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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 | Al Johri |
| Company name (if applicable) | N/A |
| Title or role (if applicable) | N/A |
| Date | December 27th, 2019 |
| GitHub username | AlJohri |
| Website (optional) | http://aljohri.com/ |

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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 | Jan Jessewitsch |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 16.02.2020 |
| GitHub username | Jan-711 |
| Website (optional) | |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [ ] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [x] 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 | Paco Nathan |
| Company name (if applicable) | Derwen, Inc. |
| Title or role (if applicable) | Managing Partner |
| Date | 2020-01-25 |
| GitHub username | ceteri |
| Website (optional) | https://derwen.ai/paco |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [ ] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [x] 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 | Filip Bednárik |
| Company name (if applicable) | Ardevop, s. r. o. |
| Title or role (if applicable) | IT Consultant |
| Date | 2020-01-26 |
| GitHub username | drndos |
| Website (optional) | https://ardevop.sk |

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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 | Ivan Echevarria |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2019-12-24 |
| GitHub username | iechevarria |
| Website (optional) | https://echevarria.io |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [ ] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Anastasiia Iurshina |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 28.12.2019 |
| GitHub username | iurshina |
| 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,
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documentation, or any other material posted or submitted by you to the project.
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- Each contribution that you submit is and shall be an original work of
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- each contribution shall be in compliance with U.S. export control laws and
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actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
| ----------------------------- | ---------------- |
| Name | Tyler Couto |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | January 29, 2020 |
| GitHub username | onlyanegg |
| Website (optional) | |

2
MANIFEST.in
View File

@ -1,5 +1,5 @@
recursive-include include *.h
recursive-include spacy *.pyx *.pxd *.txt
recursive-include spacy *.txt *.pyx *.pxd
include LICENSE
include README.md
include bin/spacy

1
bin/spacy
View File

@ -1 +1,2 @@
#! /bin/sh
python -m spacy "$@"

29
bin/wiki_entity_linking/README.md
View File

@ -7,16 +7,17 @@ Run `wikipedia_pretrain_kb.py`
* WikiData: get `latest-all.json.bz2` from https://dumps.wikimedia.org/wikidatawiki/entities/
* Wikipedia: get `enwiki-latest-pages-articles-multistream.xml.bz2` from https://dumps.wikimedia.org/enwiki/latest/ (or for any other language)
* You can set the filtering parameters for KB construction:
* `max_per_alias`: (max) number of candidate entities in the KB per alias/synonym
* `min_freq`: threshold of number of times an entity should occur in the corpus to be included in the KB
* `min_pair`: threshold of number of times an entity+alias combination should occur in the corpus to be included in the KB
* `max_per_alias` (`-a`): (max) number of candidate entities in the KB per alias/synonym
* `min_freq` (`-f`): threshold of number of times an entity should occur in the corpus to be included in the KB
* `min_pair` (`-c`): threshold of number of times an entity+alias combination should occur in the corpus to be included in the KB
* Further parameters to set:
* `descriptions_from_wikipedia`: whether to parse descriptions from Wikipedia (`True`) or Wikidata (`False`)
* `entity_vector_length`: length of the pre-trained entity description vectors
* `lang`: language for which to fetch Wikidata information (as the dump contains all languages)
* `descriptions_from_wikipedia` (`-wp`): whether to parse descriptions from Wikipedia (`True`) or Wikidata (`False`)
* `entity_vector_length` (`-v`): length of the pre-trained entity description vectors
* `lang` (`-la`): language for which to fetch Wikidata information (as the dump contains all languages)
Quick testing and rerunning:
* When trying out the pipeline for a quick test, set `limit_prior`, `limit_train` and/or `limit_wd` to read only parts of the dumps instead of everything.
* When trying out the pipeline for a quick test, set `limit_prior` (`-lp`), `limit_train` (`-lt`) and/or `limit_wd` (`-lw`) to read only parts of the dumps instead of everything.
* e.g. set `-lt 20000 -lp 2000 -lw 3000 -f 1`
* If you only want to (re)run certain parts of the pipeline, just remove the corresponding files and they will be recalculated or reparsed.
@ -24,11 +25,13 @@ Quick testing and rerunning:
Run `wikidata_train_entity_linker.py`
* This takes the **KB directory** produced by Step 1, and trains an **Entity Linking model**
* Specify the output directory (`-o`) in which the final, trained model will be saved
* You can set the learning parameters for the EL training:
* `epochs`: number of training iterations
* `dropout`: dropout rate
* `lr`: learning rate
* `l2`: L2 regularization
* Specify the number of training and dev testing entities with `train_inst` and `dev_inst` respectively
* `epochs` (`-e`): number of training iterations
* `dropout` (`-p`): dropout rate
* `lr` (`-n`): learning rate
* `l2` (`-r`): L2 regularization
* Specify the number of training and dev testing articles with `train_articles` (`-t`) and `dev_articles` (`-d`) respectively
* If not specified, the full dataset will be processed - this may take a LONG time !
* Further parameters to set:
* `labels_discard`: NER label types to discard during training
* `labels_discard` (`-l`): NER label types to discard during training

181
bin/wiki_entity_linking/entity_linker_evaluation.py
View File

@ -1,6 +1,8 @@
# coding: utf-8
from __future__ import unicode_literals
import logging
import random
from tqdm import tqdm
from collections import defaultdict
@ -92,133 +94,110 @@ class BaselineResults(object):
self.random.update_metrics(ent_label, true_entity, random_candidate)
def measure_performance(dev_data, kb, el_pipe, baseline=True, context=True):
if baseline:
baseline_accuracies, counts = measure_baselines(dev_data, kb)
logger.info("Counts: {}".format({k: v for k, v in sorted(counts.items())}))
logger.info(baseline_accuracies.report_performance("random"))
logger.info(baseline_accuracies.report_performance("prior"))
logger.info(baseline_accuracies.report_performance("oracle"))
def measure_performance(dev_data, kb, el_pipe, baseline=True, context=True, dev_limit=None):
counts = dict()
baseline_results = BaselineResults()
context_results = EvaluationResults()
combo_results = EvaluationResults()
if context:
# using only context
el_pipe.cfg["incl_context"] = True
el_pipe.cfg["incl_prior"] = False
results = get_eval_results(dev_data, el_pipe)
logger.info(results.report_metrics("context only"))
# measuring combined accuracy (prior + context)
el_pipe.cfg["incl_context"] = True
el_pipe.cfg["incl_prior"] = True
results = get_eval_results(dev_data, el_pipe)
logger.info(results.report_metrics("context and prior"))
def get_eval_results(data, el_pipe=None):
"""
Evaluate the ent.kb_id_ annotations against the gold standard.
Only evaluate entities that overlap between gold and NER, to isolate the performance of the NEL.
If the docs in the data require further processing with an entity linker, set el_pipe.
"""
docs = []
golds = []
for d, g in tqdm(data, leave=False):
if len(d) > 0:
golds.append(g)
if el_pipe is not None:
docs.append(el_pipe(d))
else:
docs.append(d)
results = EvaluationResults()
for doc, gold in zip(docs, golds):
try:
correct_entries_per_article = dict()
for doc, gold in tqdm(dev_data, total=dev_limit, leave=False, desc='Processing dev data'):
if len(doc) > 0:
correct_ents = dict()
for entity, kb_dict in gold.links.items():
start, end = entity
for gold_kb, value in kb_dict.items():
if value:
# only evaluating on positive examples
offset = _offset(start, end)
correct_entries_per_article[offset] = gold_kb
correct_ents[offset] = gold_kb
for ent in doc.ents:
ent_label = ent.label_
pred_entity = ent.kb_id_
start = ent.start_char
end = ent.end_char
offset = _offset(start, end)
gold_entity = correct_entries_per_article.get(offset, None)
# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
if gold_entity is not None:
results.update_metrics(ent_label, gold_entity, pred_entity)
if baseline:
_add_baseline(baseline_results, counts, doc, correct_ents, kb)
except Exception as e:
logging.error("Error assessing accuracy " + str(e))
if context:
# using only context
el_pipe.cfg["incl_context"] = True
el_pipe.cfg["incl_prior"] = False
_add_eval_result(context_results, doc, correct_ents, el_pipe)
return results
# measuring combined accuracy (prior + context)
el_pipe.cfg["incl_context"] = True
el_pipe.cfg["incl_prior"] = True
_add_eval_result(combo_results, doc, correct_ents, el_pipe)
if baseline:
logger.info("Counts: {}".format({k: v for k, v in sorted(counts.items())}))
logger.info(baseline_results.report_performance("random"))
logger.info(baseline_results.report_performance("prior"))
logger.info(baseline_results.report_performance("oracle"))
if context:
logger.info(context_results.report_metrics("context only"))
logger.info(combo_results.report_metrics("context and prior"))
def measure_baselines(data, kb):
def _add_eval_result(results, doc, correct_ents, el_pipe):
"""
Measure 3 performance baselines: random selection, prior probabilities, and 'oracle' prediction for upper bound.
Evaluate the ent.kb_id_ annotations against the gold standard.
Only evaluate entities that overlap between gold and NER, to isolate the performance of the NEL.
Also return a dictionary of counts by entity label.
"""
counts_d = dict()
baseline_results = BaselineResults()
docs = [d for d, g in data if len(d) > 0]
golds = [g for d, g in data if len(d) > 0]
for doc, gold in zip(docs, golds):
correct_entries_per_article = dict()
for entity, kb_dict in gold.links.items():
start, end = entity
for gold_kb, value in kb_dict.items():
# only evaluating on positive examples
if value:
offset = _offset(start, end)
correct_entries_per_article[offset] = gold_kb
try:
doc = el_pipe(doc)
for ent in doc.ents:
ent_label = ent.label_
start = ent.start_char
end = ent.end_char
offset = _offset(start, end)
gold_entity = correct_entries_per_article.get(offset, None)
gold_entity = correct_ents.get(offset, None)
# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
if gold_entity is not None:
candidates = kb.get_candidates(ent.text)
oracle_candidate = ""
prior_candidate = ""
random_candidate = ""
if candidates:
scores = []
pred_entity = ent.kb_id_
results.update_metrics(ent_label, gold_entity, pred_entity)
for c in candidates:
scores.append(c.prior_prob)
if c.entity_ == gold_entity:
oracle_candidate = c.entity_
except Exception as e:
logging.error("Error assessing accuracy " + str(e))
best_index = scores.index(max(scores))
prior_candidate = candidates[best_index].entity_
random_candidate = random.choice(candidates).entity_
current_count = counts_d.get(ent_label, 0)
counts_d[ent_label] = current_count+1
def _add_baseline(baseline_results, counts, doc, correct_ents, kb):
"""
Measure 3 performance baselines: random selection, prior probabilities, and 'oracle' prediction for upper bound.
Only evaluate entities that overlap between gold and NER, to isolate the performance of the NEL.
"""
for ent in doc.ents:
ent_label = ent.label_
start = ent.start_char
end = ent.end_char
offset = _offset(start, end)
gold_entity = correct_ents.get(offset, None)
baseline_results.update_baselines(
gold_entity,
ent_label,
random_candidate,
prior_candidate,
oracle_candidate,
)
# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
if gold_entity is not None:
candidates = kb.get_candidates(ent.text)
oracle_candidate = ""
prior_candidate = ""
random_candidate = ""
if candidates:
scores = []
return baseline_results, counts_d
for c in candidates:
scores.append(c.prior_prob)
if c.entity_ == gold_entity:
oracle_candidate = c.entity_
best_index = scores.index(max(scores))
prior_candidate = candidates[best_index].entity_
random_candidate = random.choice(candidates).entity_
current_count = counts.get(ent_label, 0)
counts[ent_label] = current_count+1
baseline_results.update_baselines(
gold_entity,
ent_label,
random_candidate,
prior_candidate,
oracle_candidate,
)
def _offset(start, end):

2
bin/wiki_entity_linking/wikidata_pretrain_kb.py
View File

@ -40,7 +40,7 @@ logger = logging.getLogger(__name__)
loc_prior_prob=("Location to file with prior probabilities", "option", "p", Path),
loc_entity_defs=("Location to file with entity definitions", "option", "d", Path),
loc_entity_desc=("Location to file with entity descriptions", "option", "s", Path),
descr_from_wp=("Flag for using wp descriptions not wd", "flag", "wp"),
descr_from_wp=("Flag for using descriptions from WP instead of WD (default False)", "flag", "wp"),
limit_prior=("Threshold to limit lines read from WP for prior probabilities", "option", "lp", int),
limit_train=("Threshold to limit lines read from WP for training set", "option", "lt", int),
limit_wd=("Threshold to limit lines read from WD", "option", "lw", int),

226
bin/wiki_entity_linking/wikidata_train_entity_linker.py
View File

@ -1,5 +1,5 @@
# coding: utf-8
"""Script to take a previously created Knowledge Base and train an entity linking
"""Script that takes a previously created Knowledge Base and trains an entity linking
pipeline. The provided KB directory should hold the kb, the original nlp object and
its vocab used to create the KB, and a few auxiliary files such as the entity definitions,
as created by the script `wikidata_create_kb`.
@ -14,9 +14,16 @@ import logging
import spacy
from pathlib import Path
import plac
from tqdm import tqdm
from bin.wiki_entity_linking import wikipedia_processor
from bin.wiki_entity_linking import TRAINING_DATA_FILE, KB_MODEL_DIR, KB_FILE, LOG_FORMAT, OUTPUT_MODEL_DIR
from bin.wiki_entity_linking import (
TRAINING_DATA_FILE,
KB_MODEL_DIR,
KB_FILE,
LOG_FORMAT,
OUTPUT_MODEL_DIR,
)
from bin.wiki_entity_linking.entity_linker_evaluation import measure_performance
from bin.wiki_entity_linking.kb_creator import read_kb
@ -33,8 +40,8 @@ logger = logging.getLogger(__name__)
dropout=("Dropout to prevent overfitting (default 0.5)", "option", "p", float),
lr=("Learning rate (default 0.005)", "option", "n", float),
l2=("L2 regularization", "option", "r", float),
train_inst=("# training instances (default 90% of all)", "option", "t", int),
dev_inst=("# test instances (default 10% of all)", "option", "d", int),
train_articles=("# training articles (default 90% of all)", "option", "t", int),
dev_articles=("# dev test articles (default 10% of all)", "option", "d", int),
labels_discard=("NER labels to discard (default None)", "option", "l", str),
)
def main(
@ -45,10 +52,15 @@ def main(
dropout=0.5,
lr=0.005,
l2=1e-6,
train_inst=None,
dev_inst=None,
labels_discard=None
train_articles=None,
dev_articles=None,
labels_discard=None,
):
if not output_dir:
logger.warning(
"No output dir specified so no results will be written, are you sure about this ?"
)
logger.info("Creating Entity Linker with Wikipedia and WikiData")
output_dir = Path(output_dir) if output_dir else dir_kb
@ -64,47 +76,57 @@ def main(
# STEP 1 : load the NLP object
logger.info("STEP 1a: Loading model from {}".format(nlp_dir))
nlp = spacy.load(nlp_dir)
logger.info("STEP 1b: Loading KB from {}".format(kb_path))
kb = read_kb(nlp, kb_path)
logger.info(
"Original NLP pipeline has following pipeline components: {}".format(
nlp.pipe_names
)
)
# check that there is a NER component in the pipeline
if "ner" not in nlp.pipe_names:
raise ValueError("The `nlp` object should have a pretrained `ner` component.")
# STEP 2: read the training dataset previously created from WP
logger.info("STEP 2: Reading training dataset from {}".format(training_path))
logger.info("STEP 1b: Loading KB from {}".format(kb_path))
kb = read_kb(nlp, kb_path)
# STEP 2: read the training dataset previously created from WP
logger.info("STEP 2: Reading training & dev dataset from {}".format(training_path))
train_indices, dev_indices = wikipedia_processor.read_training_indices(
training_path
)
logger.info(
"Training set has {} articles, limit set to roughly {} articles per epoch".format(
len(train_indices), train_articles if train_articles else "all"
)
)
logger.info(
"Dev set has {} articles, limit set to rougly {} articles for evaluation".format(
len(dev_indices), dev_articles if dev_articles else "all"
)
)
if dev_articles:
dev_indices = dev_indices[0:dev_articles]
# STEP 3: create and train an entity linking pipe
logger.info(
"STEP 3: Creating and training an Entity Linking pipe for {} epochs".format(
epochs
)
)
if labels_discard:
labels_discard = [x.strip() for x in labels_discard.split(",")]
logger.info("Discarding {} NER types: {}".format(len(labels_discard), labels_discard))
logger.info(
"Discarding {} NER types: {}".format(len(labels_discard), labels_discard)
)
else:
labels_discard = []
train_data = wikipedia_processor.read_training(
nlp=nlp,
entity_file_path=training_path,
dev=False,
limit=train_inst,
kb=kb,
labels_discard=labels_discard
)
# for testing, get all pos instances (independently of KB)
dev_data = wikipedia_processor.read_training(
nlp=nlp,
entity_file_path=training_path,
dev=True,
limit=dev_inst,
kb=None,
labels_discard=labels_discard
)
# STEP 3: create and train an entity linking pipe
logger.info("STEP 3: Creating and training an Entity Linking pipe")
el_pipe = nlp.create_pipe(
name="entity_linker", config={"pretrained_vectors": nlp.vocab.vectors,
"labels_discard": labels_discard}
name="entity_linker",
config={
"pretrained_vectors": nlp.vocab.vectors,
"labels_discard": labels_discard,
},
)
el_pipe.set_kb(kb)
nlp.add_pipe(el_pipe, last=True)
@ -115,78 +137,96 @@ def main(
optimizer.learn_rate = lr
optimizer.L2 = l2
logger.info("Training on {} articles".format(len(train_data)))
logger.info("Dev testing on {} articles".format(len(dev_data)))
# baseline performance on dev data
logger.info("Dev Baseline Accuracies:")
measure_performance(dev_data, kb, el_pipe, baseline=True, context=False)
dev_data = wikipedia_processor.read_el_docs_golds(
nlp=nlp,
entity_file_path=training_path,
dev=True,
line_ids=dev_indices,
kb=kb,
labels_discard=labels_discard,
)
measure_performance(
dev_data, kb, el_pipe, baseline=True, context=False, dev_limit=len(dev_indices)
)
for itn in range(epochs):
random.shuffle(train_data)
random.shuffle(train_indices)
losses = {}
batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
batches = minibatch(train_indices, size=compounding(8.0, 128.0, 1.001))
batchnr = 0
articles_processed = 0
with nlp.disable_pipes(*other_pipes):
# we either process the whole training file, or just a part each epoch
bar_total = len(train_indices)
if train_articles:
bar_total = train_articles
with tqdm(total=bar_total, leave=False, desc=f"Epoch {itn}") as pbar:
for batch in batches:
try:
nlp.update(
examples=batch,
sgd=optimizer,
drop=dropout,
losses=losses,
)
batchnr += 1
except Exception as e:
logger.error("Error updating batch:" + str(e))
if not train_articles or articles_processed < train_articles:
with nlp.disable_pipes("entity_linker"):
train_batch = wikipedia_processor.read_el_docs_golds(
nlp=nlp,
entity_file_path=training_path,
dev=False,
line_ids=batch,
kb=kb,
labels_discard=labels_discard,
)
docs, golds = zip(*train_batch)
try:
with nlp.disable_pipes(*other_pipes):
nlp.update(
docs=docs,
golds=golds,
sgd=optimizer,
drop=dropout,
losses=losses,
)
batchnr += 1
articles_processed += len(docs)
pbar.update(len(docs))
except Exception as e:
logger.error("Error updating batch:" + str(e))
if batchnr > 0:
logging.info("Epoch {}, train loss {}".format(itn, round(losses["entity_linker"] / batchnr, 2)))
measure_performance(dev_data, kb, el_pipe, baseline=False, context=True)
# STEP 4: measure the performance of our trained pipe on an independent dev set
logger.info("STEP 4: Final performance measurement of Entity Linking pipe")
measure_performance(dev_data, kb, el_pipe)
# STEP 5: apply the EL pipe on a toy example
logger.info("STEP 5: Applying Entity Linking to toy example")
run_el_toy_example(nlp=nlp)
logging.info(
"Epoch {} trained on {} articles, train loss {}".format(
itn, articles_processed, round(losses["entity_linker"] / batchnr, 2)
)
)
# re-read the dev_data (data is returned as a generator)
dev_data = wikipedia_processor.read_el_docs_golds(
nlp=nlp,
entity_file_path=training_path,
dev=True,
line_ids=dev_indices,
kb=kb,
labels_discard=labels_discard,
)
measure_performance(
dev_data,
kb,
el_pipe,
baseline=False,
context=True,
dev_limit=len(dev_indices),
)
if output_dir:
# STEP 6: write the NLP pipeline (now including an EL model) to file
logger.info("STEP 6: Writing trained NLP to {}".format(nlp_output_dir))
# STEP 4: write the NLP pipeline (now including an EL model) to file
logger.info(
"Final NLP pipeline has following pipeline components: {}".format(
nlp.pipe_names
)
)
logger.info("STEP 4: Writing trained NLP to {}".format(nlp_output_dir))
nlp.to_disk(nlp_output_dir)
logger.info("Done!")
def check_kb(kb):
for mention in ("Bush", "Douglas Adams", "Homer", "Brazil", "China"):
candidates = kb.get_candidates(mention)
logger.info("generating candidates for " + mention + " :")
for c in candidates:
logger.info(" ".join[
str(c.prior_prob),
c.alias_,
"-->",
c.entity_ + " (freq=" + str(c.entity_freq) + ")"
])
def run_el_toy_example(nlp):
text = (
"In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, "
"Douglas reminds us to always bring our towel, even in China or Brazil. "
"The main character in Doug's novel is the man Arthur Dent, "
"but Dougledydoug doesn't write about George Washington or Homer Simpson."
)
doc = nlp(text)
logger.info(text)
for ent in doc.ents:
logger.info(" ".join(["ent", ent.text, ent.label_, ent.kb_id_]))
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO, format=LOG_FORMAT)
plac.call(main)

61
bin/wiki_entity_linking/wikipedia_processor.py
View File

@ -6,9 +6,6 @@ import bz2
import logging
import random
import json
from tqdm import tqdm
from functools import partial
from spacy.gold import GoldParse
from bin.wiki_entity_linking import wiki_io as io
@ -454,25 +451,40 @@ def _write_training_entities(outputfile, article_id, clean_text, entities):
outputfile.write(line)
def read_training(nlp, entity_file_path, dev, limit, kb, labels_discard=None):
""" This method provides training examples that correspond to the entity annotations found by the nlp object.
def read_training_indices(entity_file_path):
""" This method creates two lists of indices into the training file: one with indices for the
training examples, and one for the dev examples."""
train_indices = []
dev_indices = []
with entity_file_path.open("r", encoding="utf8") as file:
for i, line in enumerate(file):
example = json.loads(line)
article_id = example["article_id"]
clean_text = example["clean_text"]
if is_valid_article(clean_text):
if is_dev(article_id):
dev_indices.append(i)
else:
train_indices.append(i)
return train_indices, dev_indices
def read_el_docs_golds(nlp, entity_file_path, dev, line_ids, kb, labels_discard=None):
""" This method provides training/dev examples that correspond to the entity annotations found by the nlp object.
For training, it will include both positive and negative examples by using the candidate generator from the kb.
For testing (kb=None), it will include all positive examples only."""
if not labels_discard:
labels_discard = []
data = []
num_entities = 0
get_gold_parse = partial(
_get_gold_parse, dev=dev, kb=kb, labels_discard=labels_discard
)
texts = []
entities_list = []
logger.info(
"Reading {} data with limit {}".format("dev" if dev else "train", limit)
)
with entity_file_path.open("r", encoding="utf8") as file:
with tqdm(total=limit, leave=False) as pbar:
for i, line in enumerate(file):
for i, line in enumerate(file):
if i in line_ids:
example = json.loads(line)
article_id = example["article_id"]
clean_text = example["clean_text"]
@ -481,16 +493,15 @@ def read_training(nlp, entity_file_path, dev, limit, kb, labels_discard=None):
if dev != is_dev(article_id) or not is_valid_article(clean_text):
continue
doc = nlp(clean_text)
gold = get_gold_parse(doc, entities)
if gold and len(gold.links) > 0:
data.append((doc, gold))
num_entities += len(gold.links)
pbar.update(len(gold.links))
if limit and num_entities >= limit:
break
logger.info("Read {} entities in {} articles".format(num_entities, len(data)))
return data
texts.append(clean_text)
entities_list.append(entities)
docs = nlp.pipe(texts, batch_size=50)
for doc, entities in zip(docs, entities_list):
gold = _get_gold_parse(doc, entities, dev=dev, kb=kb, labels_discard=labels_discard)
if gold and len(gold.links) > 0:
yield doc, gold
def _get_gold_parse(doc, entities, dev, kb, labels_discard):

8
examples/streamlit_spacy.py
View File

@ -26,12 +26,12 @@ DEFAULT_TEXT = "Mark Zuckerberg is the CEO of Facebook."
HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
@st.cache(ignore_hash=True)
@st.cache(allow_output_mutation=True)
def load_model(name):
return spacy.load(name)
@st.cache(ignore_hash=True)
@st.cache(allow_output_mutation=True)
def process_text(model_name, text):
nlp = load_model(model_name)
return nlp(text)
@ -79,7 +79,9 @@ if "ner" in nlp.pipe_names:
st.header("Named Entities")
st.sidebar.header("Named Entities")
label_set = nlp.get_pipe("ner").labels
labels = st.sidebar.multiselect("Entity labels", label_set, label_set)
labels = st.sidebar.multiselect(
"Entity labels", options=label_set, default=list(label_set)
)
html = displacy.render(doc, style="ent", options={"ents": labels})
# Newlines seem to mess with the rendering
html = html.replace("\n", " ")

35
examples/training/pretrain_kb.py
View File

@ -32,27 +32,24 @@ DESC_WIDTH = 64 # dimension of output entity vectors
@plac.annotations(
vocab_path=("Path to the vocab for the kb", "option", "v", Path),
model=("Model name, should have pretrained word embeddings", "option", "m", str),
model=("Model name, should have pretrained word embeddings", "positional", None, str),
output_dir=("Optional output directory", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int),
)
def main(vocab_path=None, model=None, output_dir=None, n_iter=50):
def main(model=None, output_dir=None, n_iter=50):
"""Load the model, create the KB and pretrain the entity encodings.
Either an nlp model or a vocab is needed to provide access to pretrained word embeddings.
If an output_dir is provided, the KB will be stored there in a file 'kb'.
When providing an nlp model, the updated vocab will also be written to a directory in the output_dir."""
if model is None and vocab_path is None:
raise ValueError("Either the `nlp` model or the `vocab` should be specified.")
The updated vocab will also be written to a directory in the output_dir."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
vocab = Vocab().from_disk(vocab_path)
# create blank Language class with specified vocab
nlp = spacy.blank("en", vocab=vocab)
print("Created blank 'en' model with vocab from '%s'" % vocab_path)
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
# check the length of the nlp vectors
if "vectors" not in nlp.meta or not nlp.vocab.vectors.size:
raise ValueError(
"The `nlp` object should have access to pretrained word vectors, "
" cf. https://spacy.io/usage/models#languages."
)
kb = KnowledgeBase(vocab=nlp.vocab)
@ -103,11 +100,9 @@ def main(vocab_path=None, model=None, output_dir=None, n_iter=50):
print()
print("Saved KB to", kb_path)
# only storing the vocab if we weren't already reading it from file
if not vocab_path:
vocab_path = output_dir / "vocab"
kb.vocab.to_disk(vocab_path)
print("Saved vocab to", vocab_path)
vocab_path = output_dir / "vocab"
kb.vocab.to_disk(vocab_path)
print("Saved vocab to", vocab_path)
print()

3
examples/training/pretrain_textcat.py
View File

@ -131,7 +131,8 @@ def train_textcat(nlp, n_texts, n_iter=10):
train_data = list(zip(train_texts, [{"cats": cats} for cats in train_cats]))
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
pipe_exceptions = ["textcat", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train textcat
optimizer = nlp.begin_training()
textcat.model.tok2vec.from_bytes(tok2vec_weights)

3
examples/training/rehearsal.py
View File

@ -63,7 +63,8 @@ def main(model_name, unlabelled_loc):
optimizer.b2 = 0.0
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
sizes = compounding(1.0, 4.0, 1.001)
with nlp.disable_pipes(*other_pipes):
for itn in range(n_iter):

3
examples/training/train_entity_linker.py
View File

@ -113,7 +113,8 @@ def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
TRAIN_DOCS.append((doc, annotation_clean))
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "entity_linker"]
pipe_exceptions = ["entity_linker", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train entity linker
# reset and initialize the weights randomly
optimizer = nlp.begin_training()

3
examples/training/train_intent_parser.py
View File

@ -124,7 +124,8 @@ def main(model=None, output_dir=None, n_iter=15):
for dep in annotations.get("deps", []):
parser.add_label(dep)
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "parser"]
pipe_exceptions = ["parser", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train parser
optimizer = nlp.begin_training()
for itn in range(n_iter):

3
examples/training/train_ner.py
View File

@ -55,7 +55,8 @@ def main(model=None, output_dir=None, n_iter=100):
ner.add_label(ent[2])
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train NER
# reset and initialize the weights randomly – but only if we're
# training a new model

3
examples/training/train_new_entity_type.py
View File

@ -95,7 +95,8 @@ def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
optimizer = nlp.resume_training()
move_names = list(ner.move_names)
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train NER
sizes = compounding(1.0, 4.0, 1.001)
# batch up the examples using spaCy's minibatch

3
examples/training/train_parser.py
View File

@ -65,7 +65,8 @@ def main(model=None, output_dir=None, n_iter=15):
parser.add_label(dep)
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "parser"]
pipe_exceptions = ["parser", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train parser
optimizer = nlp.begin_training()
for itn in range(n_iter):

3
examples/training/train_textcat.py
View File

@ -68,7 +68,8 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None
train_data = list(zip(train_texts, [{"cats": cats} for cats in train_cats]))
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
pipe_exceptions = ["textcat", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train textcat
optimizer = nlp.begin_training()
if init_tok2vec is not None:

1
setup.cfg
View File

@ -49,6 +49,7 @@ install_requires =
catalogue>=0.0.7,<1.1.0
ml_datasets
# Third-party dependencies
tqdm>=4.38.0,<5.0.0
setuptools
numpy>=1.15.0
plac>=0.9.6,<1.2.0

2
spacy/__init__.py
View File

@ -5,7 +5,7 @@ warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
# These are imported as part of the API
from thinc.util import prefer_gpu, require_gpu
from thinc.api import prefer_gpu, require_gpu
from . import pipeline
from .cli.info import info as cli_info

1
spacy/attrs.pxd
View File

@ -92,3 +92,4 @@ cdef enum attr_id_t:
LANG
ENT_KB_ID = symbols.ENT_KB_ID
MORPH
ENT_ID = symbols.ENT_ID

1
spacy/attrs.pyx
View File

@ -81,6 +81,7 @@ IDS = {
"DEP": DEP,
"ENT_IOB": ENT_IOB,
"ENT_TYPE": ENT_TYPE,
"ENT_ID": ENT_ID,
"ENT_KB_ID": ENT_KB_ID,
"HEAD": HEAD,
"SENT_START": SENT_START,

2
spacy/cli/__init__.py
View File

@ -4,7 +4,7 @@ from .link import link # noqa: F401
from .package import package # noqa: F401
from .profile import profile # noqa: F401
from .train import train # noqa: F401
from .train_from_config import train_from_config_cli # noqa: F401
from .train_from_config import train_from_config_cli # noqa: F401
from .pretrain import pretrain # noqa: F401
from .debug_data import debug_data # noqa: F401
from .evaluate import evaluate # noqa: F401

79
spacy/cli/converters/conllu2json.py
View File

@ -9,8 +9,14 @@ from wasabi import Printer
def conllu2json(
input_data, n_sents=10, append_morphology=False, lang=None, ner_map=None,
merge_subtokens=False, no_print=False, **_
input_data,
n_sents=10,
append_morphology=False,
lang=None,
ner_map=None,
merge_subtokens=False,
no_print=False,
**_
):
"""
Convert conllu files into JSON format for use with train cli.
@ -26,9 +32,13 @@ def conllu2json(
docs = []
raw = ""
sentences = []
conll_data = read_conllx(input_data, append_morphology=append_morphology,
ner_tag_pattern=MISC_NER_PATTERN, ner_map=ner_map,
merge_subtokens=merge_subtokens)
conll_data = read_conllx(
input_data,
append_morphology=append_morphology,
ner_tag_pattern=MISC_NER_PATTERN,
ner_map=ner_map,
merge_subtokens=merge_subtokens,
)
has_ner_tags = has_ner(input_data, ner_tag_pattern=MISC_NER_PATTERN)
for i, example in enumerate(conll_data):
raw += example.text
@ -72,20 +82,28 @@ def has_ner(input_data, ner_tag_pattern):
return False
def read_conllx(input_data, append_morphology=False, merge_subtokens=False,
ner_tag_pattern="", ner_map=None):
def read_conllx(
input_data,
append_morphology=False,
merge_subtokens=False,
ner_tag_pattern="",
ner_map=None,
):
""" Yield examples, one for each sentence """
vocab = Language.Defaults.create_vocab() # need vocab to make a minimal Doc
i = 0
vocab = Language.Defaults.create_vocab() # need vocab to make a minimal Doc
for sent in input_data.strip().split("\n\n"):
lines = sent.strip().split("\n")
if lines:
while lines[0].startswith("#"):
lines.pop(0)
example = example_from_conllu_sentence(vocab, lines,
ner_tag_pattern, merge_subtokens=merge_subtokens,
append_morphology=append_morphology,
ner_map=ner_map)
example = example_from_conllu_sentence(
vocab,
lines,
ner_tag_pattern,
merge_subtokens=merge_subtokens,
append_morphology=append_morphology,
ner_map=ner_map,
)
yield example
@ -157,8 +175,14 @@ def create_json_doc(raw, sentences, id_):
return doc
def example_from_conllu_sentence(vocab, lines, ner_tag_pattern,
merge_subtokens=False, append_morphology=False, ner_map=None):
def example_from_conllu_sentence(
vocab,
lines,
ner_tag_pattern,
merge_subtokens=False,
append_morphology=False,
ner_map=None,
):
"""Create an Example from the lines for one CoNLL-U sentence, merging
subtokens and appending morphology to tags if required.
@ -182,7 +206,6 @@ def example_from_conllu_sentence(vocab, lines, ner_tag_pattern,
in_subtok = False
for i in range(len(lines)):
line = lines[i]
subtok_lines = []
parts = line.split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
if "." in id_:
@ -212,7 +235,7 @@ def example_from_conllu_sentence(vocab, lines, ner_tag_pattern,
subtok_word = ""
in_subtok = False
id_ = int(id_) - 1
head = (int(head) - 1) if head != "0" else id_
head = (int(head) - 1) if head not in ("0", "_") else id_
tag = pos if tag == "_" else tag
morph = morph if morph != "_" else ""
dep = "ROOT" if dep == "root" else dep
@ -266,9 +289,17 @@ def example_from_conllu_sentence(vocab, lines, ner_tag_pattern,
if space:
raw += " "
example = Example(doc=raw)
example.set_token_annotation(ids=ids, words=words, tags=tags, pos=pos,
morphs=morphs, lemmas=lemmas, heads=heads,
deps=deps, entities=ents)
example.set_token_annotation(
ids=ids,
words=words,
tags=tags,
pos=pos,
morphs=morphs,
lemmas=lemmas,
heads=heads,
deps=deps,
entities=ents,
)
return example
@ -280,7 +311,7 @@ def merge_conllu_subtokens(lines, doc):
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
if "-" in id_:
subtok_start, subtok_end = id_.split("-")
subtok_span = doc[int(subtok_start) - 1:int(subtok_end)]
subtok_span = doc[int(subtok_start) - 1 : int(subtok_end)]
subtok_spans.append(subtok_span)
# create merged tag, morph, and lemma values
tags = []
@ -292,7 +323,7 @@ def merge_conllu_subtokens(lines, doc):
if token._.merged_morph:
for feature in token._.merged_morph.split("|"):
field, values = feature.split("=", 1)
if not field in morphs:
if field not in morphs:
morphs[field] = set()
for value in values.split(","):
morphs[field].add(value)
@ -306,7 +337,9 @@ def merge_conllu_subtokens(lines, doc):
token._.merged_lemma = " ".join(lemmas)
token.tag_ = "_".join(tags)
token._.merged_morph = "|".join(sorted(morphs.values()))
token._.merged_spaceafter = True if subtok_span[-1].whitespace_ else False
token._.merged_spaceafter = (
True if subtok_span[-1].whitespace_ else False
)
with doc.retokenize() as retokenizer:
for span in subtok_spans:

24
spacy/cli/debug_data.py
View File

@ -166,6 +166,7 @@ def debug_data(
has_low_data_warning = False
has_no_neg_warning = False
has_ws_ents_error = False
has_punct_ents_warning = False
msg.divider("Named Entity Recognition")
msg.info(
@ -190,6 +191,10 @@ def debug_data(
msg.fail(f"{gold_train_data['ws_ents']} invalid whitespace entity spans")
has_ws_ents_error = True
if gold_train_data["punct_ents"]:
msg.warn(f"{gold_train_data['punct_ents']} entity span(s) with punctuation")
has_punct_ents_warning = True
for label in new_labels:
if label_counts[label] <= NEW_LABEL_THRESHOLD:
msg.warn(
@ -209,6 +214,8 @@ def debug_data(
msg.good("Examples without occurrences available for all labels")
if not has_ws_ents_error:
msg.good("No entities consisting of or starting/ending with whitespace")
if not has_punct_ents_warning:
msg.good("No entities consisting of or starting/ending with punctuation")
if has_low_data_warning:
msg.text(
@ -229,6 +236,12 @@ def debug_data(
"with whitespace characters are considered invalid."
)
if has_punct_ents_warning:
msg.text(
"Entity spans consisting of or starting/ending "
"with punctuation can not be trained with a noise level > 0."
)
if "textcat" in pipeline:
msg.divider("Text Classification")
labels = [label for label in gold_train_data["cats"]]
@ -446,6 +459,7 @@ def _compile_gold(examples, pipeline):
"words": Counter(),
"roots": Counter(),
"ws_ents": 0,
"punct_ents": 0,
"n_words": 0,
"n_misaligned_words": 0,
"n_sents": 0,
@ -469,6 +483,16 @@ def _compile_gold(examples, pipeline):
if label.startswith(("B-", "U-", "L-")) and doc[i].is_space:
# "Illegal" whitespace entity
data["ws_ents"] += 1
if label.startswith(("B-", "U-", "L-")) and doc[i].text in [
".",
"'",
"!",
"?",
",",
]:
# punctuation entity: could be replaced by whitespace when training with noise,
# so add a warning to alert the user to this unexpected side effect.
data["punct_ents"] += 1
if label.startswith(("B-", "U-")):
combined_label = label.split("-")[1]
data["ner"][combined_label] += 1

20
spacy/cli/pretrain.py
View File

@ -4,14 +4,12 @@ import time
import re
from collections import Counter
from pathlib import Path
from thinc.layers import Linear, Maxout
from thinc.util import prefer_gpu
from thinc.api import Linear, Maxout, chain, list2array, prefer_gpu
from thinc.api import CosineDistance, L2Distance
from wasabi import msg
import srsly
from thinc.layers import chain, list2array
from thinc.loss import CosineDistance, L2Distance
from spacy.gold import Example
from ..gold import Example
from ..errors import Errors
from ..tokens import Doc
from ..attrs import ID, HEAD
@ -28,7 +26,7 @@ def pretrain(
vectors_model: ("Name or path to spaCy model with vectors to learn from", "positional", None, str),
output_dir: ("Directory to write models to on each epoch", "positional", None, str),
width: ("Width of CNN layers", "option", "cw", int) = 96,
depth: ("Depth of CNN layers", "option", "cd", int) = 4,
conv_depth: ("Depth of CNN layers", "option", "cd", int) = 4,
bilstm_depth: ("Depth of BiLSTM layers (requires PyTorch)", "option", "lstm", int) = 0,
cnn_pieces: ("Maxout size for CNN layers. 1 for Mish", "option", "cP", int) = 3,
sa_depth: ("Depth of self-attention layers", "option", "sa", int) = 0,
@ -77,9 +75,15 @@ def pretrain(
msg.info("Using GPU" if has_gpu else "Not using GPU")
output_dir = Path(output_dir)
if output_dir.exists() and [p for p in output_dir.iterdir()]:
msg.warn(
"Output directory is not empty",
"It is better to use an empty directory or refer to a new output path, "
"then the new directory will be created for you.",
)
if not output_dir.exists():
output_dir.mkdir()
msg.good("Created output directory")
msg.good(f"Created output directory: {output_dir}")
srsly.write_json(output_dir / "config.json", config)
msg.good("Saved settings to config.json")
@ -107,7 +111,7 @@ def pretrain(
Tok2Vec(
width,
embed_rows,
conv_depth=depth,
conv_depth=conv_depth,
pretrained_vectors=pretrained_vectors,
bilstm_depth=bilstm_depth, # Requires PyTorch. Experimental.
subword_features=not use_chars, # Set to False for Chinese etc

135
spacy/cli/train.py
View File

@ -1,7 +1,7 @@
import os
import tqdm
from pathlib import Path
from thinc.backends import use_ops
from thinc.api import use_ops
from timeit import default_timer as timer
import shutil
import srsly
@ -10,6 +10,7 @@ import contextlib
import random
from ..util import create_default_optimizer
from ..util import use_gpu as set_gpu
from ..attrs import PROB, IS_OOV, CLUSTER, LANG
from ..gold import GoldCorpus
from .. import util
@ -26,6 +27,14 @@ def train(
base_model: ("Name of model to update (optional)", "option", "b", str) = None,
pipeline: ("Comma-separated names of pipeline components", "option", "p", str) = "tagger,parser,ner",
vectors: ("Model to load vectors from", "option", "v", str) = None,
replace_components: ("Replace components from base model", "flag", "R", bool) = False,
width: ("Width of CNN layers of Tok2Vec component", "option", "cw", int) = 96,
conv_depth: ("Depth of CNN layers of Tok2Vec component", "option", "cd", int) = 4,
cnn_window: ("Window size for CNN layers of Tok2Vec component", "option", "cW", int) = 1,
cnn_pieces: ("Maxout size for CNN layers of Tok2Vec component. 1 for Mish", "option", "cP", int) = 3,
use_chars: ("Whether to use character-based embedding of Tok2Vec component", "flag", "chr", bool) = False,
bilstm_depth: ("Depth of BiLSTM layers of Tok2Vec component (requires PyTorch)", "option", "lstm", int) = 0,
embed_rows: ("Number of embedding rows of Tok2Vec component", "option", "er", int) = 2000,
n_iter: ("Number of iterations", "option", "n", int) = 30,
n_early_stopping: ("Maximum number of training epochs without dev accuracy improvement", "option", "ne", int) = None,
n_examples: ("Number of examples", "option", "ns", int) = 0,
@ -80,6 +89,7 @@ def train(
)
if not output_path.exists():
output_path.mkdir()
msg.good(f"Created output directory: {output_path}")
tag_map = {}
if tag_map_path is not None:
@ -113,6 +123,21 @@ def train(
# training starts from a blank model, intitalize the language class.
pipeline = [p.strip() for p in pipeline.split(",")]
msg.text(f"Training pipeline: {pipeline}")
disabled_pipes = None
pipes_added = False
msg.text(f"Training pipeline: {pipeline}")
if use_gpu >= 0:
activated_gpu = None
try:
activated_gpu = set_gpu(use_gpu)
except Exception as e:
msg.warn(f"Exception: {e}")
if activated_gpu is not None:
msg.text(f"Using GPU: {use_gpu}")
else:
msg.warn(f"Unable to activate GPU: {use_gpu}")
msg.text("Using CPU only")
use_gpu = -1
if base_model:
msg.text(f"Starting with base model '{base_model}'")
nlp = util.load_model(base_model)
@ -122,20 +147,24 @@ def train(
f"specified as `lang` argument ('{lang}') ",
exits=1,
)
nlp.disable_pipes([p for p in nlp.pipe_names if p not in pipeline])
for pipe in pipeline:
pipe_cfg = {}
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
if pipe not in nlp.pipe_names:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
else:
pipe_cfg = {}
msg.text(f"Adding component to base model '{pipe}'")
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
pipes_added = True
elif replace_components:
msg.text(f"Replacing component from base model '{pipe}'")
nlp.replace_pipe(pipe, nlp.create_pipe(pipe, config=pipe_cfg))
pipes_added = True
else:
if pipe == "textcat":
textcat_cfg = nlp.get_pipe("textcat").cfg
@ -144,11 +173,6 @@ def train(
"architecture": textcat_cfg["architecture"],
"positive_label": textcat_cfg["positive_label"],
}
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
if base_cfg != pipe_cfg:
msg.fail(
f"The base textcat model configuration does"
@ -156,6 +180,10 @@ def train(
f"Existing cfg: {base_cfg}, provided cfg: {pipe_cfg}",
exits=1,
)
msg.text(f"Extending component from base model '{pipe}'")
disabled_pipes = nlp.disable_pipes(
[p for p in nlp.pipe_names if p not in pipeline]
)
else:
msg.text(f"Starting with blank model '{lang}'")
lang_cls = util.get_lang_class(lang)
@ -198,13 +226,20 @@ def train(
corpus = GoldCorpus(train_path, dev_path, limit=n_examples)
n_train_words = corpus.count_train()
if base_model:
if base_model and not pipes_added:
# Start with an existing model, use default optimizer
optimizer = create_default_optimizer()
else:
# Start with a blank model, call begin_training
optimizer = nlp.begin_training(lambda: corpus.train_examples, device=use_gpu)
cfg = {"device": use_gpu}
cfg["conv_depth"] = conv_depth
cfg["token_vector_width"] = width
cfg["bilstm_depth"] = bilstm_depth
cfg["cnn_maxout_pieces"] = cnn_pieces
cfg["embed_size"] = embed_rows
cfg["conv_window"] = cnn_window
cfg["subword_features"] = not use_chars
optimizer = nlp.begin_training(lambda: corpus.train_tuples, **cfg)
nlp._optimizer = None
# Load in pretrained weights
@ -214,7 +249,7 @@ def train(
# Verify textcat config
if "textcat" in pipeline:
textcat_labels = nlp.get_pipe("textcat").cfg["labels"]
textcat_labels = nlp.get_pipe("textcat").cfg.get("labels", [])
if textcat_positive_label and textcat_positive_label not in textcat_labels:
msg.fail(
f"The textcat_positive_label (tpl) '{textcat_positive_label}' "
@ -327,12 +362,22 @@ def train(
for batch in util.minibatch_by_words(train_data, size=batch_sizes):
if not batch:
continue
nlp.update(
batch,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses,
)
docs, golds = zip(*batch)
try:
nlp.update(
docs,
golds,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses,
)
except ValueError as e:
msg.warn("Error during training")
if init_tok2vec:
msg.warn(
"Did you provide the same parameters during 'train' as during 'pretrain'?"
)
msg.fail(f"Original error message: {e}", exits=1)
if raw_text:
# If raw text is available, perform 'rehearsal' updates,
# which use unlabelled data to reduce overfitting.
@ -396,11 +441,16 @@ def train(
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["accuracy"] = scorer.scores
meta.setdefault("accuracy", {})
for component in nlp.pipe_names:
for metric in _get_metrics(component):
meta["accuracy"][metric] = scorer.scores[metric]
else:
meta.setdefault("beam_accuracy", {})
meta.setdefault("beam_speed", {})
meta["beam_accuracy"][beam_width] = scorer.scores
for component in nlp.pipe_names:
for metric in _get_metrics(component):
meta["beam_accuracy"][metric] = scorer.scores[metric]
meta["beam_speed"][beam_width] = {
"nwords": nwords,
"cpu": cpu_wps,
@ -453,13 +503,19 @@ def train(
f"Best score = {best_score}; Final iteration score = {current_score}"
)
break
except Exception as e:
msg.warn(f"Aborting and saving final best model. Encountered exception: {e}")
finally:
best_pipes = nlp.pipe_names
if disabled_pipes:
disabled_pipes.restore()
with nlp.use_params(optimizer.averages):
final_model_path = output_path / "model-final"
nlp.to_disk(final_model_path)
final_meta = srsly.read_json(output_path / "model-final" / "meta.json")
msg.good("Saved model to output directory", final_model_path)
with msg.loading("Creating best model..."):
best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
best_model_path = _collate_best_model(final_meta, output_path, best_pipes)
msg.good("Created best model", best_model_path)
@ -519,15 +575,14 @@ def _load_pretrained_tok2vec(nlp, loc):
def _collate_best_model(meta, output_path, components):
bests = {}
meta.setdefault("accuracy", {})
for component in components:
bests[component] = _find_best(output_path, component)
best_dest = output_path / "model-best"
shutil.copytree(str(output_path / "model-final"), str(best_dest))
for component, best_component_src in bests.items():
shutil.rmtree(str(best_dest / component))
shutil.copytree(
str(best_component_src / component), str(best_dest / component)
)
shutil.copytree(str(best_component_src / component), str(best_dest / component))
accs = srsly.read_json(best_component_src / "accuracy.json")
for metric in _get_metrics(component):
meta["accuracy"][metric] = accs[metric]
@ -550,13 +605,15 @@ def _find_best(experiment_dir, component):
def _get_metrics(component):
if component == "parser":
return ("las", "uas", "token_acc", "sent_f")
return ("las", "uas", "las_per_type", "token_acc", "sent_f")
elif component == "tagger":
return ("tags_acc",)
elif component == "ner":
return ("ents_f", "ents_p", "ents_r")
return ("ents_f", "ents_p", "ents_r", "enty_per_type")
elif component == "sentrec":
return ("sent_f", "sent_p", "sent_r")
elif component == "textcat":
return ("textcat_score",)
return ("token_acc",)
@ -568,8 +625,12 @@ def _configure_training_output(pipeline, use_gpu, has_beam_widths):
row_head.extend(["Tag Loss ", " Tag % "])
output_stats.extend(["tag_loss", "tags_acc"])
elif pipe == "parser":
row_head.extend(["Dep Loss ", " UAS ", " LAS ", "Sent P", "Sent R", "Sent F"])
output_stats.extend(["dep_loss", "uas", "las", "sent_p", "sent_r", "sent_f"])
row_head.extend(
["Dep Loss ", " UAS ", " LAS ", "Sent P", "Sent R", "Sent F"]
)
output_stats.extend(
["dep_loss", "uas", "las", "sent_p", "sent_r", "sent_f"]
)
elif pipe == "ner":
row_head.extend(["NER Loss ", "NER P ", "NER R ", "NER F "])
output_stats.extend(["ner_loss", "ents_p", "ents_r", "ents_f"])

36
spacy/cli/train_from_config.py
View File

@ -1,19 +1,20 @@
from typing import Optional, Dict, List, Union, Sequence
import plac
from thinc.util import require_gpu
from wasabi import msg
from pathlib import Path
import thinc
import thinc.schedules
from thinc.model import Model
from spacy.gold import GoldCorpus
import spacy
from spacy.pipeline.tok2vec import Tok2VecListener
from typing import Optional, Dict, List, Union, Sequence
from thinc.api import Model
from pydantic import BaseModel, FilePath, StrictInt
import tqdm
from ..ml import component_models
from .. import util
# TODO: relative imports?
import spacy
from spacy.gold import GoldCorpus
from spacy.pipeline.tok2vec import Tok2VecListener
from spacy.ml import component_models
from spacy import util
registry = util.registry
@ -153,10 +154,9 @@ def create_tb_parser_model(
hidden_width: StrictInt = 64,
maxout_pieces: StrictInt = 3,
):
from thinc.layers import Linear, chain, list2array
from thinc.api import Linear, chain, list2array, use_ops, zero_init
from spacy.ml._layers import PrecomputableAffine
from spacy.syntax._parser_model import ParserModel
from thinc.api import use_ops, zero_init
token_vector_width = tok2vec.get_dim("nO")
tok2vec = chain(tok2vec, list2array())
@ -221,13 +221,9 @@ def train_from_config_cli(
def train_from_config(
config_path,
data_paths,
raw_text=None,
meta_path=None,
output_path=None,
config_path, data_paths, raw_text=None, meta_path=None, output_path=None,
):
msg.info("Loading config from: {}".format(config_path))
msg.info(f"Loading config from: {config_path}")
config = util.load_from_config(config_path, create_objects=True)
use_gpu = config["training"]["use_gpu"]
if use_gpu >= 0:
@ -241,9 +237,7 @@ def train_from_config(
msg.info("Loading training corpus")
corpus = GoldCorpus(data_paths["train"], data_paths["dev"], limit=limit)
msg.info("Initializing the nlp pipeline")
nlp.begin_training(
lambda: corpus.train_examples, device=use_gpu
)
nlp.begin_training(lambda: corpus.train_examples, device=use_gpu)
train_batches = create_train_batches(nlp, corpus, config["training"])
evaluate = create_evaluation_callback(nlp, optimizer, corpus, config["training"])
@ -260,7 +254,7 @@ def train_from_config(
config["training"]["eval_frequency"],
)
msg.info("Training. Initial learn rate: {}".format(optimizer.learn_rate))
msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
print_row = setup_printer(config)
try:
@ -414,7 +408,7 @@ def subdivide_batch(batch):
def setup_printer(config):
score_cols = config["training"]["scores"]
score_widths = [max(len(col), 6) for col in score_cols]
loss_cols = ["Loss {}".format(pipe) for pipe in config["nlp"]["pipeline"]]
loss_cols = [f"Loss {pipe}" for pipe in config["nlp"]["pipeline"]]
loss_widths = [max(len(col), 8) for col in loss_cols]
table_header = ["#"] + loss_cols + score_cols + ["Score"]
table_header = [col.upper() for col in table_header]

2
spacy/compat.py
View File

@ -30,7 +30,7 @@ try:
except ImportError:
cupy = None
from thinc.optimizers import Optimizer # noqa: F401
from thinc.api import Optimizer # noqa: F401
pickle = pickle
copy_reg = copy_reg

3
spacy/displacy/__init__.py
View File

@ -51,9 +51,10 @@ def render(
html = RENDER_WRAPPER(html)
if jupyter or (jupyter is None and is_in_jupyter()):
# return HTML rendered by IPython display()
# See #4840 for details on span wrapper to disable mathjax
from IPython.core.display import display, HTML
return display(HTML(html))
return display(HTML('<span class="tex2jax_ignore">{}</span>'.format(html)))
return html

1
spacy/displacy/templates.py
View File

@ -1,4 +1,3 @@
# Setting explicit height and max-width: none on the SVG is required for
# Jupyter to render it properly in a cell

14
spacy/errors.py
View File

@ -75,10 +75,9 @@ class Warnings(object):
W015 = ("As of v2.1.0, the use of keyword arguments to exclude fields from "
"being serialized or deserialized is deprecated. Please use the "
"`exclude` argument instead. For example: exclude=['{arg}'].")
W016 = ("The keyword argument `n_threads` on the is now deprecated, as "
"the v2.x models cannot release the global interpreter lock. "
"Future versions may introduce a `n_process` argument for "
"parallel inference via multiprocessing.")
W016 = ("The keyword argument `n_threads` is now deprecated. As of v2.2.2, "
"the argument `n_process` controls parallel inference via "
"multiprocessing.")
W017 = ("Alias '{alias}' already exists in the Knowledge Base.")
W018 = ("Entity '{entity}' already exists in the Knowledge Base - "
"ignoring the duplicate entry.")
@ -170,7 +169,8 @@ class Errors(object):
"and satisfies the correct annotations specified in the GoldParse. "
"For example, are all labels added to the model? If you're "
"training a named entity recognizer, also make sure that none of "
"your annotated entity spans have leading or trailing whitespace. "
"your annotated entity spans have leading or trailing whitespace "
"or punctuation. "
"You can also use the experimental `debug-data` command to "
"validate your JSON-formatted training data. For details, run:\n"
"python -m spacy debug-data --help")
@ -541,8 +541,8 @@ class Errors(object):
E997 = ("Tokenizer special cases are not allowed to modify the text. "
"This would map '{chunk}' to '{orth}' given token attributes "
"'{token_attrs}'.")
E998 = ("Can only create GoldParse's from Example's without a Doc, "
"if get_gold_parses() is called with a Vocab object.")
E998 = ("Can only create GoldParse objects from Example objects without a "
"Doc if get_gold_parses() is called with a Vocab object.")
E999 = ("Encountered an unexpected format for the dictionary holding "
"gold annotations: {gold_dict}")

1
spacy/glossary.py
View File

@ -1,4 +1,3 @@
def explain(term):
"""Get a description for a given POS tag, dependency label or entity type.

4
spacy/gold.pxd
View File

@ -1,6 +1,6 @@
from cymem.cymem cimport Pool
from spacy.tokens import Doc
from .tokens import Doc
from .typedefs cimport attr_t
from .syntax.transition_system cimport Transition
@ -65,5 +65,3 @@ cdef class Example:
cdef public TokenAnnotation token_annotation
cdef public DocAnnotation doc_annotation
cdef public object goldparse

5
spacy/kb.pxd
View File

@ -6,7 +6,7 @@ from libcpp.vector cimport vector
from libc.stdint cimport int32_t, int64_t
from libc.stdio cimport FILE
from spacy.vocab cimport Vocab
from .vocab cimport Vocab
from .typedefs cimport hash_t
from .structs cimport KBEntryC, AliasC
@ -113,7 +113,7 @@ cdef class KnowledgeBase:
return new_index
cdef inline void _create_empty_vectors(self, hash_t dummy_hash) nogil:
"""
"""
Initializing the vectors and making sure the first element of each vector is a dummy,
because the PreshMap maps pointing to indices in these vectors can not contain 0 as value
cf. https://github.com/explosion/preshed/issues/17
@ -169,4 +169,3 @@ cdef class Reader:
cdef int read_alias(self, int64_t* entry_index, float* prob) except -1
cdef int _read(self, void* value, size_t size) except -1

1
spacy/lang/af/stop_words.py
View File

@ -1,4 +1,3 @@
# Source: https://github.com/stopwords-iso/stopwords-af
STOP_WORDS = set(

1
spacy/lang/bg/stop_words.py
View File

@ -1,4 +1,3 @@
# Source: https://github.com/Alir3z4/stop-words
STOP_WORDS = set(

1
spacy/lang/bn/examples.py
View File

@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/bn/stop_words.py
View File

@ -1,4 +1,3 @@
STOP_WORDS = set(
"""
অতএব অথচ অথবা অনুযায়ী অনেক অনেকে অনেকেই অন্তত অবধি অবশ্য অর্থাৎ অন্য অনুযায়ী অর্ধভাগে

1
spacy/lang/ca/examples.py
View File

@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

12
spacy/lang/char_classes.py
View File

@ -14,6 +14,17 @@ _tamil = r"\u0B80-\u0BFF"
_telugu = r"\u0C00-\u0C7F"
# from the final table in: https://en.wikipedia.org/wiki/CJK_Unified_Ideographs
_cjk = (
r"\u4E00-\u62FF\u6300-\u77FF\u7800-\u8CFF\u8D00-\u9FFF\u3400-\u4DBF"
r"\U00020000-\U000215FF\U00021600-\U000230FF\U00023100-\U000245FF"
r"\U00024600-\U000260FF\U00026100-\U000275FF\U00027600-\U000290FF"
r"\U00029100-\U0002A6DF\U0002A700-\U0002B73F\U0002B740-\U0002B81F"
r"\U0002B820-\U0002CEAF\U0002CEB0-\U0002EBEF\u2E80-\u2EFF\u2F00-\u2FDF"
r"\u2FF0-\u2FFF\u3000-\u303F\u31C0-\u31EF\u3200-\u32FF\u3300-\u33FF"
r"\uF900-\uFAFF\uFE30-\uFE4F\U0001F200-\U0001F2FF\U0002F800-\U0002FA1F"
)
# Latin standard
_latin_u_standard = r"A-Z"
_latin_l_standard = r"a-z"
@ -212,6 +223,7 @@ _uncased = (
+ _tamil
+ _telugu
+ _hangul
+ _cjk
)
ALPHA = group_chars(LATIN + _russian + _tatar + _greek + _ukrainian + _uncased)

1
spacy/lang/cs/stop_words.py
View File

@ -1,4 +1,3 @@
# Source: https://github.com/Alir3z4/stop-words
STOP_WORDS = set(

1
spacy/lang/da/examples.py
View File

@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/de/examples.py
View File

@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

10
spacy/lang/de/stop_words.py
View File

@ -1,4 +1,3 @@
STOP_WORDS = set(
"""
á a ab aber ach acht achte achten achter achtes ag alle allein allem allen
@ -19,14 +18,14 @@ dort drei drin dritte dritten dritter drittes du durch durchaus dürfen dürft
durfte durften
eben ebenso ehrlich eigen eigene eigenen eigener eigenes ein einander eine
einem einen einer eines einigeeinigen einiger einiges einmal einmaleins elf en
einem einen einer eines einige einigen einiger einiges einmal einmaleins elf en
ende endlich entweder er erst erste ersten erster erstes es etwa etwas euch
früher fünf fünfte fünften fünfter fünftes für
gab ganz ganze ganzen ganzer ganzes gar gedurft gegen gegenüber gehabt gehen
geht gekannt gekonnt gemacht gemocht gemusst genug gerade gern gesagt geschweige
gewesen gewollt geworden gibt ging gleich gott gross groß grosse große grossen
gewesen gewollt geworden gibt ging gleich gross groß grosse große grossen
großen grosser großer grosses großes gut gute guter gutes
habe haben habt hast hat hatte hätte hatten hätten heisst heißt her heute hier
@ -44,9 +43,8 @@ kleines kommen kommt können könnt konnte könnte konnten kurz
lang lange leicht leider lieber los
machen macht machte mag magst man manche manchem manchen mancher manches mehr
mein meine meinem meinen meiner meines mensch menschen mich mir mit mittel
mochte möchte mochten mögen möglich mögt morgen muss muß müssen musst müsst
musste mussten
mein meine meinem meinen meiner meines mich mir mit mittel mochte möchte mochten
mögen möglich mögt morgen muss muß müssen musst müsst musste mussten
na nach nachdem nahm natürlich neben nein neue neuen neun neunte neunten neunter
neuntes nicht nichts nie niemand niemandem niemanden noch nun nur

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

@ -1,5 +1,5 @@
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .tag_map_general import TAG_MAP
from ..tag_map import TAG_MAP
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from .lemmatizer import GreekLemmatizer

1
spacy/lang/el/get_pos_from_wiktionary.py
View File

@ -1,4 +1,3 @@
def get_pos_from_wiktionary():
import re
from gensim.corpora.wikicorpus import extract_pages

1
spacy/lang/el/norm_exceptions.py
View File

@ -1,4 +1,3 @@
# These exceptions are used to add NORM values based on a token's ORTH value.
# Norms are only set if no alternative is provided in the tokenizer exceptions.

1
spacy/lang/el/stop_words.py
View File

@ -1,4 +1,3 @@
# Stop words
# Link to greek stop words: https://www.translatum.gr/forum/index.php?topic=3550.0?topic=3550.0
STOP_WORDS = set(

0
spacy/lang/el/tag_map.py → spacy/lang/el/tag_map_fine.py
View File

24
spacy/lang/el/tag_map_general.py
View File

@ -1,24 +0,0 @@
from ...symbols import POS, ADV, NOUN, ADP, PRON, SCONJ, PROPN, DET, SYM, INTJ
from ...symbols import PUNCT, NUM, AUX, X, ADJ, VERB, PART, SPACE, CCONJ
TAG_MAP = {
"ADJ": {POS: ADJ},
"ADV": {POS: ADV},
"INTJ": {POS: INTJ},
"NOUN": {POS: NOUN},
"PROPN": {POS: PROPN},
"VERB": {POS: VERB},
"ADP": {POS: ADP},
"CCONJ": {POS: CCONJ},
"SCONJ": {POS: SCONJ},
"PART": {POS: PART},
"PUNCT": {POS: PUNCT},
"SYM": {POS: SYM},
"NUM": {POS: NUM},
"PRON": {POS: PRON},
"AUX": {POS: AUX},
"SPACE": {POS: SPACE},
"DET": {POS: DET},
"X": {POS: X},
}

1
spacy/lang/en/examples.py
View File

@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/en/norm_exceptions.py
View File

@ -1,4 +1,3 @@
_exc = {
# Slang and abbreviations
"cos": "because",

1
spacy/lang/en/stop_words.py
View File

@ -1,4 +1,3 @@
# Stop words
STOP_WORDS = set(
"""

1
spacy/lang/es/examples.py
View File

@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/es/stop_words.py
View File

@ -1,4 +1,3 @@
STOP_WORDS = set(
"""
actualmente acuerdo adelante ademas además adrede afirmó agregó ahi ahora ahí

1
spacy/lang/et/stop_words.py
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@ -1,4 +1,3 @@
# Source: https://github.com/stopwords-iso/stopwords-et
STOP_WORDS = set(

1
spacy/lang/fa/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/fa/generate_verbs_exc.py
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@ -1,4 +1,3 @@
verb_roots = """
#هست
آخت#آهنج

1
spacy/lang/fa/stop_words.py
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@ -1,4 +1,3 @@
# Stop words from HAZM package
STOP_WORDS = set(
"""

9
spacy/lang/fi/punctuation.py
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@ -1,9 +1,10 @@
from ..char_classes import LIST_ELLIPSES, LIST_ICONS
from ..char_classes import LIST_ELLIPSES, LIST_ICONS, LIST_HYPHENS
from ..char_classes import CONCAT_QUOTES, ALPHA, ALPHA_LOWER, ALPHA_UPPER
from ..punctuation import TOKENIZER_SUFFIXES
_quotes = CONCAT_QUOTES.replace("'", "")
DASHES = "|".join(x for x in LIST_HYPHENS if x != "-")
_infixes = (
LIST_ELLIPSES
@ -11,11 +12,9 @@ _infixes = (
+ [
r"(?<=[{al}])\.(?=[{au}])".format(al=ALPHA_LOWER, au=ALPHA_UPPER),
r"(?<=[{a}])[,!?](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])[:<>=](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])([{q}\)\]\(\[])(?=[{a}])".format(a=ALPHA, q=_quotes),
r"(?<=[{a}])--(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])(?:{d})(?=[{a}])".format(a=ALPHA, d=DASHES),
r"(?<=[{a}0-9])[<>=/](?=[{a}])".format(a=ALPHA),
]
)

1
spacy/lang/fi/stop_words.py
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@ -1,4 +1,3 @@
# Source https://github.com/stopwords-iso/stopwords-fi/blob/master/stopwords-fi.txt
# Reformatted with some minor corrections
STOP_WORDS = set(

3
spacy/lang/fi/tokenizer_exceptions.py
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@ -28,6 +28,9 @@ for exc_data in [
{ORTH: "myöh.", LEMMA: "myöhempi"},
{ORTH: "n.", LEMMA: "noin"},
{ORTH: "nimim.", LEMMA: "nimimerkki"},
{ORTH: "n:o", LEMMA: "numero"},
{ORTH: "N:o", LEMMA: "numero"},
{ORTH: "nro", LEMMA: "numero"},
{ORTH: "ns.", LEMMA: "niin sanottu"},
{ORTH: "nyk.", LEMMA: "nykyinen"},
{ORTH: "oik.", LEMMA: "oikealla"},

1
spacy/lang/fr/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/fr/stop_words.py
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@ -1,4 +1,3 @@
STOP_WORDS = set(
"""
a à â abord absolument afin ah ai aie ailleurs ainsi ait allaient allo allons

1
spacy/lang/ga/irish_morphology_helpers.py
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@ -1,4 +1,3 @@
# fmt: off
consonants = ["b", "c", "d", "f", "g", "h", "j", "k", "l", "m", "n", "p", "q", "r", "s", "t", "v", "w", "x", "z"]
broad_vowels = ["a", "á", "o", "ó", "u", "ú"]

1
spacy/lang/he/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/hi/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/hi/stop_words.py
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@ -1,4 +1,3 @@
# Source: https://github.com/taranjeet/hindi-tokenizer/blob/master/stopwords.txt, https://data.mendeley.com/datasets/bsr3frvvjc/1#file-a21d5092-99d7-45d8-b044-3ae9edd391c6
STOP_WORDS = set(

1
spacy/lang/hu/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/hu/stop_words.py
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@ -1,4 +1,3 @@
STOP_WORDS = set(
"""
a abban ahhoz ahogy ahol aki akik akkor akár alatt amely amelyek amelyekben

1
spacy/lang/id/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/is/stop_words.py
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@ -1,4 +1,3 @@
# Source: https://github.com/Xangis/extra-stopwords
STOP_WORDS = set(

1
spacy/lang/it/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/it/stop_words.py
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@ -1,4 +1,3 @@
STOP_WORDS = set(
"""
a abbastanza abbia abbiamo abbiano abbiate accidenti ad adesso affinche agl

1
spacy/lang/ja/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/kn/stop_words.py
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@ -1,4 +1,3 @@
STOP_WORDS = set(
"""
ಹಲವು

1
spacy/lang/lt/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/lv/stop_words.py
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@ -1,4 +1,3 @@
# Source: https://github.com/stopwords-iso/stopwords-lv
STOP_WORDS = set(

1
spacy/lang/mr/stop_words.py
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@ -1,4 +1,3 @@
# Source: https://github.com/stopwords-iso/stopwords-mr/blob/master/stopwords-mr.txt, https://github.com/6/stopwords-json/edit/master/dist/mr.json
STOP_WORDS = set(
"""

1
spacy/lang/nb/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/nl/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/norm_exceptions.py
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@ -1,4 +1,3 @@
# These exceptions are used to add NORM values based on a token's ORTH value.
# Individual languages can also add their own exceptions and overwrite them -
# for example, British vs. American spelling in English.

1
spacy/lang/pl/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/pt/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/pt/stop_words.py
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@ -1,4 +1,3 @@
STOP_WORDS = set(
"""
à às área acerca ademais adeus agora ainda algo algumas alguns ali além ambas ambos antes

1
spacy/lang/ro/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/ru/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/ru/norm_exceptions.py
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@ -1,4 +1,3 @@
_exc = {
# Slang
"прив": "привет",

1
spacy/lang/si/examples.py
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@ -1,4 +1,3 @@
"""
Example sentences to test spaCy and its language models.

1
spacy/lang/si/stop_words.py
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@ -1,4 +1,3 @@
STOP_WORDS = set(
"""
අතර

5
spacy/lang/sk/__init__.py
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@ -1,11 +1,16 @@
from .stop_words import STOP_WORDS
from .tag_map import TAG_MAP
from .lex_attrs import LEX_ATTRS
from ...language import Language
from ...attrs import LANG
class SlovakDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters.update(LEX_ATTRS)
lex_attr_getters[LANG] = lambda text: "sk"
tag_map = TAG_MAP
stop_words = STOP_WORDS

23
spacy/lang/sk/examples.py Normal file
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@ -0,0 +1,23 @@
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.sk.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Ardevop, s.r.o. je malá startup firma na území SR.",
"Samojazdiace autá presúvajú poistnú zodpovednosť na výrobcov automobilov.",
"Košice sú na východe.",
"Bratislava je hlavné mesto Slovenskej republiky.",
"Kde si?",
"Kto je prezidentom Francúzska?",
"Aké je hlavné mesto Slovenska?",
"Kedy sa narodil Andrej Kiska?",
"Včera som dostal 100€ na ruku.",
"Dnes je nedeľa 26.1.2020.",
"Narodil sa 15.4.1998 v Ružomberku.",
"Niekto mi povedal, že 500 eur je veľa peňazí.",
"Podaj mi ruku!",
]

59
spacy/lang/sk/lex_attrs.py Normal file
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@ -0,0 +1,59 @@
from ...attrs import LIKE_NUM
_num_words = [
"nula",
"jeden",
"dva",
"tri",
"štyri",
"päť",
"šesť",
"sedem",
"osem",
"deväť",
"desať",
"jedenásť",
"dvanásť",
"trinásť",
"štrnásť",
"pätnásť",
"šestnásť",
"sedemnásť",
"osemnásť",
"devätnásť",
"dvadsať",
"tridsať",
"štyridsať",
"päťdesiat",
"šesťdesiat",
"sedemdesiat",
"osemdesiat",
"deväťdesiat",
"sto",
"tisíc",
"milión",
"miliarda",
"bilión",
"biliarda",
"trilión",
"triliarda",
"kvadrilión",
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text.lower() in _num_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}

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