{ORTH: "'s", LEMMA: PRON_LEMMA, NORM: "us"},
],
"c'mon": [
{ORTH: "c'm", NORM: "come", LEMMA: "come"},
{ORTH: "on"}
],
"C'mon": [
{ORTH: "C'm", NORM: "come", LEMMA: "come"},
{ORTH: "on"}
]
}
_exc.update(_other_exc)

6
spacy/lang/es/examples.py
View File

@ -18,5 +18,9 @@ sentences = [
"El gato come pescado.",
"Veo al hombre con el telescopio.",
"La araña come moscas.",
"El pingüino incuba en su nido.",
"El pingüino incuba en su nido sobre el hielo.",
"¿Dónde estais?",
"¿Quién es el presidente Francés?",
"¿Dónde está encuentra la capital de Argentina?",
"¿Cuándo nació José de San Martín?",
]

17
spacy/lang/es/tokenizer_exceptions.py
View File

@ -4,15 +4,16 @@ from __future__ import unicode_literals
from ...symbols import ORTH, LEMMA, NORM, PRON_LEMMA
_exc = {
"pal": [{ORTH: "pa", LEMMA: "para"}, {ORTH: "l", LEMMA: "el", NORM: "el"}],
"pala": [{ORTH: "pa", LEMMA: "para"}, {ORTH: "la", LEMMA: "la", NORM: "la"}],
}
_exc = {}
for exc_data in [
{ORTH: "n°", LEMMA: "número"},
{ORTH: "°C", LEMMA: "grados Celcius"},
{ORTH: "aprox.", LEMMA: "aproximadamente"},
{ORTH: "dna.", LEMMA: "docena"},
{ORTH: "dpto.", LEMMA: "departamento"},
{ORTH: "ej.", LEMMA: "ejemplo"},
{ORTH: "esq.", LEMMA: "esquina"},
{ORTH: "pág.", LEMMA: "página"},
{ORTH: "p.ej.", LEMMA: "por ejemplo"},
@ -20,6 +21,8 @@ for exc_data in [
{ORTH: "Vd.", LEMMA: PRON_LEMMA, NORM: "usted"},
{ORTH: "Uds.", LEMMA: PRON_LEMMA, NORM: "ustedes"},
{ORTH: "Vds.", LEMMA: PRON_LEMMA, NORM: "ustedes"},
{ORTH: "vol.", NORM: "volúmen"},
]:
_exc[exc_data[ORTH]] = [exc_data]
@ -39,10 +42,14 @@ for h in range(1, 12 + 1):
for orth in [
"a.C.",
"a.J.C.",
"d.C.",
"d.J.C.",
"apdo.",
"Av.",
"Avda.",
"Cía.",
"Dr.",
"Dra.",
"EE.UU.",
"etc.",
"fig.",
@ -58,8 +65,10 @@ for orth in [
"Prof.",
"Profa.",
"q.e.p.d.",
"Q.E.P.D."
"S.A.",
"S.L.",
"S.R.L."
"s.s.s.",
"Sr.",
"Sra.",

65
spacy/lang/fr/_tokenizer_exceptions_list.py
View File

@ -534,7 +534,6 @@ FR_BASE_EXCEPTIONS = [
"Beaumont-Hamel",
"Beaumont-Louestault",
"Beaumont-Monteux",
"Beaumont-Pied-de-Bœuf",
"Beaumont-Pied-de-Bœuf",
"Beaumont-Sardolles",
"Beaumont-Village",
@ -951,7 +950,7 @@ FR_BASE_EXCEPTIONS = [
"Buxières-sous-les-Côtes",
"Buzy-Darmont",
"Byhleguhre-Byhlen",
"Bœurs-en-Othe",
"Bœurs-en-Othe",
"Bâle-Campagne",
"Bâle-Ville",
"Béard-Géovreissiat",
@ -1589,11 +1588,11 @@ FR_BASE_EXCEPTIONS = [
"Cruci-Falgardiens",
"Cruquius-Oost",
"Cruviers-Lascours",
"Crèvecœur-en-Auge",
"Crèvecœur-en-Brie",
"Crèvecœur-le-Grand",
"Crèvecœur-le-Petit",
"Crèvecœur-sur-l'Escaut",
"Crèvecœur-en-Auge",
"Crèvecœur-en-Brie",
"Crèvecœur-le-Grand",
"Crèvecœur-le-Petit",
"Crèvecœur-sur-l'Escaut",
"Crécy-Couvé",
"Créon-d'Armagnac",
"Cubjac-Auvézère-Val-d'Ans",
@ -1619,7 +1618,7 @@ FR_BASE_EXCEPTIONS = [
"Cuxac-Cabardès",
"Cuxac-d'Aude",
"Cuyk-Sainte-Agathe",
"Cœuvres-et-Valsery",
"Cœuvres-et-Valsery",
"Céaux-d'Allègre",
"Céleste-Empire",
"Cénac-et-Saint-Julien",
@ -1682,7 +1681,7 @@ FR_BASE_EXCEPTIONS = [
"Devrai-Gondragnières",
"Dhuys et Morin-en-Brie",
"Diane-Capelle",
"Dieffenbach-lès-Wœrth",
"Dieffenbach-lès-Wœrth",
"Diekhusen-Fahrstedt",
"Diennes-Aubigny",
"Diensdorf-Radlow",
@ -1755,7 +1754,7 @@ FR_BASE_EXCEPTIONS = [
"Durdat-Larequille",
"Durfort-Lacapelette",
"Durfort-et-Saint-Martin-de-Sossenac",
"Dœuil-sur-le-Mignon",
"Dœuil-sur-le-Mignon",
"Dão-Lafões",
"Débats-Rivière-d'Orpra",
"Décines-Charpieu",
@ -2690,8 +2689,8 @@ FR_BASE_EXCEPTIONS = [
"Kuhlen-Wendorf",
"KwaZulu-Natal",
"Kyzyl-Arvat",
"Kœur-la-Grande",
"Kœur-la-Petite",
"Kœur-la-Grande",
"Kœur-la-Petite",
"Kölln-Reisiek",
"Königsbach-Stein",
"Königshain-Wiederau",
@ -4027,7 +4026,7 @@ FR_BASE_EXCEPTIONS = [
"Marcilly-d'Azergues",
"Marcillé-Raoul",
"Marcillé-Robert",
"Marcq-en-Barœul",
"Marcq-en-Barœul",
"Marcy-l'Etoile",
"Marcy-l'Étoile",
"Mareil-Marly",
@ -4261,7 +4260,7 @@ FR_BASE_EXCEPTIONS = [
"Monlezun-d'Armagnac",
"Monléon-Magnoac",
"Monnetier-Mornex",
"Mons-en-Barœul",
"Mons-en-Barœul",
"Monsempron-Libos",
"Monsteroux-Milieu",
"Montacher-Villegardin",
@ -4351,7 +4350,7 @@ FR_BASE_EXCEPTIONS = [
"Mornay-Berry",
"Mortain-Bocage",
"Morteaux-Couliboeuf",
"Morteaux-Coulibœuf",
"Morteaux-Coulibœuf",
"Morteaux-Coulibœuf",
"Mortes-Frontières",
"Mory-Montcrux",
@ -4394,7 +4393,7 @@ FR_BASE_EXCEPTIONS = [
"Muncq-Nieurlet",
"Murtin-Bogny",
"Murtin-et-le-Châtelet",
"Mœurs-Verdey",
"Mœurs-Verdey",
"Ménestérol-Montignac",
"Ménil'muche",
"Ménil-Annelles",
@ -4615,7 +4614,7 @@ FR_BASE_EXCEPTIONS = [
"Neuves-Maisons",
"Neuvic-Entier",
"Neuvicq-Montguyon",
"Neuville-lès-Lœuilly",
"Neuville-lès-Lœuilly",
"Neuvy-Bouin",
"Neuvy-Deux-Clochers",
"Neuvy-Grandchamp",
@ -4776,8 +4775,8 @@ FR_BASE_EXCEPTIONS = [
"Nuncq-Hautecôte",
"Nurieux-Volognat",
"Nuthe-Urstromtal",
"Nœux-les-Mines",
"Nœux-lès-Auxi",
"Nœux-les-Mines",
"Nœux-lès-Auxi",
"Nâves-Parmelan",
"Nézignan-l'Evêque",
"Nézignan-l'Évêque",
@ -5346,7 +5345,7 @@ FR_BASE_EXCEPTIONS = [
"Quincy-Voisins",
"Quincy-sous-le-Mont",
"Quint-Fonsegrives",
"Quœux-Haut-Maînil",
"Quœux-Haut-Maînil",
"Quœux-Haut-Maînil",
"Qwa-Qwa",
"R.-V.",
@ -5634,12 +5633,12 @@ FR_BASE_EXCEPTIONS = [
"Saint Aulaye-Puymangou",
"Saint Geniez d'Olt et d'Aubrac",
"Saint Martin de l'If",
"Saint-Denœux",
"Saint-Jean-de-Bœuf",
"Saint-Martin-le-Nœud",
"Saint-Michel-Tubœuf",
"Saint-Denœux",
"Saint-Jean-de-Bœuf",
"Saint-Martin-le-Nœud",
"Saint-Michel-Tubœuf",
"Saint-Paul - Flaugnac",
"Saint-Pierre-de-Bœuf",
"Saint-Pierre-de-Bœuf",
"Saint-Thegonnec Loc-Eguiner",
"Sainte-Alvère-Saint-Laurent Les Bâtons",
"Salignac-Eyvignes",
@ -6211,7 +6210,7 @@ FR_BASE_EXCEPTIONS = [
"Tite-Live",
"Titisee-Neustadt",
"Tobel-Tägerschen",
"Togny-aux-Bœufs",
"Togny-aux-Bœufs",
"Tongre-Notre-Dame",
"Tonnay-Boutonne",
"Tonnay-Charente",
@ -6339,7 +6338,7 @@ FR_BASE_EXCEPTIONS = [
"Vals-près-le-Puy",
"Valverde-Enrique",
"Valzin-en-Petite-Montagne",
"Vandœuvre-lès-Nancy",
"Vandœuvre-lès-Nancy",
"Varces-Allières-et-Risset",
"Varenne-l'Arconce",
"Varenne-sur-le-Doubs",
@ -6460,9 +6459,9 @@ FR_BASE_EXCEPTIONS = [
"Villenave-d'Ornon",
"Villequier-Aumont",
"Villerouge-Termenès",
"Villers-aux-Nœuds",
"Villers-aux-Nœuds",
"Villez-sur-le-Neubourg",
"Villiers-en-Désœuvre",
"Villiers-en-Désœuvre",
"Villieu-Loyes-Mollon",
"Villingen-Schwenningen",
"Villié-Morgon",
@ -6470,7 +6469,7 @@ FR_BASE_EXCEPTIONS = [
"Vilosnes-Haraumont",
"Vilters-Wangs",
"Vincent-Froideville",
"Vincy-Manœuvre",
"Vincy-Manœuvre",
"Vincy-Manœuvre",
"Vincy-Reuil-et-Magny",
"Vindrac-Alayrac",
@ -6514,8 +6513,8 @@ FR_BASE_EXCEPTIONS = [
"Vrigne-Meusiens",
"Vrijhoeve-Capelle",
"Vuisternens-devant-Romont",
"Vœlfling-lès-Bouzonville",
"Vœuil-et-Giget",
"Vœlfling-lès-Bouzonville",
"Vœuil-et-Giget",
"Vélez-Blanco",
"Vélez-Málaga",
"Vélez-Rubio",
@ -6618,7 +6617,7 @@ FR_BASE_EXCEPTIONS = [
"Wust-Fischbeck",
"Wutha-Farnroda",
"Wy-dit-Joli-Village",
"Wœlfling-lès-Sarreguemines",
"Wœlfling-lès-Sarreguemines",
"Wünnewil-Flamatt",
"X-SAMPA",
"X-arbre",

4
spacy/lang/fr/tokenizer_exceptions.py
View File

@ -4,7 +4,6 @@ from __future__ import unicode_literals
import re
from .punctuation import ELISION, HYPHENS
from ..tokenizer_exceptions import URL_PATTERN
from ..char_classes import ALPHA_LOWER, ALPHA
from ...symbols import ORTH, LEMMA
@ -455,9 +454,6 @@ _regular_exp += [
for hc in _hyphen_combination
]
# URLs
_regular_exp.append(URL_PATTERN)
TOKENIZER_EXCEPTIONS = _exc
TOKEN_MATCH = re.compile(

6
spacy/lang/hu/punctuation.py
View File

@ -10,7 +10,6 @@ _concat_icons = CONCAT_ICONS.replace("\u00B0", "")
_currency = r"\$¢£€¥฿"
_quotes = CONCAT_QUOTES.replace("'", "")
_units = UNITS.replace("%", "")
_prefixes = (
LIST_PUNCT
@ -21,7 +20,8 @@ _prefixes = (
)
_suffixes = (
LIST_PUNCT
[r"\+"]
+ LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ [_concat_icons]
@ -29,7 +29,7 @@ _suffixes = (
r"(?<=[0-9])\+",
r"(?<=°[FfCcKk])\.",
r"(?<=[0-9])(?:[{c}])".format(c=_currency),
r"(?<=[0-9])(?:{u})".format(u=_units),
r"(?<=[0-9])(?:{u})".format(u=UNITS),
r"(?<=[{al}{e}{q}(?:{c})])\.".format(
al=ALPHA_LOWER, e=r"%²\-\+", q=CONCAT_QUOTES, c=_currency
),

3
spacy/lang/hu/tokenizer_exceptions.py
View File

@ -4,7 +4,6 @@ from __future__ import unicode_literals
import re
from ..punctuation import ALPHA_LOWER, CURRENCY
from ..tokenizer_exceptions import URL_PATTERN
from ...symbols import ORTH
@ -649,4 +648,4 @@ _nums = r"(({ne})|({t})|({on})|({c}))({s})?".format(
TOKENIZER_EXCEPTIONS = _exc
TOKEN_MATCH = re.compile(r"^({u})|({n})$".format(u=URL_PATTERN, n=_nums)).match
TOKEN_MATCH = re.compile(r"^{n}$".format(n=_nums)).match

277
spacy/lang/ja/__init__.py
View File

@ -1,114 +1,279 @@
# encoding: utf8
from __future__ import unicode_literals, print_function
import re
from collections import namedtuple
import srsly
from collections import namedtuple, OrderedDict
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
from .tag_map import TAG_MAP
from .tag_orth_map import TAG_ORTH_MAP
from .tag_bigram_map import TAG_BIGRAM_MAP
from ...attrs import LANG
from ...language import Language
from ...tokens import Doc
from ...compat import copy_reg
from ...errors import Errors
from ...language import Language
from ...symbols import POS
from ...tokens import Doc
from ...util import DummyTokenizer
from ... import util
# Hold the attributes we need with convenient names
DetailedToken = namedtuple("DetailedToken", ["surface", "pos", "lemma"])
# Handling for multiple spaces in a row is somewhat awkward, this simplifies
# the flow by creating a dummy with the same interface.
DummyNode = namedtuple("DummyNode", ["surface", "pos", "feature"])
DummyNodeFeatures = namedtuple("DummyNodeFeatures", ["lemma"])
DummySpace = DummyNode(" ", " ", DummyNodeFeatures(" "))
DummyNode = namedtuple("DummyNode", ["surface", "pos", "lemma"])
DummySpace = DummyNode(" ", " ", " ")
def try_fugashi_import():
"""Fugashi is required for Japanese support, so check for it.
It it's not available blow up and explain how to fix it."""
def try_sudachi_import(split_mode="A"):
"""SudachiPy is required for Japanese support, so check for it.
It it's not available blow up and explain how to fix it.
split_mode should be one of these values: "A", "B", "C", None->"A"."""
try:
import fugashi
return fugashi
from sudachipy import dictionary, tokenizer
split_mode = {
None: tokenizer.Tokenizer.SplitMode.A,
"A": tokenizer.Tokenizer.SplitMode.A,
"B": tokenizer.Tokenizer.SplitMode.B,
"C": tokenizer.Tokenizer.SplitMode.C,
}[split_mode]
tok = dictionary.Dictionary().create(
mode=split_mode
)
return tok
except ImportError:
raise ImportError(
"Japanese support requires Fugashi: " "https://github.com/polm/fugashi"
"Japanese support requires SudachiPy and SudachiDict-core "
"(https://github.com/WorksApplications/SudachiPy). "
"Install with `pip install sudachipy sudachidict_core` or "
"install spaCy with `pip install spacy[ja]`."
)
def resolve_pos(token):
def resolve_pos(orth, pos, next_pos):
"""If necessary, add a field to the POS tag for UD mapping.
Under Universal Dependencies, sometimes the same Unidic POS tag can
be mapped differently depending on the literal token or its context
in the sentence. This function adds information to the POS tag to
resolve ambiguous mappings.
in the sentence. This function returns resolved POSs for both token
and next_token by tuple.
"""
# this is only used for consecutive ascii spaces
if token.surface == " ":
return "空白"
# Some tokens have their UD tag decided based on the POS of the following
# token.
# 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.
if token.pos == "連体詞,*,*,*":
if re.match(r"[こそあど此其彼]の", token.surface):
return token.pos + ",DET"
if re.match(r"[こそあど此其彼]", token.surface):
return token.pos + ",PRON"
return token.pos + ",ADJ"
return token.pos
# orth based rules
if pos[0] in TAG_ORTH_MAP:
orth_map = TAG_ORTH_MAP[pos[0]]
if orth in orth_map:
return orth_map[orth], None
# tag bi-gram mapping
if next_pos:
tag_bigram = pos[0], next_pos[0]
if tag_bigram in TAG_BIGRAM_MAP:
bipos = TAG_BIGRAM_MAP[tag_bigram]
if bipos[0] is None:
return TAG_MAP[pos[0]][POS], bipos[1]
else:
return bipos
return TAG_MAP[pos[0]][POS], None
def get_words_and_spaces(tokenizer, text):
"""Get the individual tokens that make up the sentence and handle white space.
# Use a mapping of paired punctuation to avoid splitting quoted sentences.
pairpunct = {'「':'」', '『': '』', '【': '】'}
Japanese doesn't usually use white space, and MeCab's handling of it for
multiple spaces in a row is somewhat awkward.
def separate_sentences(doc):
"""Given a doc, mark tokens that start sentences based on Unidic tags.
"""
tokens = tokenizer.parseToNodeList(text)
stack = [] # save paired punctuation
for i, token in enumerate(doc[:-2]):
# Set all tokens after the first to false by default. This is necessary
# for the doc code to be aware we've done sentencization, see
# `is_sentenced`.
token.sent_start = (i == 0)
if token.tag_:
if token.tag_ == "補助記号-括弧開":
ts = str(token)
if ts in pairpunct:
stack.append(pairpunct[ts])
elif stack and ts == stack[-1]:
stack.pop()
if token.tag_ == "補助記号-句点":
next_token = doc[i+1]
if next_token.tag_ != token.tag_ and not stack:
next_token.sent_start = True
def get_dtokens(tokenizer, text):
tokens = tokenizer.tokenize(text)
words = []
spaces = []
for token in tokens:
# If there's more than one space, spaces after the first become tokens
for ii in range(len(token.white_space) - 1):
words.append(DummySpace)
spaces.append(False)
for ti, token in enumerate(tokens):
tag = '-'.join([xx for xx in token.part_of_speech()[:4] if xx != '*'])
inf = '-'.join([xx for xx in token.part_of_speech()[4:] if xx != '*'])
dtoken = DetailedToken(
token.surface(),
(tag, inf),
token.dictionary_form())
if ti > 0 and words[-1].pos[0] == '空白' and tag == '空白':
# don't add multiple space tokens in a row
continue
words.append(dtoken)
words.append(token)
spaces.append(bool(token.white_space))
return words, spaces
# remove empty tokens. These can be produced with characters like … that
# Sudachi normalizes internally.
words = [ww for ww in words if len(ww.surface) > 0]
return words
def get_words_lemmas_tags_spaces(dtokens, text, gap_tag=("空白", "")):
words = [x.surface for x in dtokens]
if "".join("".join(words).split()) != "".join(text.split()):
raise ValueError(Errors.E194.format(text=text, words=words))
text_words = []
text_lemmas = []
text_tags = []
text_spaces = []
text_pos = 0
# handle empty and whitespace-only texts
if len(words) == 0:
return text_words, text_lemmas, text_tags, text_spaces
elif len([word for word in words if not word.isspace()]) == 0:
assert text.isspace()
text_words = [text]
text_lemmas = [text]
text_tags = [gap_tag]
text_spaces = [False]
return text_words, text_lemmas, text_tags, text_spaces
# normalize words to remove all whitespace tokens
norm_words, norm_dtokens = zip(*[(word, dtokens) for word, dtokens in zip(words, dtokens) if not word.isspace()])
# align words with text
for word, dtoken in zip(norm_words, norm_dtokens):
try:
word_start = text[text_pos:].index(word)
except ValueError:
raise ValueError(Errors.E194.format(text=text, words=words))
if word_start > 0:
w = text[text_pos:text_pos + word_start]
text_words.append(w)
text_lemmas.append(w)
text_tags.append(gap_tag)
text_spaces.append(False)
text_pos += word_start
text_words.append(word)
text_lemmas.append(dtoken.lemma)
text_tags.append(dtoken.pos)
text_spaces.append(False)
text_pos += len(word)
if text_pos < len(text) and text[text_pos] == " ":
text_spaces[-1] = True
text_pos += 1
if text_pos < len(text):
w = text[text_pos:]
text_words.append(w)
text_lemmas.append(w)
text_tags.append(gap_tag)
text_spaces.append(False)
return text_words, text_lemmas, text_tags, text_spaces
class JapaneseTokenizer(DummyTokenizer):
def __init__(self, cls, nlp=None):
def __init__(self, cls, nlp=None, config={}):
self.vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
self.tokenizer = try_fugashi_import().Tagger()
self.tokenizer.parseToNodeList("") # see #2901
self.split_mode = config.get("split_mode", None)
self.tokenizer = try_sudachi_import(self.split_mode)
def __call__(self, text):
dtokens, spaces = get_words_and_spaces(self.tokenizer, text)
words = [x.surface for x in dtokens]
dtokens = get_dtokens(self.tokenizer, text)
words, lemmas, unidic_tags, spaces = get_words_lemmas_tags_spaces(dtokens, text)
doc = Doc(self.vocab, words=words, spaces=spaces)
unidic_tags = []
for token, dtoken in zip(doc, dtokens):
unidic_tags.append(dtoken.pos)
token.tag_ = resolve_pos(dtoken)
next_pos = None
for idx, (token, lemma, unidic_tag) in enumerate(zip(doc, lemmas, unidic_tags)):
token.tag_ = unidic_tag[0]
if next_pos:
token.pos = next_pos
next_pos = None
else:
token.pos, next_pos = resolve_pos(
token.orth_,
unidic_tag,
unidic_tags[idx + 1] if idx + 1 < len(unidic_tags) else None
)
# if there's no lemma info (it's an unk) just use the surface
token.lemma_ = dtoken.feature.lemma or dtoken.surface
token.lemma_ = lemma
doc.user_data["unidic_tags"] = unidic_tags
return doc
def _get_config(self):
config = OrderedDict(
(
("split_mode", self.split_mode),
)
)
return config
def _set_config(self, config={}):
self.split_mode = config.get("split_mode", None)
def to_bytes(self, **kwargs):
serializers = OrderedDict(
(
("cfg", lambda: srsly.json_dumps(self._get_config())),
)
)
return util.to_bytes(serializers, [])
def from_bytes(self, data, **kwargs):
deserializers = OrderedDict(
(
("cfg", lambda b: self._set_config(srsly.json_loads(b))),
)
)
util.from_bytes(data, deserializers, [])
self.tokenizer = try_sudachi_import(self.split_mode)
return self
def to_disk(self, path, **kwargs):
path = util.ensure_path(path)
serializers = OrderedDict(
(
("cfg", lambda p: srsly.write_json(p, self._get_config())),
)
)
return util.to_disk(path, serializers, [])
def from_disk(self, path, **kwargs):
path = util.ensure_path(path)
serializers = OrderedDict(
(
("cfg", lambda p: self._set_config(srsly.read_json(p))),
)
)
util.from_disk(path, serializers, [])
self.tokenizer = try_sudachi_import(self.split_mode)
class JapaneseDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda _text: "ja"
stop_words = STOP_WORDS
tag_map = TAG_MAP
syntax_iterators = SYNTAX_ITERATORS
writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
@classmethod
def create_tokenizer(cls, nlp=None):
return JapaneseTokenizer(cls, nlp)
def create_tokenizer(cls, nlp=None, config={}):
return JapaneseTokenizer(cls, nlp, config)
class Japanese(Language):

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spacy/lang/ja/bunsetu.py Normal file
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# coding: utf8
from __future__ import unicode_literals
from .stop_words import STOP_WORDS
POS_PHRASE_MAP = {
"NOUN": "NP",
"NUM": "NP",
"PRON": "NP",
"PROPN": "NP",
"VERB": "VP",
"ADJ": "ADJP",
"ADV": "ADVP",
"CCONJ": "CCONJP",
}
# return value: [(bunsetu_tokens, phrase_type={'NP', 'VP', 'ADJP', 'ADVP'}, phrase_tokens)]
def yield_bunsetu(doc, debug=False):
bunsetu = []
bunsetu_may_end = False
phrase_type = None
phrase = None
prev = None
prev_tag = None
prev_dep = None
prev_head = None
for t in doc:
pos = t.pos_
pos_type = POS_PHRASE_MAP.get(pos, None)
tag = t.tag_
dep = t.dep_
head = t.head.i
if debug:
print(t.i, t.orth_, pos, pos_type, dep, head, bunsetu_may_end, phrase_type, phrase, bunsetu)
# DET is always an individual bunsetu
if pos == "DET":
if bunsetu:
yield bunsetu, phrase_type, phrase
yield [t], None, None
bunsetu = []
bunsetu_may_end = False
phrase_type = None
phrase = None
# PRON or Open PUNCT always splits bunsetu
elif tag == "補助記号-括弧開":
if bunsetu:
yield bunsetu, phrase_type, phrase
bunsetu = [t]
bunsetu_may_end = True
phrase_type = None
phrase = None
# bunsetu head not appeared
elif phrase_type is None:
if bunsetu and prev_tag == "補助記号-読点":
yield bunsetu, phrase_type, phrase
bunsetu = []
bunsetu_may_end = False
phrase_type = None
phrase = None
bunsetu.append(t)
if pos_type: # begin phrase
phrase = [t]
phrase_type = pos_type
if pos_type in {"ADVP", "CCONJP"}:
bunsetu_may_end = True
# entering new bunsetu
elif pos_type and (
pos_type != phrase_type or # different phrase type arises
bunsetu_may_end # same phrase type but bunsetu already ended
):
# exceptional case: NOUN to VERB
if phrase_type == "NP" and pos_type == "VP" and prev_dep == 'compound' and prev_head == t.i:
bunsetu.append(t)
phrase_type = "VP"
phrase.append(t)
# exceptional case: VERB to NOUN
elif phrase_type == "VP" and pos_type == "NP" and (
prev_dep == 'compound' and prev_head == t.i or
dep == 'compound' and prev == head or
prev_dep == 'nmod' and prev_head == t.i
):
bunsetu.append(t)
phrase_type = "NP"
phrase.append(t)
else:
yield bunsetu, phrase_type, phrase
bunsetu = [t]
bunsetu_may_end = False
phrase_type = pos_type
phrase = [t]
# NOUN bunsetu
elif phrase_type == "NP":
bunsetu.append(t)
if not bunsetu_may_end and ((
(pos_type == "NP" or pos == "SYM") and (prev_head == t.i or prev_head == head) and prev_dep in {'compound', 'nummod'}
) or (
pos == "PART" and (prev == head or prev_head == head) and dep == 'mark'
)):
phrase.append(t)
else:
bunsetu_may_end = True
# VERB bunsetu
elif phrase_type == "VP":
bunsetu.append(t)
if not bunsetu_may_end and pos == "VERB" and prev_head == t.i and prev_dep == 'compound':
phrase.append(t)
else:
bunsetu_may_end = True
# ADJ bunsetu
elif phrase_type == "ADJP" and tag != '連体詞':
bunsetu.append(t)
if not bunsetu_may_end and ((
pos == "NOUN" and (prev_head == t.i or prev_head == head) and prev_dep in {'amod', 'compound'}
) or (
pos == "PART" and (prev == head or prev_head == head) and dep == 'mark'
)):
phrase.append(t)
else:
bunsetu_may_end = True
# other bunsetu
else:
bunsetu.append(t)
prev = t.i
prev_tag = t.tag_
prev_dep = t.dep_
prev_head = head
if bunsetu:
yield bunsetu, phrase_type, phrase

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spacy/lang/ja/syntax_iterators.py Normal file
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# coding: utf8
from __future__ import unicode_literals
from ...symbols import NOUN, PROPN, PRON, VERB
# XXX this can probably be pruned a bit
labels = [
"nsubj",
"nmod",
"dobj",
"nsubjpass",
"pcomp",
"pobj",
"obj",
"obl",
"dative",
"appos",
"attr",
"ROOT",
]
def noun_chunks(obj):
"""
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
"""
doc = obj.doc # Ensure works on both Doc and Span.
np_deps = [doc.vocab.strings.add(label) for label in labels]
conj = doc.vocab.strings.add("conj")
np_label = doc.vocab.strings.add("NP")
seen = set()
for i, word in enumerate(obj):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.i in seen:
continue
if word.dep in np_deps:
unseen = [w.i for w in word.subtree if w.i not in seen]
if not unseen:
continue
# this takes care of particles etc.
seen.update(j.i for j in word.subtree)
# This avoids duplicating embedded clauses
seen.update(range(word.i + 1))
# if the head of this is a verb, mark that and rights seen
# Don't do the subtree as that can hide other phrases
if word.head.pos == VERB:
seen.add(word.head.i)
seen.update(w.i for w in word.head.rights)
yield unseen[0], word.i + 1, np_label
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

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spacy/lang/ja/tag_bigram_map.py Normal file
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# encoding: utf8
from __future__ import unicode_literals
from ...symbols import POS, ADJ, AUX, NOUN, PART, VERB
# mapping from tag bi-gram to pos of previous token
TAG_BIGRAM_MAP = {
# This covers only small part of AUX.
("形容詞-非自立可能", "助詞-終助詞"): (AUX, None),
("名詞-普通名詞-形状詞可能", "助動詞"): (ADJ, None),
# ("副詞", "名詞-普通名詞-形状詞可能"): (None, ADJ),
# This covers acl, advcl, obl and root, but has side effect for compound.
("名詞-普通名詞-サ変可能", "動詞-非自立可能"): (VERB, AUX),
# This covers almost all of the deps
("名詞-普通名詞-サ変形状詞可能", "動詞-非自立可能"): (VERB, AUX),
("名詞-普通名詞-副詞可能", "動詞-非自立可能"): (None, VERB),
("副詞", "動詞-非自立可能"): (None, VERB),
("形容詞-一般", "動詞-非自立可能"): (None, VERB),
("形容詞-非自立可能", "動詞-非自立可能"): (None, VERB),
("接頭辞", "動詞-非自立可能"): (None, VERB),
("助詞-係助詞", "動詞-非自立可能"): (None, VERB),
("助詞-副助詞", "動詞-非自立可能"): (None, VERB),
("助詞-格助詞", "動詞-非自立可能"): (None, VERB),
("補助記号-読点", "動詞-非自立可能"): (None, VERB),
("形容詞-一般", "接尾辞-名詞的-一般"): (None, PART),
("助詞-格助詞", "形状詞-助動詞語幹"): (None, NOUN),
("連体詞", "形状詞-助動詞語幹"): (None, NOUN),
("動詞-一般", "助詞-副助詞"): (None, PART),
("動詞-非自立可能", "助詞-副助詞"): (None, PART),
("助動詞", "助詞-副助詞"): (None, PART),
}

158
spacy/lang/ja/tag_map.py
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# encoding: utf8
from __future__ import unicode_literals
from ...symbols import POS, PUNCT, INTJ, X, ADJ, AUX, ADP, PART, SCONJ, NOUN
from ...symbols import POS, PUNCT, INTJ, X, ADJ, AUX, ADP, PART, CCONJ, SCONJ, NOUN
from ...symbols import SYM, PRON, VERB, ADV, PROPN, NUM, DET, SPACE
TAG_MAP = {
# Explanation of Unidic tags:
# https://www.gavo.t.u-tokyo.ac.jp/~mine/japanese/nlp+slp/UNIDIC_manual.pdf
# Universal Dependencies Mapping:
# Universal Dependencies Mapping: (Some of the entries in this mapping are updated to v2.6 in the list below)
# http://universaldependencies.org/ja/overview/morphology.html
# http://universaldependencies.org/ja/pos/all.html
"記号,一般,*,*": {
POS: PUNCT
"記号-一般": {
POS: NOUN
}, # this includes characters used to represent sounds like ドレミ
"記号,文字,*,*": {
POS: PUNCT
}, # this is for Greek and Latin characters used as sumbols, as in math
"感動詞,フィラー,*,*": {POS: INTJ},
"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
"記号-文字": {
POS: NOUN
}, # this is for Greek and Latin characters having some meanings, or used as symbols, as in math
"感動詞-フィラー": {POS: INTJ},
"感動詞-一般": {POS: INTJ},
"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},
"形容詞,一般,*,*": {POS: ADJ},
"形容詞,非自立可能,*,*": {POS: AUX}, # XXX ADJ if alone, AUX otherwise
"助詞,格助詞,*,*": {POS: ADP},
"助詞,係助詞,*,*": {POS: ADP},
"助詞,終助詞,*,*": {POS: PART},
"助詞,準体助詞,*,*": {POS: SCONJ}, # の as in 走るのが速い
"助詞,接続助詞,*,*": {POS: SCONJ}, # verb ending て
"助詞,副助詞,*,*": {POS: PART}, # ばかり, つつ after a verb
"助動詞,*,*,*": {POS: AUX},
"接続詞,*,*,*": {POS: SCONJ}, # XXX: might need refinement
"接頭辞,*,*,*": {POS: NOUN},
"接尾辞,形状詞的,*,*": {POS: ADJ}, # がち, チック
"接尾辞,形容詞的,*,*": {POS: ADJ}, # -らしい
"接尾辞,動詞的,*,*": {POS: NOUN}, # -じみ
"接尾辞,名詞的,サ変可能,*": {POS: NOUN}, # XXX see 名詞,普通名詞,サ変可能,*
"接尾辞,名詞的,一般,*": {POS: NOUN},
"接尾辞,名詞的,助数詞,*": {POS: NOUN},
"接尾辞,名詞的,副詞可能,*": {POS: NOUN}, # -後, -過ぎ
"代名詞,*,*,*": {POS: PRON},
"動詞,一般,*,*": {POS: VERB},
"動詞,非自立可能,*,*": {POS: VERB}, # XXX VERB if alone, AUX otherwise
"動詞,非自立可能,*,*,AUX": {POS: AUX},
"動詞,非自立可能,*,*,VERB": {POS: VERB},
"副詞,*,*,*": {POS: ADV},
"補助記号,ＡＡ,一般,*": {POS: SYM}, # text art
"補助記号,ＡＡ,顔文字,*": {POS: SYM}, # kaomoji
"補助記号,一般,*,*": {POS: SYM},
"補助記号,括弧開,*,*": {POS: PUNCT}, # open bracket
"補助記号,括弧閉,*,*": {POS: PUNCT}, # close bracket
"補助記号,句点,*,*": {POS: PUNCT}, # period or other EOS marker
"補助記号,読点,*,*": {POS: PUNCT}, # comma
"名詞,固有名詞,一般,*": {POS: PROPN}, # general proper noun
"名詞,固有名詞,人名,一般": {POS: PROPN}, # person's name
"名詞,固有名詞,人名,姓": {POS: PROPN}, # surname
"名詞,固有名詞,人名,名": {POS: PROPN}, # first name
"名詞,固有名詞,地名,一般": {POS: PROPN}, # place name
"名詞,固有名詞,地名,国": {POS: PROPN}, # country name
"名詞,助動詞語幹,*,*": {POS: AUX},
"名詞,数詞,*,*": {POS: NUM}, # includes Chinese numerals
"名詞,普通名詞,サ変可能,*": {POS: NOUN}, # XXX: sometimes VERB in UDv2; suru-verb noun
"名詞,普通名詞,サ変可能,*,NOUN": {POS: NOUN},
"名詞,普通名詞,サ変可能,*,VERB": {POS: VERB},
"名詞,普通名詞,サ変形状詞可能,*": {POS: NOUN}, # ex: 下手
"名詞,普通名詞,一般,*": {POS: NOUN},
"名詞,普通名詞,形状詞可能,*": {POS: NOUN}, # XXX: sometimes ADJ in UDv2
"名詞,普通名詞,形状詞可能,*,NOUN": {POS: NOUN},
"名詞,普通名詞,形状詞可能,*,ADJ": {POS: ADJ},
"名詞,普通名詞,助数詞可能,*": {POS: NOUN}, # counter / unit
"名詞,普通名詞,副詞可能,*": {POS: NOUN},
"連体詞,*,*,*": {POS: ADJ}, # XXX this has exceptions based on literal token
"連体詞,*,*,*,ADJ": {POS: ADJ},
"連体詞,*,*,*,PRON": {POS: PRON},
"連体詞,*,*,*,DET": {POS: DET},
"形状詞-一般": {POS: ADJ},
"形状詞-タリ": {POS: ADJ},
"形状詞-助動詞語幹": {POS: AUX},
"形容詞-一般": {POS: ADJ},
"形容詞-非自立可能": {POS: ADJ}, # XXX ADJ if alone, AUX otherwise
"助詞-格助詞": {POS: ADP},
"助詞-係助詞": {POS: ADP},
"助詞-終助詞": {POS: PART},
"助詞-準体助詞": {POS: SCONJ}, # の as in 走るのが速い
"助詞-接続助詞": {POS: SCONJ}, # verb ending て0
"助詞-副助詞": {POS: ADP}, # ばかり, つつ after a verb
"助動詞": {POS: AUX},
"接続詞": {POS: CCONJ}, # XXX: might need refinement
"接頭辞": {POS: NOUN},
"接尾辞-形状詞的": {POS: PART}, # がち, チック
"接尾辞-形容詞的": {POS: AUX}, # -らしい
"接尾辞-動詞的": {POS: PART}, # -じみ
"接尾辞-名詞的-サ変可能": {POS: NOUN}, # XXX see 名詞,普通名詞,サ変可能,*
"接尾辞-名詞的-一般": {POS: NOUN},
"接尾辞-名詞的-助数詞": {POS: NOUN},
"接尾辞-名詞的-副詞可能": {POS: NOUN}, # -後, -過ぎ
"代名詞": {POS: PRON},
"動詞-一般": {POS: VERB},
"動詞-非自立可能": {POS: AUX}, # XXX VERB if alone, AUX otherwise
"副詞": {POS: ADV},
"補助記号-ＡＡ-一般": {POS: SYM}, # text art
"補助記号-ＡＡ-顔文字": {POS: PUNCT}, # kaomoji
"補助記号-一般": {POS: SYM},
"補助記号-括弧開": {POS: PUNCT}, # open bracket
"補助記号-括弧閉": {POS: PUNCT}, # close bracket
"補助記号-句点": {POS: PUNCT}, # period or other EOS marker
"補助記号-読点": {POS: PUNCT}, # comma
"名詞-固有名詞-一般": {POS: PROPN}, # general proper noun
"名詞-固有名詞-人名-一般": {POS: PROPN}, # person's name
"名詞-固有名詞-人名-姓": {POS: PROPN}, # surname
"名詞-固有名詞-人名-名": {POS: PROPN}, # first name
"名詞-固有名詞-地名-一般": {POS: PROPN}, # place name
"名詞-固有名詞-地名-国": {POS: PROPN}, # country name
"名詞-助動詞語幹": {POS: AUX},
"名詞-数詞": {POS: NUM}, # includes Chinese numerals
"名詞-普通名詞-サ変可能": {POS: NOUN}, # XXX: sometimes VERB in UDv2; suru-verb noun
"名詞-普通名詞-サ変形状詞可能": {POS: NOUN},
"名詞-普通名詞-一般": {POS: NOUN},
"名詞-普通名詞-形状詞可能": {POS: NOUN}, # XXX: sometimes ADJ in UDv2
"名詞-普通名詞-助数詞可能": {POS: NOUN}, # counter / unit
"名詞-普通名詞-副詞可能": {POS: NOUN},
"連体詞": {POS: DET}, # XXX this has exceptions based on literal token
# GSD tags. These aren't in Unidic, but we need them for the GSD data.
"外国語": {POS: PROPN}, # Foreign words
"絵文字・記号等": {POS: SYM}, # emoji / kaomoji ^^;
}

30
spacy/lang/ja/tag_orth_map.py Normal file
View File

@ -0,0 +1,30 @@
# encoding: utf8
from __future__ import unicode_literals
from ...symbols import POS, ADJ, AUX, DET, PART, PRON, SPACE ,X
# mapping from tag bi-gram to pos of previous token
TAG_ORTH_MAP = {
"空白": {
" ": SPACE,
"　": X,
},
"助詞-副助詞": {
"たり": PART,
},
"連体詞": {
"あの": DET,
"かの": DET,
"この": DET,
"その": DET,
"どの": DET,
"彼の": DET,
"此の": DET,
"其の": DET,
"ある": PRON,
"こんな": PRON,
"そんな": PRON,
"どんな": PRON,
"あらゆる": PRON,
},
}

87
spacy/lang/pl/lemmatizer.py
View File

@ -6,98 +6,73 @@ from ...parts_of_speech import NAMES
class PolishLemmatizer(Lemmatizer):
# This lemmatizer implements lookup lemmatization based on
# the Morfeusz dictionary (morfeusz.sgjp.pl/en) by Institute of Computer Science PAS
# It utilizes some prefix based improvements for
# verb and adjectives lemmatization, as well as case-sensitive
# lemmatization for nouns
def __init__(self, lookups, *args, **kwargs):
# this lemmatizer is lookup based, so it does not require an index, exceptionlist, or rules
super(PolishLemmatizer, self).__init__(lookups)
self.lemma_lookups = {}
for tag in [
"ADJ",
"ADP",
"ADV",
"AUX",
"NOUN",
"NUM",
"PART",
"PRON",
"VERB",
"X",
]:
self.lemma_lookups[tag] = self.lookups.get_table(
"lemma_lookup_" + tag.lower(), {}
)
self.lemma_lookups["DET"] = self.lemma_lookups["X"]
self.lemma_lookups["PROPN"] = self.lemma_lookups["NOUN"]
# This lemmatizer implements lookup lemmatization based on the Morfeusz
# dictionary (morfeusz.sgjp.pl/en) by Institute of Computer Science PAS.
# It utilizes some prefix based improvements for verb and adjectives
# lemmatization, as well as case-sensitive lemmatization for nouns.
def __call__(self, string, univ_pos, morphology=None):
if isinstance(univ_pos, int):
univ_pos = NAMES.get(univ_pos, "X")
univ_pos = univ_pos.upper()
lookup_pos = univ_pos.lower()
if univ_pos == "PROPN":
lookup_pos = "noun"
lookup_table = self.lookups.get_table("lemma_lookup_" + lookup_pos, {})
if univ_pos == "NOUN":
return self.lemmatize_noun(string, morphology)
return self.lemmatize_noun(string, morphology, lookup_table)
if univ_pos != "PROPN":
string = string.lower()
if univ_pos == "ADJ":
return self.lemmatize_adj(string, morphology)
return self.lemmatize_adj(string, morphology, lookup_table)
elif univ_pos == "VERB":
return self.lemmatize_verb(string, morphology)
return self.lemmatize_verb(string, morphology, lookup_table)
lemma_dict = self.lemma_lookups.get(univ_pos, {})
return [lemma_dict.get(string, string.lower())]
return [lookup_table.get(string, string.lower())]
def lemmatize_adj(self, string, morphology):
def lemmatize_adj(self, string, morphology, lookup_table):
# this method utilizes different procedures for adjectives
# with 'nie' and 'naj' prefixes
lemma_dict = self.lemma_lookups["ADJ"]
if string[:3] == "nie":
search_string = string[3:]
if search_string[:3] == "naj":
naj_search_string = search_string[3:]
if naj_search_string in lemma_dict:
return [lemma_dict[naj_search_string]]
if search_string in lemma_dict:
return [lemma_dict[search_string]]
if naj_search_string in lookup_table:
return [lookup_table[naj_search_string]]
if search_string in lookup_table:
return [lookup_table[search_string]]
if string[:3] == "naj":
naj_search_string = string[3:]
if naj_search_string in lemma_dict:
return [lemma_dict[naj_search_string]]
if naj_search_string in lookup_table:
return [lookup_table[naj_search_string]]
return [lemma_dict.get(string, string)]
return [lookup_table.get(string, string)]
def lemmatize_verb(self, string, morphology):
def lemmatize_verb(self, string, morphology, lookup_table):
# this method utilizes a different procedure for verbs
# with 'nie' prefix
lemma_dict = self.lemma_lookups["VERB"]
if string[:3] == "nie":
search_string = string[3:]
if search_string in lemma_dict:
return [lemma_dict[search_string]]
if search_string in lookup_table:
return [lookup_table[search_string]]
return [lemma_dict.get(string, string)]
return [lookup_table.get(string, string)]
def lemmatize_noun(self, string, morphology):
def lemmatize_noun(self, string, morphology, lookup_table):
# this method is case-sensitive, in order to work
# for incorrectly tagged proper names
lemma_dict = self.lemma_lookups["NOUN"]
if string != string.lower():
if string.lower() in lemma_dict:
return [lemma_dict[string.lower()]]
elif string in lemma_dict:
return [lemma_dict[string]]
if string.lower() in lookup_table:
return [lookup_table[string.lower()]]
elif string in lookup_table:
return [lookup_table[string]]
return [string.lower()]
return [lemma_dict.get(string, string)]
return [lookup_table.get(string, string)]
def lookup(self, string, orth=None):
return string.lower()

5
spacy/lang/ta/examples.py
View File

@ -18,4 +18,9 @@ sentences = [
"இந்த ஃபோனுடன் சுமார் ரூ.2,990 மதிப்புள்ள போட் ராக்கர்ஸ் நிறுவனத்தின் ஸ்போர்ட் புளூடூத் ஹெட்போன்ஸ் இலவசமாக வழங்கப்படவுள்ளது.",
"மட்டக்களப்பில் பல இடங்களில் வீட்டுத் திட்டங்களுக்கு இன்று அடிக்கல் நாட்டல்",
"ஐ போன்க்கு முகத்தை வைத்து அன்லாக் செய்யும் முறை மற்றும் விரலால் தொட்டு அன்லாக் செய்யும் முறையை வாட்ஸ் ஆப் நிறுவனம் இதற்கு முன் கண்டுபிடித்தது",
"இது ஒரு வாக்கியம்.",
"ஆப்பிள் நிறுவனம் யு.கே. தொடக்க நிறுவனத்தை ஒரு லட்சம் கோடிக்கு வாங்கப் பார்க்கிறது",
"தன்னாட்சி கார்கள் காப்பீட்டு பொறுப்பை உற்பத்தியாளரிடம் மாற்றுகின்றன",
"நடைபாதை விநியோக ரோபோக்களை தடை செய்வதை சான் பிரான்சிஸ்கோ கருதுகிறது",
"லண்டன் ஐக்கிய இராச்சியத்தில் ஒரு பெரிய நகரம்."
]

5
spacy/lang/tokenizer_exceptions.py
View File

@ -3,7 +3,7 @@ from __future__ import unicode_literals
import re
from .char_classes import ALPHA_LOWER
from .char_classes import ALPHA_LOWER, ALPHA
from ..symbols import ORTH, POS, TAG, LEMMA, SPACE
@ -58,7 +58,8 @@ URL_PATTERN = (
# fmt: on
).strip()
TOKEN_MATCH = re.compile("(?u)" + URL_PATTERN).match
TOKEN_MATCH = None
URL_MATCH = re.compile("(?u)" + URL_PATTERN).match
BASE_EXCEPTIONS = {}

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

@ -2,7 +2,7 @@
from __future__ import unicode_literals
from ...symbols import POS, PUNCT, ADJ, SCONJ, CCONJ, NUM, DET, ADV, ADP, X
from ...symbols import NOUN, PART, INTJ, PRON, VERB, SPACE
from ...symbols import NOUN, PART, INTJ, PRON, VERB, SPACE, PROPN
# The Chinese part-of-speech tagger uses the OntoNotes 5 version of the Penn
# Treebank tag set. We also map the tags to the simpler Universal Dependencies
@ -28,7 +28,7 @@ TAG_MAP = {
"URL": {POS: X},
"INF": {POS: X},
"NN": {POS: NOUN},
"NR": {POS: NOUN},
"NR": {POS: PROPN},
"NT": {POS: NOUN},
"VA": {POS: VERB},
"VC": {POS: VERB},

43
spacy/language.py
View File

@ -28,7 +28,7 @@ from ._ml import link_vectors_to_models, create_default_optimizer
from .attrs import IS_STOP, LANG, NORM
from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .lang.punctuation import TOKENIZER_INFIXES
from .lang.tokenizer_exceptions import TOKEN_MATCH
from .lang.tokenizer_exceptions import TOKEN_MATCH, URL_MATCH
from .lang.norm_exceptions import BASE_NORMS
from .lang.tag_map import TAG_MAP
from .tokens import Doc
@ -89,6 +89,7 @@ class BaseDefaults(object):
def create_tokenizer(cls, nlp=None):
rules = cls.tokenizer_exceptions
token_match = cls.token_match
url_match = cls.url_match
prefix_search = (
util.compile_prefix_regex(cls.prefixes).search if cls.prefixes else None
)
@ -106,10 +107,12 @@ class BaseDefaults(object):
suffix_search=suffix_search,
infix_finditer=infix_finditer,
token_match=token_match,
url_match=url_match,
)
pipe_names = ["tagger", "parser", "ner"]
token_match = TOKEN_MATCH
url_match = URL_MATCH
prefixes = tuple(TOKENIZER_PREFIXES)
suffixes = tuple(TOKENIZER_SUFFIXES)
infixes = tuple(TOKENIZER_INFIXES)
@ -931,15 +934,26 @@ class Language(object):
DOCS: https://spacy.io/api/language#from_disk
"""
def deserialize_meta(path):
if path.exists():
data = srsly.read_json(path)
self.meta.update(data)
# self.meta always overrides meta["vectors"] with the metadata
# from self.vocab.vectors, so set the name directly
self.vocab.vectors.name = data.get("vectors", {}).get("name")
def deserialize_vocab(path):
if path.exists():
self.vocab.from_disk(path)
_fix_pretrained_vectors_name(self)
if disable is not None:
warnings.warn(Warnings.W014, DeprecationWarning)
exclude = disable
path = util.ensure_path(path)
deserializers = OrderedDict()
deserializers["meta.json"] = lambda p: self.meta.update(srsly.read_json(p))
deserializers["vocab"] = lambda p: self.vocab.from_disk(
p
) and _fix_pretrained_vectors_name(self)
deserializers["meta.json"] = deserialize_meta
deserializers["vocab"] = deserialize_vocab
deserializers["tokenizer"] = lambda p: self.tokenizer.from_disk(
p, exclude=["vocab"]
)
@ -993,14 +1007,23 @@ class Language(object):
DOCS: https://spacy.io/api/language#from_bytes
"""
def deserialize_meta(b):
data = srsly.json_loads(b)
self.meta.update(data)
# self.meta always overrides meta["vectors"] with the metadata
# from self.vocab.vectors, so set the name directly
self.vocab.vectors.name = data.get("vectors", {}).get("name")
def deserialize_vocab(b):
self.vocab.from_bytes(b)
_fix_pretrained_vectors_name(self)
if disable is not None:
warnings.warn(Warnings.W014, DeprecationWarning)
exclude = disable
deserializers = OrderedDict()
deserializers["meta.json"] = lambda b: self.meta.update(srsly.json_loads(b))
deserializers["vocab"] = lambda b: self.vocab.from_bytes(
b
) and _fix_pretrained_vectors_name(self)
deserializers["meta.json"] = deserialize_meta
deserializers["vocab"] = deserialize_vocab
deserializers["tokenizer"] = lambda b: self.tokenizer.from_bytes(
b, exclude=["vocab"]
)
@ -1066,7 +1089,7 @@ class component(object):
def _fix_pretrained_vectors_name(nlp):
# TODO: Replace this once we handle vectors consistently as static
# data
if "vectors" in nlp.meta and nlp.meta["vectors"].get("name"):
if "vectors" in nlp.meta and "name" in nlp.meta["vectors"]:
nlp.vocab.vectors.name = nlp.meta["vectors"]["name"]
elif not nlp.vocab.vectors.size:
nlp.vocab.vectors.name = None

3
spacy/lexeme.pyx
View File

@ -12,7 +12,6 @@ import numpy
import warnings
from thinc.neural.util import get_array_module
from libc.stdint cimport UINT64_MAX
from .typedefs cimport attr_t, flags_t
from .attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
from .attrs cimport IS_TITLE, IS_UPPER, LIKE_URL, LIKE_NUM, LIKE_EMAIL, IS_STOP
@ -23,7 +22,7 @@ from .attrs import intify_attrs
from .errors import Errors, Warnings
OOV_RANK = UINT64_MAX
OOV_RANK = 0xffffffffffffffff # UINT64_MAX
memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
EMPTY_LEXEME.id = OOV_RANK

2
spacy/matcher/phrasematcher.pyx
View File

@ -332,7 +332,7 @@ def unpickle_matcher(vocab, docs, callbacks, attr):
matcher = PhraseMatcher(vocab, attr=attr)
for key, specs in docs.items():
callback = callbacks.get(key, None)
matcher.add(key, callback, *specs)
matcher.add(key, specs, on_match=callback)
return matcher

5
spacy/morphology.pyx
View File

@ -152,7 +152,10 @@ cdef class Morphology:
self.tags = PreshMap()
# Add special space symbol. We prefix with underscore, to make sure it
# always sorts to the end.
space_attrs = tag_map.get('SP', {POS: SPACE})
if '_SP' in tag_map:
space_attrs = tag_map.get('_SP')
else:
space_attrs = tag_map.get('SP', {POS: SPACE})
if '_SP' not in tag_map:
self.strings.add('_SP')
tag_map = dict(tag_map)

135
spacy/pipeline/pipes.pyx
View File

@ -516,6 +516,8 @@ class Tagger(Pipe):
lemma_tables = ["lemma_rules", "lemma_index", "lemma_exc", "lemma_lookup"]
if not any(table in self.vocab.lookups for table in lemma_tables):
warnings.warn(Warnings.W022)
if len(self.vocab.lookups.get_table("lexeme_norm", {})) == 0:
warnings.warn(Warnings.W033.format(model="part-of-speech tagger"))
orig_tag_map = dict(self.vocab.morphology.tag_map)
new_tag_map = OrderedDict()
for raw_text, annots_brackets in get_gold_tuples():
@ -526,6 +528,8 @@ class Tagger(Pipe):
new_tag_map[tag] = orig_tag_map[tag]
else:
new_tag_map[tag] = {POS: X}
if "_SP" in orig_tag_map:
new_tag_map["_SP"] = orig_tag_map["_SP"]
cdef Vocab vocab = self.vocab
if new_tag_map:
vocab.morphology = Morphology(vocab.strings, new_tag_map,
@ -1168,6 +1172,9 @@ class EntityLinker(Pipe):
self.model = True
self.kb = None
self.cfg = dict(cfg)
# how many neightbour sentences to take into account
self.n_sents = cfg.get("n_sents", 0)
def set_kb(self, kb):
self.kb = kb
@ -1216,6 +1223,9 @@ class EntityLinker(Pipe):
for doc, gold in zip(docs, golds):
ents_by_offset = dict()
sentences = [s for s in doc.sents]
for ent in doc.ents:
ents_by_offset[(ent.start_char, ent.end_char)] = ent
@ -1226,17 +1236,34 @@ class EntityLinker(Pipe):
# the gold annotations should link to proper entities - if this fails, the dataset is likely corrupt
if not (start, end) in ents_by_offset:
raise RuntimeError(Errors.E188)
ent = ents_by_offset[(start, end)]
for kb_id, value in kb_dict.items():
# Currently only training on the positive instances
if value:
try:
sentence_docs.append(ent.sent.as_doc())
# find the sentence in the list of sentences.
sent_index = sentences.index(ent.sent)
except AttributeError:
# Catch the exception when ent.sent is None and provide a user-friendly warning
raise RuntimeError(Errors.E030)
# get n previous sentences, if there are any
start_sentence = max(0, sent_index - self.n_sents)
# get n posterior sentences, or as many < n as there are
end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
# get token positions
start_token = sentences[start_sentence].start
end_token = sentences[end_sentence].end
# append that span as a doc to training
sent_doc = doc[start_token:end_token].as_doc()
sentence_docs.append(sent_doc)
sentence_encodings, bp_context = self.model.begin_update(sentence_docs, drop=drop)
loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds, docs=None)
bp_context(d_scores, sgd=sgd)
@ -1307,69 +1334,81 @@ class EntityLinker(Pipe):
if isinstance(docs, Doc):
docs = [docs]
for i, doc in enumerate(docs):
sentences = [s for s in doc.sents]
if len(doc) > 0:
# Looping through each sentence and each entity
# This may go wrong if there are entities across sentences - which shouldn't happen normally.
for sent in doc.sents:
sent_doc = sent.as_doc()
# currently, the context is the same for each entity in a sentence (should be refined)
sentence_encoding = self.model([sent_doc])[0]
xp = get_array_module(sentence_encoding)
sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)
for sent_index, sent in enumerate(sentences):
if sent.ents:
# get n_neightbour sentences, clipped to the length of the document
start_sentence = max(0, sent_index - self.n_sents)
end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
for ent in sent_doc.ents:
entity_count += 1
start_token = sentences[start_sentence].start
end_token = sentences[end_sentence].end
to_discard = self.cfg.get("labels_discard", [])
if to_discard and ent.label_ in to_discard:
# ignoring this entity - setting to NIL
final_kb_ids.append(self.NIL)
final_tensors.append(sentence_encoding)
sent_doc = doc[start_token:end_token].as_doc()
else:
candidates = self.kb.get_candidates(ent.text)
if not candidates:
# no prediction possible for this entity - setting to NIL
# currently, the context is the same for each entity in a sentence (should be refined)
sentence_encoding = self.model([sent_doc])[0]
xp = get_array_module(sentence_encoding)
sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)
for ent in sent.ents:
entity_count += 1
to_discard = self.cfg.get("labels_discard", [])
if to_discard and ent.label_ in to_discard:
# ignoring this entity - setting to NIL
final_kb_ids.append(self.NIL)
final_tensors.append(sentence_encoding)
elif len(candidates) == 1:
# shortcut for efficiency reasons: take the 1 candidate
# TODO: thresholding
final_kb_ids.append(candidates[0].entity_)
final_tensors.append(sentence_encoding)
else:
random.shuffle(candidates)
candidates = self.kb.get_candidates(ent.text)
if not candidates:
# no prediction possible for this entity - setting to NIL
final_kb_ids.append(self.NIL)
final_tensors.append(sentence_encoding)
# this will set all prior probabilities to 0 if they should be excluded from the model
prior_probs = xp.asarray([c.prior_prob for c in candidates])
if not self.cfg.get("incl_prior", True):
prior_probs = xp.asarray([0.0 for c in candidates])
scores = prior_probs
elif len(candidates) == 1:
# shortcut for efficiency reasons: take the 1 candidate
# add in similarity from the context
if self.cfg.get("incl_context", True):
entity_encodings = xp.asarray([c.entity_vector for c in candidates])
entity_norm = xp.linalg.norm(entity_encodings, axis=1)
# TODO: thresholding
final_kb_ids.append(candidates[0].entity_)
final_tensors.append(sentence_encoding)
if len(entity_encodings) != len(prior_probs):
raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
else:
random.shuffle(candidates)
# cosine similarity
sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm)
if sims.shape != prior_probs.shape:
raise ValueError(Errors.E161)
scores = prior_probs + sims - (prior_probs*sims)
# this will set all prior probabilities to 0 if they should be excluded from the model
prior_probs = xp.asarray([c.prior_prob for c in candidates])
if not self.cfg.get("incl_prior", True):
prior_probs = xp.asarray([0.0 for c in candidates])
scores = prior_probs
# TODO: thresholding
best_index = scores.argmax()
best_candidate = candidates[best_index]
final_kb_ids.append(best_candidate.entity_)
final_tensors.append(sentence_encoding)
# add in similarity from the context
if self.cfg.get("incl_context", True):
entity_encodings = xp.asarray([c.entity_vector for c in candidates])
entity_norm = xp.linalg.norm(entity_encodings, axis=1)
if len(entity_encodings) != len(prior_probs):
raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
# cosine similarity
sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm)
if sims.shape != prior_probs.shape:
raise ValueError(Errors.E161)
scores = prior_probs + sims - (prior_probs*sims)
# TODO: thresholding
best_index = scores.argmax()
best_candidate = candidates[best_index]
final_kb_ids.append(best_candidate.entity_)
final_tensors.append(sentence_encoding)
if not (len(final_tensors) == len(final_kb_ids) == entity_count):
raise RuntimeError(Errors.E147.format(method="predict", msg="result variables not of equal length"))

20
spacy/syntax/nn_parser.pyx
View File

@ -9,6 +9,7 @@ import numpy
cimport cython.parallel
import numpy.random
cimport numpy as np
from itertools import islice
from cpython.ref cimport PyObject, Py_XDECREF
from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
from libc.math cimport exp
@ -25,6 +26,7 @@ from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module
from thinc.linalg cimport Vec, VecVec
import srsly
import warnings
from ._parser_model cimport alloc_activations, free_activations
from ._parser_model cimport predict_states, arg_max_if_valid
@ -36,7 +38,7 @@ from .._ml import link_vectors_to_models, create_default_optimizer
from ..compat import copy_array
from ..tokens.doc cimport Doc
from ..gold cimport GoldParse
from ..errors import Errors, TempErrors
from ..errors import Errors, TempErrors, Warnings
from .. import util
from .stateclass cimport StateClass
from ._state cimport StateC
@ -600,6 +602,8 @@ cdef class Parser:
**self.cfg.get('optimizer', {}))
def begin_training(self, get_gold_tuples, pipeline=None, sgd=None, **cfg):
if len(self.vocab.lookups.get_table("lexeme_norm", {})) == 0:
warnings.warn(Warnings.W033.format(model="parser or NER"))
if 'model' in cfg:
self.model = cfg['model']
if not hasattr(get_gold_tuples, '__call__'):
@ -620,15 +624,15 @@ cdef class Parser:
self.model, cfg = self.Model(self.moves.n_moves, **cfg)
if sgd is None:
sgd = self.create_optimizer()
docs = []
golds = []
for raw_text, annots_brackets in get_gold_tuples():
doc_sample = []
gold_sample = []
for raw_text, annots_brackets in islice(get_gold_tuples(), 1000):
for annots, brackets in annots_brackets:
ids, words, tags, heads, deps, ents = annots
docs.append(Doc(self.vocab, words=words))
golds.append(GoldParse(docs[-1], words=words, tags=tags,
heads=heads, deps=deps, entities=ents))
self.model.begin_training(docs, golds)
doc_sample.append(Doc(self.vocab, words=words))
gold_sample.append(GoldParse(doc_sample[-1], words=words, tags=tags,
heads=heads, deps=deps, entities=ents))
self.model.begin_training(doc_sample, gold_sample)
if pipeline is not None:
self.init_multitask_objectives(get_gold_tuples, pipeline, sgd=sgd, **cfg)
link_vectors_to_models(self.vocab)

2
spacy/tests/conftest.py
View File

@ -140,7 +140,7 @@ def it_tokenizer():
@pytest.fixture(scope="session")
def ja_tokenizer():
pytest.importorskip("fugashi")
pytest.importorskip("sudachipy")
return get_lang_class("ja").Defaults.create_tokenizer()

2
spacy/tests/lang/en/test_exceptions.py
View File

@ -46,7 +46,7 @@ def test_en_tokenizer_doesnt_split_apos_exc(en_tokenizer, text):
assert tokens[0].text == text
@pytest.mark.parametrize("text", ["we'll", "You'll", "there'll"])
@pytest.mark.parametrize("text", ["we'll", "You'll", "there'll", "this'll", "those'll"])
def test_en_tokenizer_handles_ll_contraction(en_tokenizer, text):
tokens = en_tokenizer(text)
assert len(tokens) == 2

2
spacy/tests/lang/ja/test_lemmatization.py
View File

@ -6,7 +6,7 @@ import pytest
@pytest.mark.parametrize(
"word,lemma",
[("新しく", "新しい"), ("赤く", "赤い"), ("すごく", "凄い"), ("いただきました", "頂く"), ("なった", "成る")],
[("新しく", "新しい"), ("赤く", "赤い"), ("すごく", "すごい"), ("いただきました", "いただく"), ("なった", "なる")],
)
def test_ja_lemmatizer_assigns(ja_tokenizer, word, lemma):
test_lemma = ja_tokenizer(word)[0].lemma_

37
spacy/tests/lang/ja/test_serialize.py Normal file
View File

@ -0,0 +1,37 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
from spacy.lang.ja import Japanese
from ...util import make_tempdir
def test_ja_tokenizer_serialize(ja_tokenizer):
tokenizer_bytes = ja_tokenizer.to_bytes()
nlp = Japanese()
nlp.tokenizer.from_bytes(tokenizer_bytes)
assert tokenizer_bytes == nlp.tokenizer.to_bytes()
assert nlp.tokenizer.split_mode == None
with make_tempdir() as d:
file_path = d / "tokenizer"
ja_tokenizer.to_disk(file_path)
nlp = Japanese()
nlp.tokenizer.from_disk(file_path)
assert tokenizer_bytes == nlp.tokenizer.to_bytes()
assert nlp.tokenizer.split_mode == None
# split mode is (de)serialized correctly
nlp = Japanese(meta={"tokenizer": {"config": {"split_mode": "B"}}})
nlp_r = Japanese()
nlp_bytes = nlp.to_bytes()
nlp_r.from_bytes(nlp_bytes)
assert nlp_bytes == nlp_r.to_bytes()
assert nlp_r.tokenizer.split_mode == "B"
with make_tempdir() as d:
nlp.to_disk(d)
nlp_r = Japanese()
nlp_r.from_disk(d)
assert nlp_bytes == nlp_r.to_bytes()
assert nlp_r.tokenizer.split_mode == "B"

67
spacy/tests/lang/ja/test_tokenizer.py
View File

@ -3,6 +3,8 @@ from __future__ import unicode_literals
import pytest
from ...tokenizer.test_naughty_strings import NAUGHTY_STRINGS
from spacy.lang.ja import Japanese
# fmt: off
TOKENIZER_TESTS = [
@ -14,20 +16,26 @@ TOKENIZER_TESTS = [
]
TAG_TESTS = [
("日本語だよ", ['名詞,固有名詞,地名,国', '名詞,普通名詞,一般,*', '助動詞,*,*,*', '助詞,終助詞,*,*']),
("東京タワーの近くに住んでいます。", ['名詞,固有名詞,地名,一般', '名詞,普通名詞,一般,*', '助詞,格助詞,*,*', '名詞,普通名詞,副詞可能,*', '助詞,格助詞,*,*', '動詞,一般,*,*', '助詞,接続助詞,*,*', '動詞,非自立可能,*,*', '助動詞,*,*,*', '補助記号,句点,*,*']),
("吾輩は猫である。", ['代名詞,*,*,*', '助詞,係助詞,*,*', '名詞,普通名詞,一般,*', '助動詞,*,*,*', '動詞,非自立可能,*,*', '補助記号,句点,*,*']),
("月に代わって、お仕置きよ!", ['名詞,普通名詞,助数詞可能,*', '助詞,格助詞,*,*', '動詞,一般,*,*', '助詞,接続助詞,*,*', '補助記号,読点,*,*', '接頭辞,*,*,*', '名詞,普通名詞,一般,*', '助詞,終助詞,*,*', '補助記号,句点,*,*']),
("すもももももももものうち", ['名詞,普通名詞,一般,*', '助詞,係助詞,*,*', '名詞,普通名詞,一般,*', '助詞,係助詞,*,*', '名詞,普通名詞,一般,*', '助詞,格助詞,*,*', '名詞,普通名詞,副詞可能,*'])
("日本語だよ", ['名詞-固有名詞-地名-国', '名詞-普通名詞-一般', '助動詞', '助詞-終助詞']),
("東京タワーの近くに住んでいます。", ['名詞-固有名詞-地名-一般', '名詞-普通名詞-一般', '助詞-格助詞', '名詞-普通名詞-副詞可能', '助詞-格助詞', '動詞-一般', '助詞-接続助詞', '動詞-非自立可能', '助動詞', '補助記号-句点']),
("吾輩は猫である。", ['代名詞', '助詞-係助詞', '名詞-普通名詞-一般', '助動詞', '動詞-非自立可能', '補助記号-句点']),
("月に代わって、お仕置きよ!", ['名詞-普通名詞-助数詞可能', '助詞-格助詞', '動詞-一般', '助詞-接続助詞', '補助記号-読点', '接頭辞', '名詞-普通名詞-一般', '助詞-終助詞', '補助記号-句点']),
("すもももももももものうち", ['名詞-普通名詞-一般', '助詞-係助詞', '名詞-普通名詞-一般', '助詞-係助詞', '名詞-普通名詞-一般', '助詞-格助詞', '名詞-普通名詞-副詞可能'])
]
POS_TESTS = [
('日本語だよ', ['PROPN', 'NOUN', 'AUX', 'PART']),
('日本語だよ', ['fish', 'NOUN', 'AUX', 'PART']),
('東京タワーの近くに住んでいます。', ['PROPN', 'NOUN', 'ADP', 'NOUN', 'ADP', 'VERB', 'SCONJ', 'VERB', 'AUX', 'PUNCT']),
('吾輩は猫である。', ['PRON', 'ADP', 'NOUN', 'AUX', 'VERB', 'PUNCT']),
('月に代わって、お仕置きよ!', ['NOUN', 'ADP', 'VERB', 'SCONJ', 'PUNCT', 'NOUN', 'NOUN', 'PART', 'PUNCT']),
('すもももももももものうち', ['NOUN', 'ADP', 'NOUN', 'ADP', 'NOUN', 'ADP', 'NOUN'])
]
SENTENCE_TESTS = [
('あれ。これ。', ['あれ。', 'これ。']),
('「伝染るんです。」という漫画があります。',
['「伝染るんです。」という漫画があります。']),
]
# fmt: on
@ -43,14 +51,55 @@ def test_ja_tokenizer_tags(ja_tokenizer, text, expected_tags):
assert tags == expected_tags
#XXX This isn't working? Always passes
@pytest.mark.parametrize("text,expected_pos", POS_TESTS)
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):
@pytest.mark.skip(reason="sentence segmentation in tokenizer is buggy")
@pytest.mark.parametrize("text,expected_sents", SENTENCE_TESTS)
def test_ja_tokenizer_pos(ja_tokenizer, text, expected_sents):
sents = [str(sent) for sent in ja_tokenizer(text).sents]
assert sents == expected_sents
def test_ja_tokenizer_extra_spaces(ja_tokenizer):
# note: three spaces after "I"
tokens = ja_tokenizer("I like cheese.")
assert tokens[1].orth_ == " "
assert tokens[2].orth_ == " "
assert tokens[1].orth_ == " "
@pytest.mark.parametrize("text", NAUGHTY_STRINGS)
def test_ja_tokenizer_naughty_strings(ja_tokenizer, text):
tokens = ja_tokenizer(text)
assert tokens.text_with_ws == text
@pytest.mark.parametrize("text,len_a,len_b,len_c",
[
("選挙管理委員会", 4, 3, 1),
("客室乗務員", 3, 2, 1),
("労働者協同組合", 4, 3, 1),
("機能性食品", 3, 2, 1),
]
)
def test_ja_tokenizer_split_modes(ja_tokenizer, text, len_a, len_b, len_c):
nlp_a = Japanese(meta={"tokenizer": {"config": {"split_mode": "A"}}})
nlp_b = Japanese(meta={"tokenizer": {"config": {"split_mode": "B"}}})
nlp_c = Japanese(meta={"tokenizer": {"config": {"split_mode": "C"}}})
assert len(ja_tokenizer(text)) == len_a
assert len(nlp_a(text)) == len_a
assert len(nlp_b(text)) == len_b
assert len(nlp_c(text)) == len_c
def test_ja_tokenizer_emptyish_texts(ja_tokenizer):
doc = ja_tokenizer("")
assert len(doc) == 0
doc = ja_tokenizer(" ")
assert len(doc) == 1
doc = ja_tokenizer("\n\n\n \t\t \n\n\n")
assert len(doc) == 1

24
spacy/tests/matcher/test_phrase_matcher.py
View File

@ -2,6 +2,7 @@
from __future__ import unicode_literals
import pytest
import srsly
from mock import Mock
from spacy.matcher import PhraseMatcher
from spacy.tokens import Doc
@ -266,3 +267,26 @@ def test_phrase_matcher_basic_check(en_vocab):
pattern = Doc(en_vocab, words=["hello", "world"])
with pytest.raises(ValueError):
matcher.add("TEST", pattern)
def test_phrase_matcher_pickle(en_vocab):
matcher = PhraseMatcher(en_vocab)
mock = Mock()
matcher.add("TEST", [Doc(en_vocab, words=["test"])])
matcher.add("TEST2", [Doc(en_vocab, words=["test2"])], on_match=mock)
doc = Doc(en_vocab, words=["these", "are", "tests", ":", "test", "test2"])
assert len(matcher) == 2
b = srsly.pickle_dumps(matcher)
matcher_unpickled = srsly.pickle_loads(b)
# call after pickling to avoid recursion error related to mock
matches = matcher(doc)
matches_unpickled = matcher_unpickled(doc)
assert len(matcher) == len(matcher_unpickled)
assert matches == matches_unpickled
# clunky way to vaguely check that callback is unpickled
(vocab, docs, callbacks, attr) = matcher_unpickled.__reduce__()[1]
assert isinstance(callbacks.get("TEST2"), Mock)

17
spacy/tests/parser/test_ner.py
View File

@ -4,6 +4,8 @@ from __future__ import unicode_literals
import pytest
from spacy.lang.en import English
from spacy.language import Language
from spacy.lookups import Lookups
from spacy.pipeline import EntityRecognizer, EntityRuler
from spacy.vocab import Vocab
from spacy.syntax.ner import BiluoPushDown
@ -305,6 +307,21 @@ def test_change_number_features():
nlp("hello world")
def test_ner_warns_no_lookups():
nlp = Language()
nlp.vocab.lookups = Lookups()
assert not len(nlp.vocab.lookups)
ner = nlp.create_pipe("ner")
nlp.add_pipe(ner)
with pytest.warns(UserWarning):
nlp.begin_training()
nlp.vocab.lookups.add_table("lexeme_norm")
nlp.vocab.lookups.get_table("lexeme_norm")["a"] = "A"
with pytest.warns(None) as record:
nlp.begin_training()
assert not record.list
class BlockerComponent1(object):
name = "my_blocker"

3
spacy/tests/regression/test_issue5152.py
View File

@ -1,3 +1,6 @@
# coding: utf8
from __future__ import unicode_literals
from spacy.lang.en import English

25
spacy/tests/regression/test_issue5230.py
View File

@ -1,16 +1,17 @@
# coding: utf8
import warnings
from unittest import TestCase
import pytest
import srsly
from numpy import zeros
from spacy.kb import KnowledgeBase, Writer
from spacy.vectors import Vectors
from spacy.language import Language
from spacy.pipeline import Pipe
from spacy.tests.util import make_tempdir
from spacy.compat import is_python2
from ..util import make_tempdir
def nlp():
@ -96,12 +97,14 @@ def write_obj_and_catch_warnings(obj):
return list(filter(lambda x: isinstance(x, ResourceWarning), warnings_list))
@pytest.mark.skipif(is_python2, reason="ResourceWarning needs Python 3.x")
@pytest.mark.parametrize("obj", objects_to_test[0], ids=objects_to_test[1])
def test_to_disk_resource_warning(obj):
warnings_list = write_obj_and_catch_warnings(obj)
assert len(warnings_list) == 0
@pytest.mark.skipif(is_python2, reason="ResourceWarning needs Python 3.x")
def test_writer_with_path_py35():
writer = None
with make_tempdir() as d:
@ -132,11 +135,13 @@ def test_save_and_load_knowledge_base():
pytest.fail(str(e))
class TestToDiskResourceWarningUnittest(TestCase):
def test_resource_warning(self):
scenarios = zip(*objects_to_test)
if not is_python2:
for scenario in scenarios:
with self.subTest(msg=scenario[1]):
warnings_list = write_obj_and_catch_warnings(scenario[0])
self.assertEqual(len(warnings_list), 0)
class TestToDiskResourceWarningUnittest(TestCase):
def test_resource_warning(self):
scenarios = zip(*objects_to_test)
for scenario in scenarios:
with self.subTest(msg=scenario[1]):
warnings_list = write_obj_and_catch_warnings(scenario[0])
self.assertEqual(len(warnings_list), 0)

5
spacy/tests/regression/test_issue5458.py
View File

@ -1,3 +1,6 @@
# coding: utf-8
from __future__ import unicode_literals
from spacy.lang.en import English
from spacy.lang.en.syntax_iterators import noun_chunks
from spacy.tests.util import get_doc
@ -6,11 +9,13 @@ from spacy.vocab import Vocab
def test_issue5458():
# Test that the noun chuncker does not generate overlapping spans
# fmt: off
words = ["In", "an", "era", "where", "markets", "have", "brought", "prosperity", "and", "empowerment", "."]
vocab = Vocab(strings=words)
dependencies = ["ROOT", "det", "pobj", "advmod", "nsubj", "aux", "relcl", "dobj", "cc", "conj", "punct"]
pos_tags = ["ADP", "DET", "NOUN", "ADV", "NOUN", "AUX", "VERB", "NOUN", "CCONJ", "NOUN", "PUNCT"]
heads = [0, 1, -2, 6, 2, 1, -4, -1, -1, -2, -10]
# fmt: on
en_doc = get_doc(vocab, words, pos_tags, heads, dependencies)
en_doc.noun_chunks_iterator = noun_chunks

2
spacy/tests/serialize/test_serialize_vocab_strings.py
View File

@ -5,6 +5,7 @@ import pytest
import pickle
from spacy.vocab import Vocab
from spacy.strings import StringStore
from spacy.compat import is_python2
from ..util import make_tempdir
@ -134,6 +135,7 @@ def test_serialize_stringstore_roundtrip_disk(strings1, strings2):
assert list(sstore1_d) != list(sstore2_d)
@pytest.mark.skipif(is_python2, reason="Dict order? Not sure if worth investigating")
@pytest.mark.parametrize("strings,lex_attr", test_strings_attrs)
def test_pickle_vocab(strings, lex_attr):
vocab = Vocab(strings=strings)

6
spacy/tests/test_lemmatizer.py
View File

@ -33,17 +33,17 @@ def test_lemmatizer_reflects_lookups_changes():
assert Doc(new_nlp.vocab, words=["hello"])[0].lemma_ == "world"
def test_tagger_warns_no_lemma_lookups():
def test_tagger_warns_no_lookups():
nlp = Language()
nlp.vocab.lookups = Lookups()
assert not len(nlp.vocab.lookups)
tagger = nlp.create_pipe("tagger")
with pytest.warns(UserWarning):
tagger.begin_training()
nlp.add_pipe(tagger)
with pytest.warns(UserWarning):
nlp.begin_training()
nlp.vocab.lookups.add_table("lemma_lookup")
nlp.vocab.lookups.add_table("lexeme_norm")
nlp.vocab.lookups.get_table("lexeme_norm")["a"] = "A"
with pytest.warns(None) as record:
nlp.begin_training()
assert not record.list

32
spacy/tests/test_misc.py
View File

@ -4,12 +4,14 @@ from __future__ import unicode_literals
import pytest
import os
import ctypes
import srsly
from pathlib import Path
from spacy import util
from spacy import prefer_gpu, require_gpu
from spacy.compat import symlink_to, symlink_remove, path2str, is_windows
from spacy._ml import PrecomputableAffine
from subprocess import CalledProcessError
from .util import make_tempdir
@pytest.fixture
@ -146,3 +148,33 @@ def test_load_model_blank_shortcut():
assert nlp.pipeline == []
with pytest.raises(ImportError):
util.load_model("blank:fjsfijsdof")
def test_load_model_version_compat():
"""Test warnings for various spacy_version specifications in meta. Since
this is more of a hack for v2, manually specify the current major.minor
version to simplify test creation."""
nlp = util.load_model("blank:en")
assert nlp.meta["spacy_version"].startswith(">=2.3")
with make_tempdir() as d:
# no change: compatible
nlp.to_disk(d)
meta_path = Path(d / "meta.json")
util.get_model_meta(d)
# additional compatible upper pin
nlp.meta["spacy_version"] = ">=2.3.0,<2.4.0"
srsly.write_json(meta_path, nlp.meta)
util.get_model_meta(d)
# incompatible older version
nlp.meta["spacy_version"] = ">=2.2.5"
srsly.write_json(meta_path, nlp.meta)
with pytest.warns(UserWarning):
util.get_model_meta(d)
# invalid version specification
nlp.meta["spacy_version"] = ">@#$%_invalid_version"
srsly.write_json(meta_path, nlp.meta)
with pytest.warns(UserWarning):
util.get_model_meta(d)

4
spacy/tests/tokenizer/test_urls.py
View File

@ -122,12 +122,12 @@ SUFFIXES = ['"', ":", ">"]
@pytest.mark.parametrize("url", URLS_SHOULD_MATCH)
def test_should_match(en_tokenizer, url):
assert en_tokenizer.token_match(url) is not None
assert en_tokenizer.url_match(url) is not None
@pytest.mark.parametrize("url", URLS_SHOULD_NOT_MATCH)
def test_should_not_match(en_tokenizer, url):
assert en_tokenizer.token_match(url) is None
assert en_tokenizer.url_match(url) is None
@pytest.mark.parametrize("url", URLS_BASIC)

2
spacy/tests/vocab_vectors/test_vectors.py
View File

@ -10,6 +10,7 @@ from spacy.vectors import Vectors
from spacy.tokenizer import Tokenizer
from spacy.strings import hash_string
from spacy.tokens import Doc
from spacy.compat import is_python2
from ..util import add_vecs_to_vocab, make_tempdir
@ -339,6 +340,7 @@ def test_vocab_prune_vectors():
assert_allclose(similarity, cosine(data[0], data[2]), atol=1e-4, rtol=1e-3)
@pytest.mark.skipif(is_python2, reason="Dict order? Not sure if worth investigating")
def test_vectors_serialize():
data = numpy.asarray([[4, 2, 2, 2], [4, 2, 2, 2], [1, 1, 1, 1]], dtype="f")
v = Vectors(data=data, keys=["A", "B", "C"])

1
spacy/tokenizer.pxd
View File

@ -17,6 +17,7 @@ cdef class Tokenizer:
cpdef readonly Vocab vocab
cdef object _token_match
cdef object _url_match
cdef object _prefix_search
cdef object _suffix_search
cdef object _infix_finditer

51
spacy/tokenizer.pyx
View File

@ -30,7 +30,8 @@ cdef class Tokenizer:
DOCS: https://spacy.io/api/tokenizer
"""
def __init__(self, Vocab vocab, rules=None, prefix_search=None,
suffix_search=None, infix_finditer=None, token_match=None):
suffix_search=None, infix_finditer=None, token_match=None,
url_match=None):
"""Create a `Tokenizer`, to create `Doc` objects given unicode text.
vocab (Vocab): A storage container for lexical types.
@ -43,6 +44,8 @@ cdef class Tokenizer:
`re.compile(string).finditer` to find infixes.
token_match (callable): A boolean function matching strings to be
recognised as tokens.
url_match (callable): A boolean function matching strings to be
recognised as tokens after considering prefixes and suffixes.
RETURNS (Tokenizer): The newly constructed object.
EXAMPLE:
@ -55,6 +58,7 @@ cdef class Tokenizer:
self._cache = PreshMap()
self._specials = PreshMap()
self.token_match = token_match
self.url_match = url_match
self.prefix_search = prefix_search
self.suffix_search = suffix_search
self.infix_finditer = infix_finditer
@ -70,6 +74,14 @@ cdef class Tokenizer:
self._token_match = token_match
self._flush_cache()
property url_match:
def __get__(self):
return self._url_match
def __set__(self, url_match):
self._url_match = url_match
self._flush_cache()
property prefix_search:
def __get__(self):
return self._prefix_search
@ -108,11 +120,12 @@ cdef class Tokenizer:
def __reduce__(self):
args = (self.vocab,
self._rules,
self.rules,
self.prefix_search,
self.suffix_search,
self.infix_finditer,
self.token_match)
self.token_match,
self.url_match)
return (self.__class__, args, None, None)
cpdef Doc tokens_from_list(self, list strings):
@ -240,6 +253,8 @@ cdef class Tokenizer:
cdef unicode minus_suf
cdef size_t last_size = 0
while string and len(string) != last_size:
if self.token_match and self.token_match(string):
break
if self._specials.get(hash_string(string)) != NULL:
has_special[0] = 1
break
@ -295,7 +310,9 @@ cdef class Tokenizer:
cache_hit = self._try_cache(hash_string(string), tokens)
if cache_hit:
pass
elif self.token_match and self.token_match(string):
elif (self.token_match and self.token_match(string)) or \
(self.url_match and \
self.url_match(string)):
# We're always saying 'no' to spaces here -- the caller will
# fix up the outermost one, with reference to the original.
# See Issue #859
@ -448,6 +465,11 @@ cdef class Tokenizer:
suffix_search = self.suffix_search
infix_finditer = self.infix_finditer
token_match = self.token_match
if token_match is None:
token_match = re.compile("a^").match
url_match = self.url_match
if url_match is None:
url_match = re.compile("a^").match
special_cases = {}
for orth, special_tokens in self.rules.items():
special_cases[orth] = [intify_attrs(special_token, strings_map=self.vocab.strings, _do_deprecated=True) for special_token in special_tokens]
@ -456,6 +478,10 @@ cdef class Tokenizer:
suffixes = []
while substring:
while prefix_search(substring) or suffix_search(substring):
if token_match(substring):
tokens.append(("TOKEN_MATCH", substring))
substring = ''
break
if substring in special_cases:
tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
substring = ''
@ -476,12 +502,15 @@ cdef class Tokenizer:
break
suffixes.append(("SUFFIX", substring[split:]))
substring = substring[:split]
if substring in special_cases:
tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
substring = ''
elif token_match(substring):
if token_match(substring):
tokens.append(("TOKEN_MATCH", substring))
substring = ''
elif url_match(substring):
tokens.append(("URL_MATCH", substring))
substring = ''
elif substring in special_cases:
tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
substring = ''
elif list(infix_finditer(substring)):
infixes = infix_finditer(substring)
offset = 0
@ -543,6 +572,7 @@ cdef class Tokenizer:
("suffix_search", lambda: _get_regex_pattern(self.suffix_search)),
("infix_finditer", lambda: _get_regex_pattern(self.infix_finditer)),
("token_match", lambda: _get_regex_pattern(self.token_match)),
("url_match", lambda: _get_regex_pattern(self.url_match)),
("exceptions", lambda: OrderedDict(sorted(self._rules.items())))
))
exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
@ -564,11 +594,12 @@ cdef class Tokenizer:
("suffix_search", lambda b: data.setdefault("suffix_search", b)),
("infix_finditer", lambda b: data.setdefault("infix_finditer", b)),
("token_match", lambda b: data.setdefault("token_match", b)),
("url_match", lambda b: data.setdefault("url_match", b)),
("exceptions", lambda b: data.setdefault("rules", b))
))
exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
msg = util.from_bytes(bytes_data, deserializers, exclude)
for key in ["prefix_search", "suffix_search", "infix_finditer", "token_match"]:
for key in ["prefix_search", "suffix_search", "infix_finditer", "token_match", "url_match"]:
if key in data:
data[key] = unescape_unicode(data[key])
if "prefix_search" in data and isinstance(data["prefix_search"], basestring_):
@ -579,6 +610,8 @@ cdef class Tokenizer:
self.infix_finditer = re.compile(data["infix_finditer"]).finditer
if "token_match" in data and isinstance(data["token_match"], basestring_):
self.token_match = re.compile(data["token_match"]).match
if "url_match" in data and isinstance(data["url_match"], basestring_):
self.url_match = re.compile(data["url_match"]).match
if "rules" in data and isinstance(data["rules"], dict):
# make sure to hard reset the cache to remove data from the default exceptions
self._rules = {}

6
spacy/tokens/morphanalysis.pyx
View File

@ -46,12 +46,6 @@ cdef class MorphAnalysis:
"""The number of features in the analysis."""
return self.c.length
def __str__(self):
return self.to_json()
def __repr__(self):
return self.to_json()
def __hash__(self):
return self.key

26
spacy/util.py
View File

@ -17,6 +17,7 @@ import srsly
import catalogue
import sys
import warnings
from . import about
try:
import jsonschema
@ -250,6 +251,31 @@ def get_model_meta(path):
for setting in ["lang", "name", "version"]:
if setting not in meta or not meta[setting]:
raise ValueError(Errors.E054.format(setting=setting))
if "spacy_version" in meta:
about_major_minor = ".".join(about.__version__.split(".")[:2])
if not meta["spacy_version"].startswith(">=" + about_major_minor):
# try to simplify version requirements from model meta to vx.x
# for warning message
meta_spacy_version = "v" + ".".join(
meta["spacy_version"].replace(">=", "").split(".")[:2]
)
# if the format is unexpected, supply the full version
if not re.match(r"v\d+\.\d+", meta_spacy_version):
meta_spacy_version = meta["spacy_version"]
warn_msg = Warnings.W031.format(
model=meta["lang"] + "_" + meta["name"],
model_version=meta["version"],
version=meta_spacy_version,
current=about.__version__,
)
warnings.warn(warn_msg)
else:
warn_msg = Warnings.W032.format(
model=meta["lang"] + "_" + meta["name"],
model_version=meta["version"],
current=about.__version__,
)
warnings.warn(warn_msg)
return meta

4
spacy/vectors.pyx
View File

@ -425,9 +425,9 @@ cdef class Vectors:
self.data = xp.load(str(path))
serializers = OrderedDict((
("key2row", load_key2row),
("keys", load_keys),
("vectors", load_vectors),
("keys", load_keys),
("key2row", load_key2row),
))
util.from_disk(path, serializers, [])
self._sync_unset()

3
spacy/vocab.pyx
View File

@ -46,7 +46,8 @@ cdef class Vocab:
vice versa.
lookups (Lookups): Container for large lookup tables and dictionaries.
lookups_extra (Lookups): Container for optional lookup tables and dictionaries.
name (unicode): Optional name to identify the vectors table.
oov_prob (float): Default OOV probability.
vectors_name (unicode): Optional name to identify the vectors table.
RETURNS (Vocab): The newly constructed object.
"""
lex_attr_getters = lex_attr_getters if lex_attr_getters is not None else {}

24
website/docs/api/cli.md
View File

@ -455,7 +455,7 @@ improvement.
```bash
$ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir]
[--width] [--depth] [--cnn-window] [--cnn-pieces] [--use-chars] [--sa-depth]
[--width] [--conv-depth] [--cnn-window] [--cnn-pieces] [--use-chars] [--sa-depth]
[--embed-rows] [--loss_func] [--dropout] [--batch-size] [--max-length]
[--min-length] [--seed] [--n-iter] [--use-vectors] [--n-save-every]
[--init-tok2vec] [--epoch-start]
@ -467,7 +467,7 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir]
| `vectors_model` | positional | Name or path to spaCy model with vectors to learn from. |
| `output_dir` | positional | Directory to write models to on each epoch. |
| `--width`, `-cw` | option | Width of CNN layers. |
| `--depth`, `-cd` | option | Depth of CNN layers. |
| `--conv-depth`, `-cd` | option | Depth of CNN layers. |
| `--cnn-window`, `-cW` <Tag variant="new">2.2.2</Tag> | option | Window size for CNN layers. |
| `--cnn-pieces`, `-cP` <Tag variant="new">2.2.2</Tag> | option | Maxout size for CNN layers. `1` for [Mish](https://github.com/digantamisra98/Mish). |
| `--use-chars`, `-chr` <Tag variant="new">2.2.2</Tag> | flag | Whether to use character-based embedding. |
@ -541,16 +541,16 @@ $ python -m spacy init-model [lang] [output_dir] [--jsonl-loc] [--vectors-loc]
[--prune-vectors]
```
| Argument | Type | Description |
| ----------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `lang` | positional | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`. |
| `output_dir` | positional | Model output directory. Will be created if it doesn't exist. |
| `--jsonl-loc`, `-j` | option | Optional location of JSONL-formatted [vocabulary file](/api/annotation#vocab-jsonl) with lexical attributes. |
| `--vectors-loc`, `-v` | option | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. |
| `--truncate-vectors`, `-t` | option | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. |
| `--prune-vectors`, `-V` | option | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. |
| `--vectors-name`, `-vn` | option | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. |
| **CREATES** | model | A spaCy model containing the vocab and vectors. |
| Argument | Type | Description |
| ------------------------------------------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `lang` | positional | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`. |
| `output_dir` | positional | Model output directory. Will be created if it doesn't exist. |
| `--jsonl-loc`, `-j` | option | Optional location of JSONL-formatted [vocabulary file](/api/annotation#vocab-jsonl) with lexical attributes. |
| `--vectors-loc`, `-v` | option | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. |
| `--truncate-vectors`, `-t` <Tag variant="new">2.3</Tag> | option | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. |
| `--prune-vectors`, `-V` | option | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. |
| `--vectors-name`, `-vn` | option | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. |
| **CREATES** | model | A spaCy model containing the vocab and vectors. |
## Evaluate {#evaluate new="2"}

17
website/docs/api/tokenizer.md
View File

@ -35,14 +35,15 @@ the
> ```
| Name | Type | Description |
| ---------------- | ----------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | A storage container for lexical types. |
| `rules` | dict | Exceptions and special-cases for the tokenizer. |
| `prefix_search` | callable | A function matching the signature of `re.compile(string).search` to match prefixes. |
| `suffix_search` | callable | A function matching the signature of `re.compile(string).search` to match suffixes. |
| `infix_finditer` | callable | A function matching the signature of `re.compile(string).finditer` to find infixes. |
| `token_match` | callable | A function matching the signature of `re.compile(string).match to find token matches. |
| **RETURNS** | `Tokenizer` | The newly constructed object. |
| ---------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | A storage container for lexical types. |
| `rules` | dict | Exceptions and special-cases for the tokenizer. |
| `prefix_search` | callable | A function matching the signature of `re.compile(string).search` to match prefixes. |
| `suffix_search` | callable | A function matching the signature of `re.compile(string).search` to match suffixes. |
| `infix_finditer` | callable | A function matching the signature of `re.compile(string).finditer` to find infixes. |
| `token_match` | callable | A function matching the signature of `re.compile(string).match` to find token matches. |
| `url_match` | callable | A function matching the signature of `re.compile(string).match` to find token matches after considering prefixes and suffixes. |
| **RETURNS** | `Tokenizer` | The newly constructed object. |
## Tokenizer.\_\_call\_\_ {#call tag="method"}

2
website/docs/usage/adding-languages.md
View File

@ -288,7 +288,7 @@ common spelling. This has no effect on any other token attributes, or
tokenization in general, but it ensures that **equivalent tokens receive similar
representations**. This can improve the model's predictions on words that
weren't common in the training data, but are equivalent to other words – for
example, "realize" and "realize", or "thx" and "thanks".
example, "realise" and "realize", or "thx" and "thanks".
Similarly, spaCy also includes
[global base norms](https://github.com/explosion/spaCy/tree/master/spacy/lang/norm_exceptions.py)

39
website/docs/usage/linguistic-features.md
View File

@ -738,6 +738,10 @@ def tokenizer_pseudo_code(self, special_cases, prefix_search, suffix_search,
suffixes = []
while substring:
while prefix_search(substring) or suffix_search(substring):
if token_match(substring):
tokens.append(substring)
substring = ''
break
if substring in special_cases:
tokens.extend(special_cases[substring])
substring = ''
@ -752,12 +756,15 @@ def tokenizer_pseudo_code(self, special_cases, prefix_search, suffix_search,
split = suffix_search(substring).start()
suffixes.append(substring[split:])
substring = substring[:split]
if substring in special_cases:
tokens.extend(special_cases[substring])
substring = ''
elif token_match(substring):
if token_match(substring):
tokens.append(substring)
substring = ''
elif url_match(substring):
tokens.append(substring)
substring = ''
elif substring in special_cases:
tokens.extend(special_cases[substring])
substring = ''
elif list(infix_finditer(substring)):
infixes = infix_finditer(substring)
offset = 0
@ -778,17 +785,19 @@ def tokenizer_pseudo_code(self, special_cases, prefix_search, suffix_search,
The algorithm can be summarized as follows:
1. Iterate over whitespace-separated substrings.
2. Check whether we have an explicitly defined rule for this substring. If we
do, use it.
3. Otherwise, try to consume one prefix. If we consumed a prefix, go back to #2,
so that special cases always get priority.
4. If we didn't consume a prefix, try to consume a suffix and then go back to
2. Look for a token match. If there is a match, stop processing and keep this
token.
3. Check whether we have an explicitly defined special case for this substring.
If we do, use it.
4. Otherwise, try to consume one prefix. If we consumed a prefix, go back to
#2, so that the token match and special cases always get priority.
5. If we didn't consume a prefix, try to consume a suffix and then go back to
#2.
5. If we can't consume a prefix or a suffix, look for a special case.
6. Next, look for a token match.
7. Look for "infixes" — stuff like hyphens etc. and split the substring into
6. If we can't consume a prefix or a suffix, look for a URL match.
7. If there's no URL match, then look for a special case.
8. Look for "infixes" — stuff like hyphens etc. and split the substring into
tokens on all infixes.
8. Once we can't consume any more of the string, handle it as a single token.
9. Once we can't consume any more of the string, handle it as a single token.
#### Debugging the tokenizer {#tokenizer-debug new="2.2.3"}
@ -832,8 +841,8 @@ domain. There are five things you would need to define:
hyphens etc.
5. An optional boolean function `token_match` matching strings that should never
be split, overriding the infix rules. Useful for things like URLs or numbers.
Note that prefixes and suffixes will be split off before `token_match` is
applied.
6. An optional boolean function `url_match`, which is similar to `token_match`
except prefixes and suffixes are removed before applying the match.
You shouldn't usually need to create a `Tokenizer` subclass. Standard usage is
to use `re.compile()` to build a regular expression object, and pass its

2
website/meta/universe.json
View File

@ -2235,7 +2235,7 @@
"",
"nlp = spacy.load('en_core_web_sm')",
"nlp.add_pipe(LanguageDetector())",
"doc = nlp('Life is like a box of chocolates. You never know what you're gonna get.')",
"doc = nlp('Life is like a box of chocolates. You never know what you are gonna get.')",
"",
"assert doc._.language == 'en'",
"assert doc._.language_score >= 0.8"

33
website/src/templates/models.js
View File

@ -1,4 +1,4 @@
import React, { useEffect, useState, useMemo } from 'react'
import React, { useEffect, useState, useMemo, Fragment } from 'react'
import { StaticQuery, graphql } from 'gatsby'
import { window } from 'browser-monads'
@ -83,15 +83,24 @@ function formatVectors(data) {
function formatAccuracy(data) {
if (!data) return null
const labels = { tags_acc: 'POS', ents_f: 'NER F', ents_p: 'NER P', ents_r: 'NER R' }
const labels = {
las: 'LAS',
uas: 'UAS',
tags_acc: 'TAG',
ents_f: 'NER F',
ents_p: 'NER P',
ents_r: 'NER R',
}
const isSyntax = key => ['tags_acc', 'las', 'uas'].includes(key)
const isNer = key => key.startsWith('ents_')
return Object.keys(data).map(key => ({
label: labels[key] || key.toUpperCase(),
value: data[key].toFixed(2),
help: MODEL_META[key],
type: isNer(key) ? 'ner' : isSyntax(key) ? 'syntax' : null,
}))
return Object.keys(data)
.filter(key => labels[key])
.map(key => ({
label: labels[key],
value: data[key].toFixed(2),
help: MODEL_META[key],
type: isNer(key) ? 'ner' : isSyntax(key) ? 'syntax' : null,
}))
}
function formatModelMeta(data) {
@ -115,11 +124,11 @@ function formatModelMeta(data) {
function formatSources(data = []) {
const sources = data.map(s => (isString(s) ? { name: s } : s))
return sources.map(({ name, url, author }, i) => (
<>
<Fragment key={i}>
{i > 0 && <br />}
{name && url ? <Link to={url}>{name}</Link> : name}
{author && ` (${author})`}
</>
</Fragment>
))
}
@ -308,12 +317,12 @@ const Model = ({ name, langId, langName, baseUrl, repo, compatibility, hasExampl
</Td>
<Td>
{labelNames.map((label, i) => (
<>
<Fragment key={i}>
{i > 0 && ', '}
<InlineCode wrap key={label}>
{label}
</InlineCode>
</>
</Fragment>
))}
</Td>
</Tr>