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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 | Adam Bittlingmayer |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 12 Aug 2020 |
| GitHub username | bittlingmayer |
| Website (optional) | |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Thomas |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2020-08-11 |
| GitHub username | graue70 |
| Website (optional) | |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Vladimir Holubec |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 30.07.2020 |
| GitHub username | holubvl3 |
| Website (optional) | |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Ido Shraga |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 20-09-2020 |
| GitHub username | idoshr |
| Website (optional) | |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Juan Gutiérrez |
| Company name (if applicable) | Ojtli |
| Title or role (if applicable) | |
| Date | 2020-08-28 |
| GitHub username | jgutix |
| Website (optional) | ojtli.app |

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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 | Gustavo Zadrozny Leyendecker |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | July 29, 2020 |
| GitHub username | leyendecker |
| Website (optional) | |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | ------------------------ |
| Name | Zhe li |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2020-07-24 |
| GitHub username | lizhe2004 |
| Website (optional) | http://www.huahuaxia.net|

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@ -0,0 +1,106 @@
# 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 | Shashank Shekhar |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2020-08-23 |
| GitHub username | snsten |
| Website (optional) | |

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@ -0,0 +1,106 @@
# 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 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 | Joshua Olson |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2020-07-22 |
| GitHub username | solarmist |
| Website (optional) | http://blog.solarmist.net |

106
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@ -0,0 +1,106 @@
# 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 | Attila Szász |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 12 Aug 2020 |
| GitHub username | tilusnet |
| Website (optional) | |

2
extra/experiments/onto-joint/defaults.cfg
View File

@ -36,7 +36,7 @@ max_length = 0
limit = 0
[training.batcher]
@batchers = "batch_by_words.v1"
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2

2
extra/experiments/ptb-joint-pos-dep/defaults.cfg
View File

@ -35,7 +35,7 @@ max_length = 0
limit = 0
[training.batcher]
@batchers = "batch_by_words.v1"
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2

38
licenses/3rd_party_licenses.txt Normal file
View File

@ -0,0 +1,38 @@
Third Party Licenses for spaCy
==============================
NumPy
-----
* Files: setup.py
Copyright (c) 2005-2020, NumPy Developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of the NumPy Developers nor the names of any
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

2
netlify.toml
View File

@ -24,7 +24,7 @@ redirects = [
{from = "/docs/usage/customizing-tokenizer", to = "/usage/linguistic-features#tokenization", force = true},
{from = "/docs/usage/language-processing-pipeline", to = "/usage/processing-pipelines", force = true},
{from = "/docs/usage/customizing-pipeline", to = "/usage/processing-pipelines", force = true},
{from = "/docs/usage/training-ner", to = "/usage/training#ner", force = true},
{from = "/docs/usage/training-ner", to = "/usage/training", force = true},
{from = "/docs/usage/tutorials", to = "/usage/examples", force = true},
{from = "/docs/usage/data-model", to = "/api", force = true},
{from = "/docs/usage/cli", to = "/api/cli", force = true},

2
spacy/about.py
View File

@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy-nightly"
__version__ = "3.0.0a13"
__version__ = "3.0.0a14"
__release__ = True
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

8
spacy/cli/__init__.py
View File

@ -29,9 +29,9 @@ from .project.document import project_document # noqa: F401
@app.command("link", no_args_is_help=True, deprecated=True, hidden=True)
def link(*args, **kwargs):
"""As of spaCy v3.0, model symlinks are deprecated. You can load models
using their full names or from a directory path."""
"""As of spaCy v3.0, symlinks like "en" are deprecated. You can load trained
pipeline packages using their full names or from a directory path."""
msg.warn(
"As of spaCy v3.0, model symlinks are deprecated. You can load models "
"using their full names or from a directory path."
"As of spaCy v3.0, model symlinks are deprecated. You can load trained "
"pipeline packages using their full names or from a directory path."
)

4
spacy/cli/_util.py
View File

@ -25,7 +25,7 @@ COMMAND = "python -m spacy"
NAME = "spacy"
HELP = """spaCy Command-line Interface
DOCS: https://spacy.io/api/cli
DOCS: https://nightly.spacy.io/api/cli
"""
PROJECT_HELP = f"""Command-line interface for spaCy projects and templates.
You'd typically start by cloning a project template to a local directory and
@ -36,7 +36,7 @@ DEBUG_HELP = """Suite of helpful commands for debugging and profiling. Includes
commands to check and validate your config files, training and evaluation data,
and custom model implementations.
"""
INIT_HELP = """Commands for initializing configs and models."""
INIT_HELP = """Commands for initializing configs and pipeline packages."""
# Wrappers for Typer's annotations. Initially created to set defaults and to
# keep the names short, but not needed at the moment.

6
spacy/cli/convert.py
View File

@ -44,7 +44,7 @@ def convert_cli(
file_type: FileTypes = Opt("spacy", "--file-type", "-t", help="Type of data to produce"),
n_sents: int = Opt(1, "--n-sents", "-n", help="Number of sentences per doc (0 to disable)"),
seg_sents: bool = Opt(False, "--seg-sents", "-s", help="Segment sentences (for -c ner)"),
model: Optional[str] = Opt(None, "--model", "-b", help="Model for sentence segmentation (for -s)"),
model: Optional[str] = Opt(None, "--model", "--base", "-b", help="Trained spaCy pipeline for sentence segmentation to use as base (for --seg-sents)"),
morphology: bool = Opt(False, "--morphology", "-m", help="Enable appending morphology to tags"),
merge_subtokens: bool = Opt(False, "--merge-subtokens", "-T", help="Merge CoNLL-U subtokens"),
converter: str = Opt("auto", "--converter", "-c", help=f"Converter: {tuple(CONVERTERS.keys())}"),
@ -61,6 +61,8 @@ def convert_cli(
If no output_dir is specified and the output format is JSON, the data
is written to stdout, so you can pipe them forward to a JSON file:
$ spacy convert some_file.conllu --file-type json > some_file.json
DOCS: https://nightly.spacy.io/api/cli#convert
"""
if isinstance(file_type, FileTypes):
# We get an instance of the FileTypes from the CLI so we need its string value
@ -261,6 +263,6 @@ def _get_converter(msg, converter, input_path):
msg.warn(
"Can't automatically detect NER format. "
"Conversion may not succeed. "
"See https://spacy.io/api/cli#convert"
"See https://nightly.spacy.io/api/cli#convert"
)
return converter

2
spacy/cli/debug_config.py
View File

@ -31,6 +31,8 @@ def debug_config_cli(
Similar as with the 'train' command, you can override settings from the config
as command line options. For instance, --training.batch_size 128 overrides
the value of "batch_size" in the block "[training]".
DOCS: https://nightly.spacy.io/api/cli#debug-config
"""
overrides = parse_config_overrides(ctx.args)
import_code(code_path)

12
spacy/cli/debug_data.py
View File

@ -18,7 +18,7 @@ from .. import util
NEW_LABEL_THRESHOLD = 50
# Minimum number of expected occurrences of dependency labels
DEP_LABEL_THRESHOLD = 20
# Minimum number of expected examples to train a blank model
# Minimum number of expected examples to train a new pipeline
BLANK_MODEL_MIN_THRESHOLD = 100
BLANK_MODEL_THRESHOLD = 2000
@ -47,6 +47,8 @@ def debug_data_cli(
Analyze, debug and validate your training and development data. Outputs
useful stats, and can help you find problems like invalid entity annotations,
cyclic dependencies, low data labels and more.
DOCS: https://nightly.spacy.io/api/cli#debug-data
"""
if ctx.command.name == "debug-data":
msg.warn(
@ -148,7 +150,7 @@ def debug_data(
msg.text(f"Language: {config['nlp']['lang']}")
msg.text(f"Training pipeline: {', '.join(pipeline)}")
if resume_components:
msg.text(f"Components from other models: {', '.join(resume_components)}")
msg.text(f"Components from other pipelines: {', '.join(resume_components)}")
if frozen_components:
msg.text(f"Frozen components: {', '.join(frozen_components)}")
msg.text(f"{len(train_dataset)} training docs")
@ -164,9 +166,7 @@ def debug_data(
# TODO: make this feedback more fine-grained and report on updated
# components vs. blank components
if not resume_components and len(train_dataset) < BLANK_MODEL_THRESHOLD:
text = (
f"Low number of examples to train from a blank model ({len(train_dataset)})"
)
text = f"Low number of examples to train a new pipeline ({len(train_dataset)})"
if len(train_dataset) < BLANK_MODEL_MIN_THRESHOLD:
msg.fail(text)
else:
@ -214,7 +214,7 @@ def debug_data(
show=verbose,
)
else:
msg.info("No word vectors present in the model")
msg.info("No word vectors present in the package")
if "ner" in factory_names:
# Get all unique NER labels present in the data

2
spacy/cli/debug_model.py
View File

@ -30,6 +30,8 @@ def debug_model_cli(
"""
Analyze a Thinc model implementation. Includes checks for internal structure
and activations during training.
DOCS: https://nightly.spacy.io/api/cli#debug-model
"""
if use_gpu >= 0:
msg.info("Using GPU")

31
spacy/cli/download.py
View File

@ -17,16 +17,19 @@ from ..errors import OLD_MODEL_SHORTCUTS
def download_cli(
# fmt: off
ctx: typer.Context,
model: str = Arg(..., help="Name of model to download"),
model: str = Arg(..., help="Name of pipeline package to download"),
direct: bool = Opt(False, "--direct", "-d", "-D", help="Force direct download of name + version"),
# fmt: on
):
"""
Download compatible model from default download path using pip. If --direct
flag is set, the command expects the full model name with version.
For direct downloads, the compatibility check will be skipped. All
Download compatible trained pipeline from the default download path using
pip. If --direct flag is set, the command expects the full package name with
version. For direct downloads, the compatibility check will be skipped. All
additional arguments provided to this command will be passed to `pip install`
on model installation.
on package installation.
DOCS: https://nightly.spacy.io/api/cli#download
AVAILABLE PACKAGES: https://spacy.io/models
"""
download(model, direct, *ctx.args)
@ -34,11 +37,11 @@ def download_cli(
def download(model: str, direct: bool = False, *pip_args) -> None:
if not is_package("spacy") and "--no-deps" not in pip_args:
msg.warn(
"Skipping model package dependencies and setting `--no-deps`. "
"Skipping pipeline package dependencies and setting `--no-deps`. "
"You don't seem to have the spaCy package itself installed "
"(maybe because you've built from source?), so installing the "
"model dependencies would cause spaCy to be downloaded, which "
"probably isn't what you want. If the model package has other "
"package dependencies would cause spaCy to be downloaded, which "
"probably isn't what you want. If the pipeline package has other "
"dependencies, you'll have to install them manually."
)
pip_args = pip_args + ("--no-deps",)
@ -53,7 +56,7 @@ def download(model: str, direct: bool = False, *pip_args) -> None:
if model in OLD_MODEL_SHORTCUTS:
msg.warn(
f"As of spaCy v3.0, shortcuts like '{model}' are deprecated. Please"
f"use the full model name '{OLD_MODEL_SHORTCUTS[model]}' instead."
f"use the full pipeline package name '{OLD_MODEL_SHORTCUTS[model]}' instead."
)
model_name = OLD_MODEL_SHORTCUTS[model]
compatibility = get_compatibility()
@ -61,7 +64,7 @@ def download(model: str, direct: bool = False, *pip_args) -> None:
download_model(dl_tpl.format(m=model_name, v=version), pip_args)
msg.good(
"Download and installation successful",
f"You can now load the model via spacy.load('{model_name}')",
f"You can now load the package via spacy.load('{model_name}')",
)
@ -71,16 +74,16 @@ def get_compatibility() -> dict:
if r.status_code != 200:
msg.fail(
f"Server error ({r.status_code})",
f"Couldn't fetch compatibility table. Please find a model for your spaCy "
f"Couldn't fetch compatibility table. Please find a package for your spaCy "
f"installation (v{about.__version__}), and download it manually. "
f"For more details, see the documentation: "
f"https://spacy.io/usage/models",
f"https://nightly.spacy.io/usage/models",
exits=1,
)
comp_table = r.json()
comp = comp_table["spacy"]
if version not in comp:
msg.fail(f"No compatible models found for v{version} of spaCy", exits=1)
msg.fail(f"No compatible packages found for v{version} of spaCy", exits=1)
return comp[version]
@ -88,7 +91,7 @@ def get_version(model: str, comp: dict) -> str:
model = get_base_version(model)
if model not in comp:
msg.fail(
f"No compatible model found for '{model}' (spaCy v{about.__version__})",
f"No compatible package found for '{model}' (spaCy v{about.__version__})",
exits=1,
)
return comp[model][0]

17
spacy/cli/evaluate.py
View File

@ -26,13 +26,16 @@ def evaluate_cli(
# fmt: on
):
"""
Evaluate a model. Expects a loadable spaCy model and evaluation data in the
binary .spacy format. The --gold-preproc option sets up the evaluation
examples with gold-standard sentences and tokens for the predictions. Gold
preprocessing helps the annotations align to the tokenization, and may
result in sequences of more consistent length. However, it may reduce
runtime accuracy due to train/test skew. To render a sample of dependency
parses in a HTML file, set as output directory as the displacy_path argument.
Evaluate a trained pipeline. Expects a loadable spaCy pipeline and evaluation
data in the binary .spacy format. The --gold-preproc option sets up the
evaluation examples with gold-standard sentences and tokens for the
predictions. Gold preprocessing helps the annotations align to the
tokenization, and may result in sequences of more consistent length. However,
it may reduce runtime accuracy due to train/test skew. To render a sample of
dependency parses in a HTML file, set as output directory as the
displacy_path argument.
DOCS: https://nightly.spacy.io/api/cli#evaluate
"""
evaluate(
model,

20
spacy/cli/info.py
View File

@ -12,15 +12,17 @@ from .. import about
@app.command("info")
def info_cli(
# fmt: off
model: Optional[str] = Arg(None, help="Optional model name"),
model: Optional[str] = Arg(None, help="Optional loadable spaCy pipeline"),
markdown: bool = Opt(False, "--markdown", "-md", help="Generate Markdown for GitHub issues"),
silent: bool = Opt(False, "--silent", "-s", "-S", help="Don't print anything (just return)"),
# fmt: on
):
"""
Print info about spaCy installation. If a model is speficied as an argument,
print model information. Flag --markdown prints details in Markdown for easy
Print info about spaCy installation. If a pipeline is speficied as an argument,
print its meta information. Flag --markdown prints details in Markdown for easy
copy-pasting to GitHub issues.
DOCS: https://nightly.spacy.io/api/cli#info
"""
info(model, markdown=markdown, silent=silent)
@ -30,14 +32,16 @@ def info(
) -> Union[str, dict]:
msg = Printer(no_print=silent, pretty=not silent)
if model:
title = f"Info about model '{model}'"
title = f"Info about pipeline '{model}'"
data = info_model(model, silent=silent)
else:
title = "Info about spaCy"
data = info_spacy()
raw_data = {k.lower().replace(" ", "_"): v for k, v in data.items()}
if "Models" in data and isinstance(data["Models"], dict):
data["Models"] = ", ".join(f"{n} ({v})" for n, v in data["Models"].items())
if "Pipelines" in data and isinstance(data["Pipelines"], dict):
data["Pipelines"] = ", ".join(
f"{n} ({v})" for n, v in data["Pipelines"].items()
)
markdown_data = get_markdown(data, title=title)
if markdown:
if not silent:
@ -63,7 +67,7 @@ def info_spacy() -> Dict[str, any]:
"Location": str(Path(__file__).parent.parent),
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Models": all_models,
"Pipelines": all_models,
}
@ -81,7 +85,7 @@ def info_model(model: str, *, silent: bool = True) -> Dict[str, Any]:
model_path = model
meta_path = model_path / "meta.json"
if not meta_path.is_file():
msg.fail("Can't find model meta.json", meta_path, exits=1)
msg.fail("Can't find pipeline meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path)
if model_path.resolve() != model_path:
meta["source"] = str(model_path.resolve())

8
spacy/cli/init_config.py
View File

@ -27,7 +27,7 @@ def init_config_cli(
# fmt: off
output_file: Path = Arg(..., help="File to save config.cfg to or - for stdout (will only output config and no additional logging info)", allow_dash=True),
lang: Optional[str] = Opt("en", "--lang", "-l", help="Two-letter code of the language to use"),
pipeline: Optional[str] = Opt("tagger,parser,ner", "--pipeline", "-p", help="Comma-separated names of trainable pipeline components to include in the model (without 'tok2vec' or 'transformer')"),
pipeline: Optional[str] = Opt("tagger,parser,ner", "--pipeline", "-p", help="Comma-separated names of trainable pipeline components to include (without 'tok2vec' or 'transformer')"),
optimize: Optimizations = Opt(Optimizations.efficiency.value, "--optimize", "-o", help="Whether to optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). This will impact the choice of architecture, pretrained weights and related hyperparameters."),
cpu: bool = Opt(False, "--cpu", "-C", help="Whether the model needs to run on CPU. This will impact the choice of architecture, pretrained weights and related hyperparameters."),
# fmt: on
@ -37,6 +37,8 @@ def init_config_cli(
specified via the CLI arguments, this command generates a config with the
optimal settings for you use case. This includes the choice of architecture,
pretrained weights and related hyperparameters.
DOCS: https://nightly.spacy.io/api/cli#init-config
"""
if isinstance(optimize, Optimizations): # instance of enum from the CLI
optimize = optimize.value
@ -59,6 +61,8 @@ def init_fill_config_cli(
functions for their default values and update the base config. This command
can be used with a config generated via the training quickstart widget:
https://nightly.spacy.io/usage/training#quickstart
DOCS: https://nightly.spacy.io/api/cli#init-fill-config
"""
fill_config(output_file, base_path, pretraining=pretraining, diff=diff)
@ -168,7 +172,7 @@ def save_config(
output_file.parent.mkdir(parents=True)
config.to_disk(output_file, interpolate=False)
msg.good("Saved config", output_file)
msg.text("You can now add your data and train your model:")
msg.text("You can now add your data and train your pipeline:")
variables = ["--paths.train ./train.spacy", "--paths.dev ./dev.spacy"]
if not no_print:
print(f"{COMMAND} train {output_file.parts[-1]} {' '.join(variables)}")

30
spacy/cli/init_model.py
View File

@ -28,7 +28,7 @@ except ImportError:
DEFAULT_OOV_PROB = -20
@init_cli.command("model")
@init_cli.command("vocab")
@app.command(
"init-model",
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
@ -37,8 +37,8 @@ DEFAULT_OOV_PROB = -20
def init_model_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
lang: str = Arg(..., help="Model language"),
output_dir: Path = Arg(..., help="Model output directory"),
lang: str = Arg(..., help="Pipeline language"),
output_dir: Path = Arg(..., help="Pipeline output directory"),
freqs_loc: Optional[Path] = Arg(None, help="Location of words frequencies file", exists=True),
clusters_loc: Optional[Path] = Opt(None, "--clusters-loc", "-c", help="Optional location of brown clusters data", exists=True),
jsonl_loc: Optional[Path] = Opt(None, "--jsonl-loc", "-j", help="Location of JSONL-formatted attributes file", exists=True),
@ -46,19 +46,22 @@ def init_model_cli(
prune_vectors: int = Opt(-1, "--prune-vectors", "-V", help="Optional number of vectors to prune to"),
truncate_vectors: int = Opt(0, "--truncate-vectors", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
vectors_name: Optional[str] = Opt(None, "--vectors-name", "-vn", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
model_name: Optional[str] = Opt(None, "--model-name", "-mn", help="Optional name for the model meta"),
base_model: Optional[str] = Opt(None, "--base-model", "-b", help="Base model (for languages with custom tokenizers)")
model_name: Optional[str] = Opt(None, "--meta-name", "-mn", help="Optional name of the package for the pipeline meta"),
base_model: Optional[str] = Opt(None, "--base", "-b", help="Name of or path to base pipeline to start with (mostly relevant for pipelines with custom tokenizers)")
# fmt: on
):
"""
Create a new model from raw data. If vectors are provided in Word2Vec format,
they can be either a .txt or zipped as a .zip or .tar.gz.
Create a new blank pipeline directory with vocab and vectors from raw data.
If vectors are provided in Word2Vec format, they can be either a .txt or
zipped as a .zip or .tar.gz.
DOCS: https://nightly.spacy.io/api/cli#init-vocab
"""
if ctx.command.name == "init-model":
msg.warn(
"The init-model command is now available via the 'init model' "
"subcommand (without the hyphen). You can run python -m spacy init "
"--help for an overview of the other available initialization commands."
"The init-model command is now called 'init vocab'. You can run "
"'python -m spacy init --help' for an overview of the other "
"available initialization commands."
)
init_model(
lang,
@ -115,10 +118,10 @@ def init_model(
msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
lex_attrs = read_attrs_from_deprecated(msg, freqs_loc, clusters_loc)
with msg.loading("Creating model..."):
with msg.loading("Creating blank pipeline..."):
nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model)
msg.good("Successfully created model")
msg.good("Successfully created blank pipeline")
if vectors_loc is not None:
add_vectors(
msg, nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name
@ -242,7 +245,8 @@ def add_vectors(
if vectors_data is not None:
nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
if name is None:
nlp.vocab.vectors.name = f"{nlp.meta['lang']}_model.vectors"
# TODO: Is this correct? Does this matter?
nlp.vocab.vectors.name = f"{nlp.meta['lang']}_{nlp.meta['name']}.vectors"
else:
nlp.vocab.vectors.name = name
nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name

32
spacy/cli/package.py
View File

@ -14,23 +14,25 @@ from .. import about
@app.command("package")
def package_cli(
# fmt: off
input_dir: Path = Arg(..., help="Directory with model data", exists=True, file_okay=False),
input_dir: Path = Arg(..., help="Directory with pipeline data", exists=True, file_okay=False),
output_dir: Path = Arg(..., help="Output parent directory", exists=True, file_okay=False),
meta_path: Optional[Path] = Opt(None, "--meta-path", "--meta", "-m", help="Path to meta.json", exists=True, dir_okay=False),
create_meta: bool = Opt(False, "--create-meta", "-c", "-C", help="Create meta.json, even if one exists"),
version: Optional[str] = Opt(None, "--version", "-v", help="Package version to override meta"),
no_sdist: bool = Opt(False, "--no-sdist", "-NS", help="Don't build .tar.gz sdist, can be set if you want to run this step manually"),
force: bool = Opt(False, "--force", "-f", "-F", help="Force overwriting existing model in output directory"),
force: bool = Opt(False, "--force", "-f", "-F", help="Force overwriting existing data in output directory"),
# fmt: on
):
"""
Generate an installable Python package for a model. Includes model data,
Generate an installable Python package for a pipeline. Includes binary data,
meta and required installation files. A new directory will be created in the
specified output directory, and model data will be copied over. If
specified output directory, and the data will be copied over. If
--create-meta is set and a meta.json already exists in the output directory,
the existing values will be used as the defaults in the command-line prompt.
After packaging, "python setup.py sdist" is run in the package directory,
which will create a .tar.gz archive that can be installed via "pip install".
DOCS: https://nightly.spacy.io/api/cli#package
"""
package(
input_dir,
@ -59,14 +61,14 @@ def package(
output_path = util.ensure_path(output_dir)
meta_path = util.ensure_path(meta_path)
if not input_path or not input_path.exists():
msg.fail("Can't locate model data", input_path, exits=1)
msg.fail("Can't locate pipeline data", input_path, exits=1)
if not output_path or not output_path.exists():
msg.fail("Output directory not found", output_path, exits=1)
if meta_path and not meta_path.exists():
msg.fail("Can't find model meta.json", meta_path, exits=1)
msg.fail("Can't find pipeline meta.json", meta_path, exits=1)
meta_path = meta_path or input_dir / "meta.json"
if not meta_path.exists() or not meta_path.is_file():
msg.fail("Can't load model meta.json", meta_path, exits=1)
msg.fail("Can't load pipeline meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path)
meta = get_meta(input_dir, meta)
if version is not None:
@ -77,7 +79,7 @@ def package(
meta = generate_meta(meta, msg)
errors = validate(ModelMetaSchema, meta)
if errors:
msg.fail("Invalid model meta.json")
msg.fail("Invalid pipeline meta.json")
print("\n".join(errors))
sys.exit(1)
model_name = meta["lang"] + "_" + meta["name"]
@ -118,7 +120,7 @@ def get_meta(
) -> Dict[str, Any]:
meta = {
"lang": "en",
"name": "model",
"name": "pipeline",
"version": "0.0.0",
"description": "",
"author": "",
@ -143,10 +145,10 @@ def get_meta(
def generate_meta(existing_meta: Dict[str, Any], msg: Printer) -> Dict[str, Any]:
meta = existing_meta or {}
settings = [
("lang", "Model language", meta.get("lang", "en")),
("name", "Model name", meta.get("name", "model")),
("version", "Model version", meta.get("version", "0.0.0")),
("description", "Model description", meta.get("description", None)),
("lang", "Pipeline language", meta.get("lang", "en")),
("name", "Pipeline name", meta.get("name", "pipeline")),
("version", "Package version", meta.get("version", "0.0.0")),
("description", "Package description", meta.get("description", None)),
("author", "Author", meta.get("author", None)),
("email", "Author email", meta.get("email", None)),
("url", "Author website", meta.get("url", None)),
@ -154,8 +156,8 @@ def generate_meta(existing_meta: Dict[str, Any], msg: Printer) -> Dict[str, Any]
]
msg.divider("Generating meta.json")
msg.text(
"Enter the package settings for your model. The following information "
"will be read from your model data: pipeline, vectors."
"Enter the package settings for your pipeline. The following information "
"will be read from your pipeline data: pipeline, vectors."
)
for setting, desc, default in settings:
response = get_raw_input(desc, default)

9
spacy/cli/pretrain.py
View File

@ -31,7 +31,7 @@ def pretrain_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
texts_loc: Path = Arg(..., help="Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the key 'tokens'", exists=True),
output_dir: Path = Arg(..., help="Directory to write models to on each epoch"),
output_dir: Path = Arg(..., help="Directory to write weights to on each epoch"),
config_path: Path = Arg(..., help="Path to config file", exists=True, dir_okay=False),
code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
resume_path: Optional[Path] = Opt(None, "--resume-path", "-r", help="Path to pretrained weights from which to resume pretraining"),
@ -57,6 +57,8 @@ def pretrain_cli(
To load the weights back in during 'spacy train', you need to ensure
all settings are the same between pretraining and training. Ideally,
this is done by using the same config file for both commands.
DOCS: https://nightly.spacy.io/api/cli#pretrain
"""
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
@ -377,9 +379,8 @@ def verify_cli_args(texts_loc, output_dir, config_path, resume_path, epoch_resum
if resume_path:
msg.warn(
"Output directory is not empty.",
"If you're resuming a run from a previous model in this directory, "
"the old models for the consecutive epochs will be overwritten "
"with the new ones.",
"If you're resuming a run in this directory, the old weights "
"for the consecutive epochs will be overwritten with the new ones.",
)
else:
msg.warn(

8
spacy/cli/profile.py
View File

@ -19,7 +19,7 @@ from ..util import load_model
def profile_cli(
# fmt: off
ctx: typer.Context, # This is only used to read current calling context
model: str = Arg(..., help="Model to load"),
model: str = Arg(..., help="Trained pipeline to load"),
inputs: Optional[Path] = Arg(None, help="Location of input file. '-' for stdin.", exists=True, allow_dash=True),
n_texts: int = Opt(10000, "--n-texts", "-n", help="Maximum number of texts to use if available"),
# fmt: on
@ -29,6 +29,8 @@ def profile_cli(
Input should be formatted as one JSON object per line with a key "text".
It can either be provided as a JSONL file, or be read from sys.sytdin.
If no input file is specified, the IMDB dataset is loaded via Thinc.
DOCS: https://nightly.spacy.io/api/cli#debug-profile
"""
if ctx.parent.command.name == NAME: # called as top-level command
msg.warn(
@ -60,9 +62,9 @@ def profile(model: str, inputs: Optional[Path] = None, n_texts: int = 10000) ->
inputs, _ = zip(*imdb_train)
msg.info(f"Loaded IMDB dataset and using {n_inputs} examples")
inputs = inputs[:n_inputs]
with msg.loading(f"Loading model '{model}'..."):
with msg.loading(f"Loading pipeline '{model}'..."):
nlp = load_model(model)
msg.good(f"Loaded model '{model}'")
msg.good(f"Loaded pipeline '{model}'")
texts = list(itertools.islice(inputs, n_texts))
cProfile.runctx("parse_texts(nlp, texts)", globals(), locals(), "Profile.prof")
s = pstats.Stats("Profile.prof")

2
spacy/cli/project/assets.py
View File

@ -20,6 +20,8 @@ def project_assets_cli(
defined in the "assets" section of the project.yml. If a checksum is
provided in the project.yml, the file is only downloaded if no local file
with the same checksum exists.
DOCS: https://nightly.spacy.io/api/cli#project-assets
"""
project_assets(project_dir)

2
spacy/cli/project/clone.py
View File

@ -22,6 +22,8 @@ def project_clone_cli(
only download the files from the given subdirectory. The GitHub repo
defaults to the official spaCy template repo, but can be customized
(including using a private repo).
DOCS: https://nightly.spacy.io/api/cli#project-clone
"""
if dest is None:
dest = Path.cwd() / name

2
spacy/cli/project/document.py
View File

@ -43,6 +43,8 @@ def project_document_cli(
hidden markers are added so you can add custom content before or after the
auto-generated section and only the auto-generated docs will be replaced
when you re-run the command.
DOCS: https://nightly.spacy.io/api/cli#project-document
"""
project_document(project_dir, output_file, no_emoji=no_emoji)

5
spacy/cli/project/dvc.py
View File

@ -31,7 +31,10 @@ def project_update_dvc_cli(
"""Auto-generate Data Version Control (DVC) config. A DVC
project can only define one pipeline, so you need to specify one workflow
defined in the project.yml. If no workflow is specified, the first defined
workflow is used. The DVC config will only be updated if the project.yml changed.
workflow is used. The DVC config will only be updated if the project.yml
changed.
DOCS: https://nightly.spacy.io/api/cli#project-dvc
"""
project_update_dvc(project_dir, workflow, verbose=verbose, force=force)

7
spacy/cli/project/pull.py
View File

@ -17,7 +17,9 @@ def project_pull_cli(
"""Retrieve available precomputed outputs from a remote storage.
You can alias remotes in your project.yml by mapping them to storage paths.
A storage can be anything that the smart-open library can upload to, e.g.
gcs, aws, ssh, local directories etc
AWS, Google Cloud Storage, SSH, local directories etc.
DOCS: https://nightly.spacy.io/api/cli#project-pull
"""
for url, output_path in project_pull(project_dir, remote):
if url is not None:
@ -38,5 +40,6 @@ def project_pull(project_dir: Path, remote: str, *, verbose: bool = False):
url = storage.pull(output_path, command_hash=cmd_hash)
yield url, output_path
if cmd.get("outptus") and all(loc.exists() for loc in cmd["outputs"]):
out_locs = [project_dir / out for out in cmd.get("outputs", [])]
if all(loc.exists() for loc in out_locs):
update_lockfile(project_dir, cmd)

20
spacy/cli/project/push.py
View File

@ -13,9 +13,12 @@ def project_push_cli(
project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
# fmt: on
):
"""Persist outputs to a remote storage. You can alias remotes in your project.yml
by mapping them to storage paths. A storage can be anything that the smart-open
library can upload to, e.g. gcs, aws, ssh, local directories etc
"""Persist outputs to a remote storage. You can alias remotes in your
project.yml by mapping them to storage paths. A storage can be anything that
the smart-open library can upload to, e.g. AWS, Google Cloud Storage, SSH,
local directories etc.
DOCS: https://nightly.spacy.io/api/cli#project-push
"""
for output_path, url in project_push(project_dir, remote):
if url is None:
@ -42,10 +45,19 @@ def project_push(project_dir: Path, remote: str):
)
for output_path in cmd.get("outputs", []):
output_loc = project_dir / output_path
if output_loc.exists():
if output_loc.exists() and _is_not_empty_dir(output_loc):
url = storage.push(
output_path,
command_hash=cmd_hash,
content_hash=get_content_hash(output_loc),
)
yield output_path, url
def _is_not_empty_dir(loc: Path):
if not loc.is_dir():
return True
elif any(_is_not_empty_dir(child) for child in loc.iterdir()):
return True
else:
return False

2
spacy/cli/project/run.py
View File

@ -24,6 +24,8 @@ def project_run_cli(
name is specified, all commands in the workflow are run, in order. If
commands define dependencies and/or outputs, they will only be re-run if
state has changed.
DOCS: https://nightly.spacy.io/api/cli#project-run
"""
if show_help or not subcommand:
print_run_help(project_dir, subcommand)

9
spacy/cli/templates/quickstart_training.jinja
View File

@ -29,7 +29,7 @@ name = "{{ transformer["name"] }}"
tokenizer_config = {"use_fast": true}
[components.transformer.model.get_spans]
@span_getters = "strided_spans.v1"
@span_getters = "spacy-transformers.strided_spans.v1"
window = 128
stride = 96
@ -186,11 +186,14 @@ accumulate_gradient = {{ transformer["size_factor"] }}
[training.optimizer]
@optimizers = "Adam.v1"
{% if use_transformer -%}
[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 5e-5
{% endif %}
[training.train_corpus]
@readers = "spacy.Corpus.v1"
@ -204,13 +207,13 @@ max_length = 0
{% if use_transformer %}
[training.batcher]
@batchers = "batch_by_padded.v1"
@batchers = "spacy.batch_by_padded.v1"
discard_oversize = true
size = 2000
buffer = 256
{%- else %}
[training.batcher]
@batchers = "batch_by_words.v1"
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2

12
spacy/cli/train.py
View File

@ -26,7 +26,7 @@ def train_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
config_path: Path = Arg(..., help="Path to config file", exists=True),
output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store model in"),
output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store trained pipeline in"),
code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
@ -34,7 +34,7 @@ def train_cli(
# fmt: on
):
"""
Train or update a spaCy model. Requires data in spaCy's binary format. To
Train or update a spaCy pipeline. Requires data in spaCy's binary format. To
convert data from other formats, use the `spacy convert` command. The
config file includes all settings and hyperparameters used during traing.
To override settings in the config, e.g. settings that point to local
@ -44,6 +44,8 @@ def train_cli(
lets you pass in a Python file that's imported before training. It can be
used to register custom functions and architectures that can then be
referenced in the config.
DOCS: https://nightly.spacy.io/api/cli#train
"""
util.logger.setLevel(logging.DEBUG if verbose else logging.ERROR)
verify_cli_args(config_path, output_path)
@ -113,12 +115,12 @@ def train(
# Load morph rules
nlp.vocab.morphology.load_morph_exceptions(morph_rules)
# Load a pretrained tok2vec model - cf. CLI command 'pretrain'
# Load pretrained tok2vec weights - cf. CLI command 'pretrain'
if weights_data is not None:
tok2vec_path = config["pretraining"].get("tok2vec_model", None)
if tok2vec_path is None:
msg.fail(
f"To use a pretrained tok2vec model, the config needs to specify which "
f"To pretrained tok2vec weights, the config needs to specify which "
f"tok2vec layer to load in the setting [pretraining.tok2vec_model].",
exits=1,
)
@ -183,7 +185,7 @@ def train(
nlp.to_disk(final_model_path)
else:
nlp.to_disk(final_model_path)
msg.good(f"Saved model to output directory {final_model_path}")
msg.good(f"Saved pipeline to output directory {final_model_path}")
def create_train_batches(iterator, batcher, max_epochs: int):

18
spacy/cli/validate.py
View File

@ -13,9 +13,11 @@ from ..util import get_package_path, get_model_meta, is_compatible_version
@app.command("validate")
def validate_cli():
"""
Validate the currently installed models and spaCy version. Checks if the
installed models are compatible and shows upgrade instructions if available.
Should be run after `pip install -U spacy`.
Validate the currently installed pipeline packages and spaCy version. Checks
if the installed packages are compatible and shows upgrade instructions if
available. Should be run after `pip install -U spacy`.
DOCS: https://nightly.spacy.io/api/cli#validate
"""
validate()
@ -25,13 +27,13 @@ def validate() -> None:
spacy_version = get_base_version(about.__version__)
current_compat = compat.get(spacy_version, {})
if not current_compat:
msg.warn(f"No compatible models found for v{spacy_version} of spaCy")
msg.warn(f"No compatible packages found for v{spacy_version} of spaCy")
incompat_models = {d["name"] for _, d in model_pkgs.items() if not d["compat"]}
na_models = [m for m in incompat_models if m not in current_compat]
update_models = [m for m in incompat_models if m in current_compat]
spacy_dir = Path(__file__).parent.parent
msg.divider(f"Installed models (spaCy v{about.__version__})")
msg.divider(f"Installed pipeline packages (spaCy v{about.__version__})")
msg.info(f"spaCy installation: {spacy_dir}")
if model_pkgs:
@ -47,15 +49,15 @@ def validate() -> None:
rows.append((data["name"], data["spacy"], version, comp))
msg.table(rows, header=header)
else:
msg.text("No models found in your current environment.", exits=0)
msg.text("No pipeline packages found in your current environment.", exits=0)
if update_models:
msg.divider("Install updates")
msg.text("Use the following commands to update the model packages:")
msg.text("Use the following commands to update the packages:")
cmd = "python -m spacy download {}"
print("\n".join([cmd.format(pkg) for pkg in update_models]) + "\n")
if na_models:
msg.info(
f"The following models are custom spaCy models or not "
f"The following packages are custom spaCy pipelines or not "
f"available for spaCy v{about.__version__}:",
", ".join(na_models),
)

2
spacy/default_config.cfg
View File

@ -69,7 +69,7 @@ max_length = 2000
limit = 0
[training.batcher]
@batchers = "batch_by_words.v1"
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2

12
spacy/displacy/__init__.py
View File

@ -1,8 +1,8 @@
"""
spaCy's built in visualization suite for dependencies and named entities.
DOCS: https://spacy.io/api/top-level#displacy
USAGE: https://spacy.io/usage/visualizers
DOCS: https://nightly.spacy.io/api/top-level#displacy
USAGE: https://nightly.spacy.io/usage/visualizers
"""
from typing import Union, Iterable, Optional, Dict, Any, Callable
import warnings
@ -37,8 +37,8 @@ def render(
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
RETURNS (str): Rendered HTML markup.
DOCS: https://spacy.io/api/top-level#displacy.render
USAGE: https://spacy.io/usage/visualizers
DOCS: https://nightly.spacy.io/api/top-level#displacy.render
USAGE: https://nightly.spacy.io/usage/visualizers
"""
factories = {
"dep": (DependencyRenderer, parse_deps),
@ -88,8 +88,8 @@ def serve(
port (int): Port to serve visualisation.
host (str): Host to serve visualisation.
DOCS: https://spacy.io/api/top-level#displacy.serve
USAGE: https://spacy.io/usage/visualizers
DOCS: https://nightly.spacy.io/api/top-level#displacy.serve
USAGE: https://nightly.spacy.io/usage/visualizers
"""
from wsgiref import simple_server

12
spacy/displacy/render.py
View File

@ -249,6 +249,12 @@ class EntityRenderer:
colors = dict(DEFAULT_LABEL_COLORS)
user_colors = registry.displacy_colors.get_all()
for user_color in user_colors.values():
if callable(user_color):
# Since this comes from the function registry, we want to make
# sure we support functions that *return* a dict of colors
user_color = user_color()
if not isinstance(user_color, dict):
raise ValueError(Errors.E925.format(obj=type(user_color)))
colors.update(user_color)
colors.update(options.get("colors", {}))
self.default_color = DEFAULT_ENTITY_COLOR
@ -323,7 +329,11 @@ class EntityRenderer:
else:
markup += entity
offset = end
markup += escape_html(text[offset:])
fragments = text[offset:].split("\n")
for i, fragment in enumerate(fragments):
markup += escape_html(fragment)
if len(fragments) > 1 and i != len(fragments) - 1:
markup += "</br>"
markup = TPL_ENTS.format(content=markup, dir=self.direction)
if title:
markup = TPL_TITLE.format(title=title) + markup

36
spacy/errors.py
View File

@ -22,7 +22,7 @@ class Warnings:
"generate a dependency visualization for it. Make sure the Doc "
"was processed with a model that supports dependency parsing, and "
"not just a language class like `English()`. For more info, see "
"the docs:\nhttps://spacy.io/usage/models")
"the docs:\nhttps://nightly.spacy.io/usage/models")
W006 = ("No entities to visualize found in Doc object. If this is "
"surprising to you, make sure the Doc was processed using a model "
"that supports named entity recognition, and check the `doc.ents` "
@ -76,6 +76,10 @@ class Warnings:
"If this is surprising, make sure you have the spacy-lookups-data "
"package installed. The languages with lexeme normalization tables "
"are currently: {langs}")
W034 = ("Please install the package spacy-lookups-data in order to include "
"the default lexeme normalization table for the language '{lang}'.")
W035 = ('Discarding subpattern "{pattern}" due to an unrecognized '
"attribute or operator.")
# TODO: fix numbering after merging develop into master
W090 = ("Could not locate any binary .spacy files in path '{path}'.")
@ -147,7 +151,7 @@ class Errors:
E010 = ("Word vectors set to length 0. This may be because you don't have "
"a model installed or loaded, or because your model doesn't "
"include word vectors. For more info, see the docs:\n"
"https://spacy.io/usage/models")
"https://nightly.spacy.io/usage/models")
E011 = ("Unknown operator: '{op}'. Options: {opts}")
E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
E014 = ("Unknown tag ID: {tag}")
@ -181,7 +185,7 @@ class Errors:
"list of (unicode, bool) tuples. Got bytes instance: {value}")
E029 = ("noun_chunks requires the dependency parse, which requires a "
"statistical model to be installed and loaded. For more info, see "
"the documentation:\nhttps://spacy.io/usage/models")
"the documentation:\nhttps://nightly.spacy.io/usage/models")
E030 = ("Sentence boundaries unset. You can add the 'sentencizer' "
"component to the pipeline with: "
"nlp.add_pipe('sentencizer'). "
@ -284,17 +288,17 @@ class Errors:
"Span objects, or dicts if set to manual=True.")
E097 = ("Invalid pattern: expected token pattern (list of dicts) or "
"phrase pattern (string) but got:\n{pattern}")
E098 = ("Invalid pattern specified: expected both SPEC and PATTERN.")
E099 = ("First node of pattern should be a root node. The root should "
"only contain NODE_NAME.")
E100 = ("Nodes apart from the root should contain NODE_NAME, NBOR_NAME and "
"NBOR_RELOP.")
E101 = ("NODE_NAME should be a new node and NBOR_NAME should already have "
E098 = ("Invalid pattern: expected both RIGHT_ID and RIGHT_ATTRS.")
E099 = ("Invalid pattern: the first node of pattern should be an anchor "
"node. The node should only contain RIGHT_ID and RIGHT_ATTRS.")
E100 = ("Nodes other than the anchor node should all contain LEFT_ID, "
"REL_OP and RIGHT_ID.")
E101 = ("RIGHT_ID should be a new node and LEFT_ID should already have "
"have been declared in previous edges.")
E102 = ("Can't merge non-disjoint spans. '{token}' is already part of "
"tokens to merge. If you want to find the longest non-overlapping "
"spans, you can use the util.filter_spans helper:\n"
"https://spacy.io/api/top-level#util.filter_spans")
"https://nightly.spacy.io/api/top-level#util.filter_spans")
E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A "
"token can only be part of one entity, so make sure the entities "
"you're setting don't overlap.")
@ -364,10 +368,10 @@ class Errors:
E137 = ("Expected 'dict' type, but got '{type}' from '{line}'. Make sure "
"to provide a valid JSON object as input with either the `text` "
"or `tokens` key. For more info, see the docs:\n"
"https://spacy.io/api/cli#pretrain-jsonl")
"https://nightly.spacy.io/api/cli#pretrain-jsonl")
E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input "
"includes either the `text` or `tokens` key. For more info, see "
"the docs:\nhttps://spacy.io/api/cli#pretrain-jsonl")
"the docs:\nhttps://nightly.spacy.io/api/cli#pretrain-jsonl")
E139 = ("Knowledge Base for component '{name}' is empty. Use the methods "
"kb.add_entity and kb.add_alias to add entries.")
E140 = ("The list of entities, prior probabilities and entity vectors "
@ -474,8 +478,13 @@ class Errors:
E198 = ("Unable to return {n} most similar vectors for the current vectors "
"table, which contains {n_rows} vectors.")
E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
E200 = ("Specifying a base model with a pretrained component '{component}' "
"can not be combined with adding a pretrained Tok2Vec layer.")
E201 = ("Span index out of range.")
# TODO: fix numbering after merging develop into master
E925 = ("Invalid color values for displaCy visualizer: expected dictionary "
"mapping label names to colors but got: {obj}")
E926 = ("It looks like you're trying to modify nlp.{attr} directly. This "
"doesn't work because it's an immutable computed property. If you "
"need to modify the pipeline, use the built-in methods like "
@ -652,6 +661,9 @@ class Errors:
"'{chunk}'. Tokenizer exceptions are only allowed to specify "
"`ORTH` and `NORM`.")
E1006 = ("Unable to initialize {name} model with 0 labels.")
E1007 = ("Unsupported DependencyMatcher operator '{op}'.")
E1008 = ("Invalid pattern: each pattern should be a list of dicts. Check "
"that you are providing a list of patterns as `List[List[dict]]`.")
@add_codes

6
spacy/gold/batchers.py
View File

@ -11,7 +11,7 @@ ItemT = TypeVar("ItemT")
BatcherT = Callable[[Iterable[ItemT]], Iterable[List[ItemT]]]
@registry.batchers("batch_by_padded.v1")
@registry.batchers("spacy.batch_by_padded.v1")
def configure_minibatch_by_padded_size(
*,
size: Sizing,
@ -46,7 +46,7 @@ def configure_minibatch_by_padded_size(
)
@registry.batchers("batch_by_words.v1")
@registry.batchers("spacy.batch_by_words.v1")
def configure_minibatch_by_words(
*,
size: Sizing,
@ -70,7 +70,7 @@ def configure_minibatch_by_words(
)
@registry.batchers("batch_by_sequence.v1")
@registry.batchers("spacy.batch_by_sequence.v1")
def configure_minibatch(
size: Sizing, get_length: Optional[Callable[[ItemT], int]] = None
) -> BatcherT:

2
spacy/gold/converters/conll_ner2docs.py
View File

@ -106,7 +106,7 @@ def conll_ner2docs(
raise ValueError(
"The token-per-line NER file is not formatted correctly. "
"Try checking whitespace and delimiters. See "
"https://spacy.io/api/cli#convert"
"https://nightly.spacy.io/api/cli#convert"
)
length = len(cols[0])
words.extend(cols[0])

2
spacy/gold/converters/iob2docs.py
View File

@ -44,7 +44,7 @@ def read_iob(raw_sents, vocab, n_sents):
sent_tags = ["-"] * len(sent_words)
else:
raise ValueError(
"The sentence-per-line IOB/IOB2 file is not formatted correctly. Try checking whitespace and delimiters. See https://spacy.io/api/cli#convert"
"The sentence-per-line IOB/IOB2 file is not formatted correctly. Try checking whitespace and delimiters. See https://nightly.spacy.io/api/cli#convert"
)
words.extend(sent_words)
tags.extend(sent_tags)

4
spacy/gold/corpus.py
View File

@ -38,7 +38,7 @@ class Corpus:
limit (int): Limit corpus to a subset of examples, e.g. for debugging.
Defaults to 0, which indicates no limit.
DOCS: https://spacy.io/api/corpus
DOCS: https://nightly.spacy.io/api/corpus
"""
def __init__(
@ -83,7 +83,7 @@ class Corpus:
nlp (Language): The current nlp object.
YIELDS (Example): The examples.
DOCS: https://spacy.io/api/corpus#call
DOCS: https://nightly.spacy.io/api/corpus#call
"""
ref_docs = self.read_docbin(nlp.vocab, self.walk_corpus(self.path))
if self.gold_preproc:

4
spacy/kb.pyx
View File

@ -21,7 +21,7 @@ cdef class Candidate:
algorithm which will disambiguate the various candidates to the correct one.
Each candidate (alias, entity) pair is assigned to a certain prior probability.
DOCS: https://spacy.io/api/kb/#candidate_init
DOCS: https://nightly.spacy.io/api/kb/#candidate_init
"""
def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
@ -79,7 +79,7 @@ cdef class KnowledgeBase:
"""A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
DOCS: https://spacy.io/api/kb
DOCS: https://nightly.spacy.io/api/kb
"""
def __init__(self, Vocab vocab, entity_vector_length):

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

@ -1,9 +1,11 @@
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from ...language import Language
class CzechDefaults(Language.Defaults):
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
class Czech(Language):

38
spacy/lang/cs/examples.py Normal file
View File

@ -0,0 +1,38 @@
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.cs.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Máma mele maso.",
"Příliš žluťoučký kůň úpěl ďábelské ódy.",
"ArcGIS je geografický informační systém určený pro práci s prostorovými daty.",
"Může data vytvářet a spravovat, ale především je dokáže analyzovat, najít v nich nové vztahy a vše přehledně vizualizovat.",
"Dnes je krásné počasí.",
"Nestihl autobus, protože pozdě vstal z postele.",
"Než budeš jíst, jdi si umýt ruce.",
"Dnes je neděle.",
"Škola začíná v 8:00.",
"Poslední autobus jede v jedenáct hodin večer.",
"V roce 2020 se téměř zastavila světová ekonomika.",
"Praha je hlavní město České republiky.",
"Kdy půjdeš ven?",
"Kam pojedete na dovolenou?",
"Kolik stojí iPhone 12?",
"Průměrná mzda je 30000 Kč.",
"1. ledna 1993 byla založena Česká republika.",
"Co se stalo 21.8.1968?",
"Moje telefonní číslo je 712 345 678.",
"Můj pes má blechy.",
"Když bude přes noc více než 20°, tak nás čeká tropická noc.",
"Kolik bylo letos tropických nocí?",
"Jak to mám udělat?",
"Bydlíme ve čtvrtém patře.",
"Vysílají 30. sezonu seriálu Simpsonovi.",
"Adresa ČVUT je Thákurova 7, 166 29, Praha 6.",
"Jaké PSČ má Praha 1?",
"PSČ Prahy 1 je 110 00.",
"Za 20 minut jede vlak.",
]

61
spacy/lang/cs/lex_attrs.py Normal file
View File

@ -0,0 +1,61 @@
from ...attrs import LIKE_NUM
_num_words = [
"nula",
"jedna",
"dva",
"tři",
"čtyři",
"pět",
"šest",
"sedm",
"osm",
"devět",
"deset",
"jedenáct",
"dvanáct",
"třináct",
"čtrnáct",
"patnáct",
"šestnáct",
"sedmnáct",
"osmnáct",
"devatenáct",
"dvacet",
"třicet",
"čtyřicet",
"padesát",
"šedesát",
"sedmdesát",
"osmdesát",
"devadesát",
"sto",
"tisíc",
"milion",
"miliarda",
"bilion",
"biliarda",
"trilion",
"triliarda",
"kvadrilion",
"kvadriliarda",
"kvintilion",
]
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}

117
spacy/lang/cs/stop_words.py
View File

@ -1,14 +1,23 @@
# Source: https://github.com/Alir3z4/stop-words
# Source: https://github.com/stopwords-iso/stopwords-cs/blob/master/stopwords-cs.txt
STOP_WORDS = set(
"""
ačkoli
a
aby
ahoj
ačkoli
ale
alespoň
anebo
ani
aniž
ano
atd.
atp.
asi
aspoň
až
během
bez
beze
@ -21,12 +30,14 @@ budeš
budete
budou
budu
by
byl
byla
byli
bylo
byly
bys
být
čau
chce
chceme
@ -35,14 +46,21 @@ chcete
chci
chtějí
chtít
chut'
chuť
chuti
co
což
cz
či
článek
článku
články
čtrnáct
čtyři
dál
dále
daleko
další
děkovat
děkujeme
děkuji
@ -50,6 +68,7 @@ den
deset
devatenáct
devět
dnes
do
dobrý
docela
@ -57,9 +76,15 @@ dva
dvacet
dvanáct
dvě
email
ho
hodně
i
já
jak
jakmile
jako
jakož
jde
je
jeden
@ -69,25 +94,39 @@ jedno
jednou
jedou
jeho
jehož
jej
její
jejich
jejichž
jehož
jelikož
jemu
jen
jenom
jenž
jež
ještě
jestli
jestliže
ještě
ji
jí
jich
jím
jim
jimi
jinak
jsem
jiné
již
jsi
jsme
jsem
jsou
jste
k
kam
každý
kde
kdo
kdy
@ -96,10 +135,13 @@ ke
kolik
kromě
která
kterak
kterou
které
kteří
který
kvůli
ku
má
mají
málo
@ -110,8 +152,10 @@ máte
mé
mě
mezi
mi
mí
mít
mne
mně
mnou
moc
@ -134,6 +178,7 @@ nás
náš
naše
naši
načež
ne
ně
nebo
@ -141,6 +186,7 @@ nebyl
nebyla
nebyli
nebyly
nechť
něco
nedělá
nedělají
@ -150,6 +196,7 @@ neděláš
neděláte
nějak
nejsi
nejsou
někde
někdo
nemají
@ -157,15 +204,22 @@ nemáme
nemáte
neměl
němu
němuž
není
nestačí
ně
nevadí
nové
nový
noví
než
nic
nich
ní
ním
nimi
nula
o
od
ode
on
@ -179,22 +233,37 @@ pak
patnáct
pět
po
pod
pokud
pořád
pouze
potom
pozdě
pravé
před
přede
přes
přese
přece
pro
proč
prosím
prostě
proto
proti
první
právě
protože
při
přičemž
rovně
s
se
sedm
sedmnáct
si
sice
skoro
sic
šest
šestnáct
skoro
@ -203,41 +272,69 @@ smí
snad
spolu
sta
svůj
své
svá
svých
svým
svými
svůj
sté
sto
strana
ta
tady
tak
takhle
taky
také
takže
tam
tamhle
tamhleto
támhle
támhleto
tamto
tě
tebe
tebou
ted'
teď
tedy
ten
tento
této
ti
tím
tímto
tisíc
tisíce
to
tobě
tohle
tohoto
tom
tomto
tomu
tomuto
toto
třeba
tři
třináct
trošku
trochu
tu
tuto
tvá
tvé
tvoje
tvůj
ty
tyto
těm
těma
těmi
u
určitě
už
v
vám
vámi
vás
@ -247,13 +344,19 @@ vaši
ve
večer
vedle
více
vlastně
však
všechen
všechno
všichni
vůbec
vy
vždy
z
zda
za
zde
zač
zatímco
ze

0
spacy/lang/cs/test_text.py Normal file
View File

17
spacy/lang/en/lex_attrs.py
View File

@ -8,6 +8,14 @@ _num_words = [
"fifty", "sixty", "seventy", "eighty", "ninety", "hundred", "thousand",
"million", "billion", "trillion", "quadrillion", "gajillion", "bazillion"
]
_ordinal_words = [
"first", "second", "third", "fourth", "fifth", "sixth", "seventh", "eighth",
"ninth", "tenth", "eleventh", "twelfth", "thirteenth", "fourteenth",
"fifteenth", "sixteenth", "seventeenth", "eighteenth", "nineteenth",
"twentieth", "thirtieth", "fortieth", "fiftieth", "sixtieth", "seventieth",
"eightieth", "ninetieth", "hundredth", "thousandth", "millionth", "billionth",
"trillionth", "quadrillionth", "gajillionth", "bazillionth"
]
# fmt: on
@ -21,7 +29,14 @@ def like_num(text: str) -> bool:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text.lower() in _num_words:
text_lower = text.lower()
if text_lower in _num_words:
return True
# Check ordinal number
if text_lower in _ordinal_words:
return True
if text_lower.endswith("th"):
if text_lower[:-2].isdigit():
return True
return False

3
spacy/lang/es/syntax_iterators.py
View File

@ -19,8 +19,7 @@ def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Span]:
np_left_deps = [doc.vocab.strings.add(label) for label in left_labels]
np_right_deps = [doc.vocab.strings.add(label) for label in right_labels]
stop_deps = [doc.vocab.strings.add(label) for label in stop_labels]
token = doc[0]
while token and token.i < len(doclike):
for token in doclike:
if token.pos in [PROPN, NOUN, PRON]:
left, right = noun_bounds(
doc, token, np_left_deps, np_right_deps, stop_deps

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

@ -1,9 +1,11 @@
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from ...language import Language
class HebrewDefaults(Language.Defaults):
stop_words = STOP_WORDS
lex_attr_getters = LEX_ATTRS
writing_system = {"direction": "rtl", "has_case": False, "has_letters": True}

95
spacy/lang/he/lex_attrs.py Normal file
View File

@ -0,0 +1,95 @@
from ...attrs import LIKE_NUM
_num_words = [
"אפס",
"אחד",
"אחת",
"שתיים",
"שתים",
"שניים",
"שנים",
"שלוש",
"שלושה",
"ארבע",
"ארבעה",
"חמש",
"חמישה",
"שש",
"שישה",
"שבע",
"שבעה",
"שמונה",
"תשע",
"תשעה",
"עשר",
"עשרה",
"אחד עשר",
"אחת עשרה",
"שנים עשר",
"שתים עשרה",
"שלושה עשר",
"שלוש עשרה",
"ארבעה עשר",
"ארבע עשרה",
"חמישה עשר",
"חמש עשרה",
"ששה עשר",
"שש עשרה",
"שבעה עשר",
"שבע עשרה",
"שמונה עשר",
"שמונה עשרה",
"תשעה עשר",
"תשע עשרה",
"עשרים",
"שלושים",
"ארבעים",
"חמישים",
"שישים",
"שבעים",
"שמונים",
"תשעים",
"מאה",
"אלף",
"מליון",
"מליארד",
"טריליון",
]
_ordinal_words = [
"ראשון",
"שני",
"שלישי",
"רביעי",
"חמישי",
"שישי",
"שביעי",
"שמיני",
"תשיעי",
"עשירי",
]
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 in _num_words:
return True
# CHeck ordinal number
if text in _ordinal_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}

13
spacy/lang/he/stop_words.py
View File

@ -39,7 +39,6 @@ STOP_WORDS = set(
בין
עם
עד
נגר
על
אל
מול
@ -58,7 +57,7 @@ STOP_WORDS = set(
עליך
עלינו
עליכם
לעיכן
עליכן
עליהם
עליהן
כל
@ -67,8 +66,8 @@ STOP_WORDS = set(
כך
ככה
כזה
כזאת
זה
זות
אותי
אותה
אותם
@ -91,7 +90,7 @@ STOP_WORDS = set(
איתכן
יהיה
תהיה
היתי
הייתי
היתה
היה
להיות
@ -101,8 +100,6 @@ STOP_WORDS = set(
עצמם
עצמן
עצמנו
עצמהם
עצמהן
מי
מה
איפה
@ -153,6 +150,7 @@ STOP_WORDS = set(
לאו
אי
כלל
בעד
נגד
אם
עם
@ -196,7 +194,6 @@ STOP_WORDS = set(
אשר
ואילו
למרות
אס
כמו
כפי
אז
@ -204,8 +201,8 @@ STOP_WORDS = set(
כן
לכן
לפיכך
מאד
עז
מאוד
מעט
מעטים
במידה

2
spacy/lang/hi/examples.py
View File

@ -15,4 +15,6 @@ sentences = [
"फ्रांस के राष्ट्रपति कौन हैं?",
"संयुक्त राज्यों की राजधानी क्या है?",
"बराक ओबामा का जन्म कब हुआ था?",
"जवाहरलाल नेहरू भारत के पहले प्रधानमंत्री हैं।",
"राजेंद्र प्रसाद, भारत के पहले राष्ट्रपति, दो कार्यकाल के लिए कार्यालय रखने वाले एकमात्र व्यक्ति हैं।",
]

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

@ -254,7 +254,7 @@ def get_dtokens_and_spaces(dtokens, text, gap_tag="空白"):
return text_dtokens, text_spaces
# align words and dtokens by referring text, and insert gap tokens for the space char spans
for word, dtoken in zip(words, dtokens):
for i, (word, dtoken) in enumerate(zip(words, dtokens)):
# skip all space tokens
if word.isspace():
continue
@ -275,7 +275,7 @@ def get_dtokens_and_spaces(dtokens, text, gap_tag="空白"):
text_spaces.append(False)
text_pos += len(word)
# poll a space char after the word
if text_pos < len(text) and text[text_pos] == " ":
if i + 1 < len(dtokens) and dtokens[i + 1].surface == " ":
text_spaces[-1] = True
text_pos += 1

2
spacy/lang/lex_attrs.py
View File

@ -8,7 +8,7 @@ from .. import attrs
_like_email = re.compile(r"([a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)").match
_tlds = set(
"com|org|edu|gov|net|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|"
"name|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|"
"name|pro|tel|travel|xyz|icu|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|"
"ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|"
"cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|"
"ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|"

4
spacy/lang/ne/stop_words.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
# Source: https://github.com/sanjaalcorps/NepaliStopWords/blob/master/NepaliStopWords.txt
STOP_WORDS = set(

16
spacy/lang/sa/__init__.py Normal file
View File

@ -0,0 +1,16 @@
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from ...language import Language
class SanskritDefaults(Language.Defaults):
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
class Sanskrit(Language):
lang = "sa"
Defaults = SanskritDefaults
__all__ = ["Sanskrit"]

15
spacy/lang/sa/examples.py Normal file
View File

@ -0,0 +1,15 @@
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.sa.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"अभ्यावहति कल्याणं विविधं वाक् सुभाषिता ।",
"मनसि व्याकुले चक्षुः पश्यन्नपि न पश्यति ।",
"यस्य बुद्धिर्बलं तस्य निर्बुद्धेस्तु कुतो बलम्?",
"परो अपि हितवान् बन्धुः बन्धुः अपि अहितः परः ।",
"अहितः देहजः व्याधिः हितम् आरण्यं औषधम् ॥",
]

127
spacy/lang/sa/lex_attrs.py Normal file
View File

@ -0,0 +1,127 @@
from ...attrs import LIKE_NUM
# reference 1: https://en.wikibooks.org/wiki/Sanskrit/Numbers
_num_words = [
"एकः",
"द्वौ",
"त्रयः",
"चत्वारः",
"पञ्च",
"षट्",
"सप्त",
"अष्ट",
"नव",
"दश",
"एकादश",
"द्वादश",
"त्रयोदश",
"चतुर्दश",
"पञ्चदश",
"षोडश",
"सप्तदश",
"अष्टादश",
"एकान्नविंशति",
"विंशति",
"एकाविंशति",
"द्वाविंशति",
"त्रयोविंशति",
"चतुर्विंशति",
"पञ्चविंशति",
"षड्विंशति",
"सप्तविंशति",
"अष्टाविंशति",
"एकान्नत्रिंशत्",
"त्रिंशत्",
"एकत्रिंशत्",
"द्वात्रिंशत्",
"त्रयत्रिंशत्",
"चतुस्त्रिंशत्",
"पञ्चत्रिंशत्",
"षट्त्रिंशत्",
"सप्तत्रिंशत्",
"अष्टात्रिंशत्",
"एकोनचत्वारिंशत्",
"चत्वारिंशत्",
"एकचत्वारिंशत्",
"द्वाचत्वारिंशत्",
"त्रयश्चत्वारिंशत्",
"चतुश्चत्वारिंशत्",
"पञ्चचत्वारिंशत्",
"षट्चत्वारिंशत्",
"सप्तचत्वारिंशत्",
"अष्टाचत्वारिंशत्",
"एकोनपञ्चाशत्",
"पञ्चाशत्",
"एकपञ्चाशत्",
"द्विपञ्चाशत्",
"त्रिपञ्चाशत्",
"चतुःपञ्चाशत्",
"पञ्चपञ्चाशत्",
"षट्पञ्चाशत्",
"सप्तपञ्चाशत्",
"अष्टपञ्चाशत्",
"एकोनषष्ठिः",
"षष्ठिः",
"एकषष्ठिः",
"द्विषष्ठिः",
"त्रिषष्ठिः",
"चतुःषष्ठिः",
"पञ्चषष्ठिः",
"षट्षष्ठिः",
"सप्तषष्ठिः",
"अष्टषष्ठिः",
"एकोनसप्ततिः",
"सप्ततिः",
"एकसप्ततिः",
"द्विसप्ततिः",
"त्रिसप्ततिः",
"चतुःसप्ततिः",
"पञ्चसप्ततिः",
"षट्सप्ततिः",
"सप्तसप्ततिः",
"अष्टसप्ततिः",
"एकोनाशीतिः",
"अशीतिः",
"एकाशीतिः",
"द्वशीतिः",
"त्र्यशीतिः",
"चतुरशीतिः",
"पञ्चाशीतिः",
"षडशीतिः",
"सप्ताशीतिः",
"अष्टाशीतिः",
"एकोननवतिः",
"नवतिः",
"एकनवतिः",
"द्विनवतिः",
"त्रिनवतिः",
"चतुर्नवतिः",
"पञ्चनवतिः",
"षण्णवतिः",
"सप्तनवतिः",
"अष्टनवतिः",
"एकोनशतम्",
"शतम्",
]
def like_num(text):
"""
Check if text resembles a number
"""
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 in _num_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}

515
spacy/lang/sa/stop_words.py Normal file
View File

@ -0,0 +1,515 @@
# Source: https://gist.github.com/Akhilesh28/fe8b8e180f64b72e64751bc31cb6d323
STOP_WORDS = set(
"""
अहम्
आवाम्
वयम्
माम् मा
आवाम्
अस्मान् नः
मया
आवाभ्याम्
अस्माभिस्
मह्यम् मे
आवाभ्याम् नौ
अस्मभ्यम् नः
मत्
आवाभ्याम्
अस्मत्
मम मे
आवयोः
अस्माकम् नः
मयि
आवयोः
अस्मासु
त्वम्
युवाम्
यूयम्
त्वाम् त्वा
युवाम् वाम्
युष्मान् वः
त्वया
युवाभ्याम्
युष्माभिः
तुभ्यम् ते
युवाभ्याम् वाम्
युष्मभ्यम् वः
त्वत्
युवाभ्याम्
युष्मत्
तव ते
युवयोः वाम्
युष्माकम् वः
त्वयि
युवयोः
युष्मासु
सः
तौ
ते
तम्
तौ
तान्
तेन
ताभ्याम्
तैः
तस्मै
ताभ्याम्
तेभ्यः
तस्मात्
ताभ्याम्
तेभ्यः
तस्य
तयोः
तेषाम्
तस्मिन्
तयोः
तेषु
सा
ते
ताः
ताम्
ते
ताः
तया
ताभ्याम्
ताभिः
तस्यै
ताभ्याम्
ताभ्यः
तस्याः
ताभ्याम्
ताभ्यः
तस्य
तयोः
तासाम्
तस्याम्
तयोः
तासु
तत्
ते
तानि
तत्
ते
तानि
तया
ताभ्याम्
ताभिः
तस्यै
ताभ्याम्
ताभ्यः
तस्याः
ताभ्याम्
ताभ्यः
तस्य
तयोः
तासाम्
तस्याम्
तयोः
तासु
अयम्
इमौ
इमे
इमम्
इमौ
इमान्
अनेन
आभ्याम्
एभिः
अस्मै
आभ्याम्
एभ्यः
अस्मात्
आभ्याम्
एभ्यः
अस्य
अनयोः
एषाम्
अस्मिन्
अनयोः
एषु
इयम्
इमे
इमाः
इमाम्
इमे
इमाः
अनया
आभ्याम्
आभिः
अस्यै
आभ्याम्
आभ्यः
अस्याः
आभ्याम्
आभ्यः
अस्याः
अनयोः
आसाम्
अस्याम्
अनयोः
आसु
इदम्
इमे
इमानि
इदम्
इमे
इमानि
अनेन
आभ्याम्
एभिः
अस्मै
आभ्याम्
एभ्यः
अस्मात्
आभ्याम्
एभ्यः
अस्य
अनयोः
एषाम्
अस्मिन्
अनयोः
एषु
एषः
एतौ
एते
एतम् एनम्
एतौ एनौ
एतान् एनान्
एतेन
एताभ्याम्
एतैः
एतस्मै
एताभ्याम्
एतेभ्यः
एतस्मात्
एताभ्याम्
एतेभ्यः
एतस्य
एतस्मिन्
एतेषाम्
एतस्मिन्
एतस्मिन्
एतेषु
एषा
एते
एताः
एताम् एनाम्
एते एने
एताः एनाः
एतया एनया
एताभ्याम्
एताभिः
एतस्यै
एताभ्याम्
एताभ्यः
एतस्याः
एताभ्याम्
एताभ्यः
एतस्याः
एतयोः एनयोः
एतासाम्
एतस्याम्
एतयोः एनयोः
एतासु
एतत् एतद्
एते
एतानि
एतत् एतद् एनत् एनद्
एते एने
एतानि एनानि
एतेन एनेन
एताभ्याम्
एतैः
एतस्मै
एताभ्याम्
एतेभ्यः
एतस्मात्
एताभ्याम्
एतेभ्यः
एतस्य
एतयोः एनयोः
एतेषाम्
एतस्मिन्
एतयोः एनयोः
एतेषु
असौ
अमू
अमी
अमूम्
अमू
अमून्
अमुना
अमूभ्याम्
अमीभिः
अमुष्मै
अमूभ्याम्
अमीभ्यः
अमुष्मात्
अमूभ्याम्
अमीभ्यः
अमुष्य
अमुयोः
अमीषाम्
अमुष्मिन्
अमुयोः
अमीषु
असौ
अमू
अमूः
अमूम्
अमू
अमूः
अमुया
अमूभ्याम्
अमूभिः
अमुष्यै
अमूभ्याम्
अमूभ्यः
अमुष्याः
अमूभ्याम्
अमूभ्यः
अमुष्याः
अमुयोः
अमूषाम्
अमुष्याम्
अमुयोः
अमूषु
अमु
अमुनी
अमूनि
अमु
अमुनी
अमूनि
अमुना
अमूभ्याम्
अमीभिः
अमुष्मै
अमूभ्याम्
अमीभ्यः
अमुष्मात्
अमूभ्याम्
अमीभ्यः
अमुष्य
अमुयोः
अमीषाम्
अमुष्मिन्
अमुयोः
अमीषु
कः
कौ
के
कम्
कौ
कान्
केन
काभ्याम्
कैः
कस्मै
काभ्याम्
केभ्य
कस्मात्
काभ्याम्
केभ्य
कस्य
कयोः
केषाम्
कस्मिन्
कयोः
केषु
का
के
काः
काम्
के
काः
कया
काभ्याम्
काभिः
कस्यै
काभ्याम्
काभ्यः
कस्याः
काभ्याम्
काभ्यः
कस्याः
कयोः
कासाम्
कस्याम्
कयोः
कासु
किम्
के
कानि
किम्
के
कानि
केन
काभ्याम्
कैः
कस्मै
काभ्याम्
केभ्य
कस्मात्
काभ्याम्
केभ्य
कस्य
कयोः
केषाम्
कस्मिन्
कयोः
केषु
भवान्
भवन्तौ
भवन्तः
भवन्तम्
भवन्तौ
भवतः
भवता
भवद्भ्याम्
भवद्भिः
भवते
भवद्भ्याम्
भवद्भ्यः
भवतः
भवद्भ्याम्
भवद्भ्यः
भवतः
भवतोः
भवताम्
भवति
भवतोः
भवत्सु
भवती
भवत्यौ
भवत्यः
भवतीम्
भवत्यौ
भवतीः
भवत्या
भवतीभ्याम्
भवतीभिः
भवत्यै
भवतीभ्याम्
भवतीभिः
भवत्याः
भवतीभ्याम्
भवतीभिः
भवत्याः
भवत्योः
भवतीनाम्
भवत्याम्
भवत्योः
भवतीषु
भवत्
भवती
भवन्ति
भवत्
भवती
भवन्ति
भवता
भवद्भ्याम्
भवद्भिः
भवते
भवद्भ्याम्
भवद्भ्यः
भवतः
भवद्भ्याम्
भवद्भ्यः
भवतः
भवतोः
भवताम्
भवति
भवतोः
भवत्सु
अये
अरे
अरेरे
अविधा
असाधुना
अस्तोभ
अहह
अहावस्
आम्
आर्यहलम्
आह
आहो
इस्
उम्
उवे
काम्
कुम्
चमत्
टसत्
दृन्
धिक्
पाट्
फत्
फाट्
फुडुत्
बत
बाल्
वट्
व्यवस्तोभति व्यवस्तुभ्
षाट्
स्तोभ
हुम्मा
हूम्
अति
अधि
अनु
अप
अपि
अभि
अव
आ
उद्
उप
नि
निर्
परा
परि
प्र
प्रति
वि
सम्
अथवा उत
अन्यथा
इव
च
चेत् यदि
तु परन्तु
यतः करणेन हि यतस् यदर्थम् यदर्थे यर्हि यथा यत्कारणम् येन ही हिन
यथा यतस्
यद्यपि
यात् अवधेस् यावति
येन प्रकारेण
स्थाने
अह
एव
एवम्
कच्चित्
कु
कुवित्
कूपत्
च
चण्
चेत्
तत्र
नकिम्
नह
नुनम्
नेत्
भूयस्
मकिम्
मकिर्
यत्र
युगपत्
वा
शश्वत्
सूपत्
ह
हन्त
हि
""".split()
)

18
spacy/lang/tokenizer_exceptions.py
View File

@ -34,13 +34,13 @@ URL_PATTERN = (
r"|"
# host & domain names
# mods: match is case-sensitive, so include [A-Z]
"(?:" # noqa: E131
"(?:"
"[A-Za-z0-9\u00a1-\uffff]"
"[A-Za-z0-9\u00a1-\uffff_-]{0,62}"
")?"
"[A-Za-z0-9\u00a1-\uffff]\."
")+"
r"(?:" # noqa: E131
r"(?:"
r"[A-Za-z0-9\u00a1-\uffff]"
r"[A-Za-z0-9\u00a1-\uffff_-]{0,62}"
r")?"
r"[A-Za-z0-9\u00a1-\uffff]\."
r")+"
# TLD identifier
# mods: use ALPHA_LOWER instead of a wider range so that this doesn't match
# strings like "lower.Upper", which can be split on "." by infixes in some
@ -128,6 +128,8 @@ emoticons = set(
:-]
[:
[-:
[=
=]
:o)
(o:
:}
@ -159,6 +161,8 @@ emoticons = set(
=|
:|
:-|
]=
=[
:1
:P
:-P

65
spacy/language.py
View File

@ -3,7 +3,6 @@ from typing import Tuple, Iterator
from dataclasses import dataclass
import random
import itertools
import weakref
import functools
from contextlib import contextmanager
from copy import deepcopy
@ -95,7 +94,7 @@ class Language:
object and processing pipeline.
lang (str): Two-letter language ID, i.e. ISO code.
DOCS: https://spacy.io/api/language
DOCS: https://nightly.spacy.io/api/language
"""
Defaults = BaseDefaults
@ -130,7 +129,7 @@ class Language:
create_tokenizer (Callable): Function that takes the nlp object and
returns a tokenizer.
DOCS: https://spacy.io/api/language#init
DOCS: https://nightly.spacy.io/api/language#init
"""
# We're only calling this to import all factories provided via entry
# points. The factory decorator applied to these functions takes care
@ -185,14 +184,14 @@ class Language:
RETURNS (Dict[str, Any]): The meta.
DOCS: https://spacy.io/api/language#meta
DOCS: https://nightly.spacy.io/api/language#meta
"""
spacy_version = util.get_model_version_range(about.__version__)
if self.vocab.lang:
self._meta.setdefault("lang", self.vocab.lang)
else:
self._meta.setdefault("lang", self.lang)
self._meta.setdefault("name", "model")
self._meta.setdefault("name", "pipeline")
self._meta.setdefault("version", "0.0.0")
self._meta.setdefault("spacy_version", spacy_version)
self._meta.setdefault("description", "")
@ -211,6 +210,7 @@ class Language:
# TODO: Adding this back to prevent breaking people's code etc., but
# we should consider removing it
self._meta["pipeline"] = list(self.pipe_names)
self._meta["components"] = list(self.component_names)
self._meta["disabled"] = list(self.disabled)
return self._meta
@ -225,7 +225,7 @@ class Language:
RETURNS (thinc.api.Config): The config.
DOCS: https://spacy.io/api/language#config
DOCS: https://nightly.spacy.io/api/language#config
"""
self._config.setdefault("nlp", {})
self._config.setdefault("training", {})
@ -433,7 +433,7 @@ class Language:
will be combined and normalized for the whole pipeline.
func (Optional[Callable]): Factory function if not used as a decorator.
DOCS: https://spacy.io/api/language#factory
DOCS: https://nightly.spacy.io/api/language#factory
"""
if not isinstance(name, str):
raise ValueError(Errors.E963.format(decorator="factory"))
@ -513,7 +513,7 @@ class Language:
Used for pipeline analysis.
func (Optional[Callable]): Factory function if not used as a decorator.
DOCS: https://spacy.io/api/language#component
DOCS: https://nightly.spacy.io/api/language#component
"""
if name is not None and not isinstance(name, str):
raise ValueError(Errors.E963.format(decorator="component"))
@ -579,7 +579,7 @@ class Language:
name (str): Name of pipeline component to get.
RETURNS (callable): The pipeline component.
DOCS: https://spacy.io/api/language#get_pipe
DOCS: https://nightly.spacy.io/api/language#get_pipe
"""
for pipe_name, component in self._components:
if pipe_name == name:
@ -608,7 +608,7 @@ class Language:
arguments and types expected by the factory.
RETURNS (Callable[[Doc], Doc]): The pipeline component.
DOCS: https://spacy.io/api/language#create_pipe
DOCS: https://nightly.spacy.io/api/language#create_pipe
"""
name = name if name is not None else factory_name
if not isinstance(config, dict):
@ -722,7 +722,7 @@ class Language:
arguments and types expected by the factory.
RETURNS (Callable[[Doc], Doc]): The pipeline component.
DOCS: https://spacy.io/api/language#add_pipe
DOCS: https://nightly.spacy.io/api/language#add_pipe
"""
if not isinstance(factory_name, str):
bad_val = repr(factory_name)
@ -820,7 +820,7 @@ class Language:
name (str): Name of the component.
RETURNS (bool): Whether a component of the name exists in the pipeline.
DOCS: https://spacy.io/api/language#has_pipe
DOCS: https://nightly.spacy.io/api/language#has_pipe
"""
return name in self.pipe_names
@ -841,7 +841,7 @@ class Language:
validate (bool): Whether to validate the component config against the
arguments and types expected by the factory.
DOCS: https://spacy.io/api/language#replace_pipe
DOCS: https://nightly.spacy.io/api/language#replace_pipe
"""
if name not in self.pipe_names:
raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names))
@ -870,7 +870,7 @@ class Language:
old_name (str): Name of the component to rename.
new_name (str): New name of the component.
DOCS: https://spacy.io/api/language#rename_pipe
DOCS: https://nightly.spacy.io/api/language#rename_pipe
"""
if old_name not in self.component_names:
raise ValueError(
@ -891,7 +891,7 @@ class Language:
name (str): Name of the component to remove.
RETURNS (tuple): A `(name, component)` tuple of the removed component.
DOCS: https://spacy.io/api/language#remove_pipe
DOCS: https://nightly.spacy.io/api/language#remove_pipe
"""
if name not in self.component_names:
raise ValueError(Errors.E001.format(name=name, opts=self.component_names))
@ -944,7 +944,7 @@ class Language:
keyword arguments for specific components.
RETURNS (Doc): A container for accessing the annotations.
DOCS: https://spacy.io/api/language#call
DOCS: https://nightly.spacy.io/api/language#call
"""
if len(text) > self.max_length:
raise ValueError(
@ -993,7 +993,7 @@ class Language:
disable (str or iterable): The name(s) of the pipes to disable
enable (str or iterable): The name(s) of the pipes to enable - all others will be disabled
DOCS: https://spacy.io/api/language#select_pipes
DOCS: https://nightly.spacy.io/api/language#select_pipes
"""
if enable is None and disable is None:
raise ValueError(Errors.E991)
@ -1044,7 +1044,7 @@ class Language:
exclude (Iterable[str]): Names of components that shouldn't be updated.
RETURNS (Dict[str, float]): The updated losses dictionary
DOCS: https://spacy.io/api/language#update
DOCS: https://nightly.spacy.io/api/language#update
"""
if _ is not None:
raise ValueError(Errors.E989)
@ -1106,7 +1106,7 @@ class Language:
>>> raw_batch = [Example.from_dict(nlp.make_doc(text), {}) for text in next(raw_text_batches)]
>>> nlp.rehearse(raw_batch)
DOCS: https://spacy.io/api/language#rehearse
DOCS: https://nightly.spacy.io/api/language#rehearse
"""
if len(examples) == 0:
return
@ -1153,7 +1153,7 @@ class Language:
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/language#begin_training
DOCS: https://nightly.spacy.io/api/language#begin_training
"""
# TODO: throw warning when get_gold_tuples is provided instead of get_examples
if get_examples is None:
@ -1200,7 +1200,7 @@ class Language:
sgd (Optional[Optimizer]): An optimizer.
RETURNS (Optimizer): The optimizer.
DOCS: https://spacy.io/api/language#resume_training
DOCS: https://nightly.spacy.io/api/language#resume_training
"""
if device >= 0: # TODO: do we need this here?
require_gpu(device)
@ -1236,7 +1236,7 @@ class Language:
for the scorer.
RETURNS (Scorer): The scorer containing the evaluation results.
DOCS: https://spacy.io/api/language#evaluate
DOCS: https://nightly.spacy.io/api/language#evaluate
"""
validate_examples(examples, "Language.evaluate")
if component_cfg is None:
@ -1275,7 +1275,7 @@ class Language:
return results
@contextmanager
def use_params(self, params: dict):
def use_params(self, params: Optional[dict]):
"""Replace weights of models in the pipeline with those provided in the
params dictionary. Can be used as a contextmanager, in which case,
models go back to their original weights after the block.
@ -1286,8 +1286,11 @@ class Language:
>>> with nlp.use_params(optimizer.averages):
>>> nlp.to_disk("/tmp/checkpoint")
DOCS: https://spacy.io/api/language#use_params
DOCS: https://nightly.spacy.io/api/language#use_params
"""
if not params:
yield
else:
contexts = [
pipe.use_params(params)
for name, pipe in self.pipeline
@ -1330,7 +1333,7 @@ class Language:
n_process (int): Number of processors to process texts. If -1, set `multiprocessing.cpu_count()`.
YIELDS (Doc): Documents in the order of the original text.
DOCS: https://spacy.io/api/language#pipe
DOCS: https://nightly.spacy.io/api/language#pipe
"""
if n_process == -1:
n_process = mp.cpu_count()
@ -1374,8 +1377,6 @@ class Language:
docs = (self.make_doc(text) for text in texts)
for pipe in pipes:
docs = pipe(docs)
nr_seen = 0
for doc in docs:
yield doc
@ -1466,7 +1467,7 @@ class Language:
the types expected by the factory.
RETURNS (Language): The initialized Language class.
DOCS: https://spacy.io/api/language#from_config
DOCS: https://nightly.spacy.io/api/language#from_config
"""
if auto_fill:
config = Config(
@ -1579,7 +1580,7 @@ class Language:
it doesn't exist.
exclude (list): Names of components or serialization fields to exclude.
DOCS: https://spacy.io/api/language#to_disk
DOCS: https://nightly.spacy.io/api/language#to_disk
"""
path = util.ensure_path(path)
serializers = {}
@ -1608,7 +1609,7 @@ class Language:
exclude (list): Names of components or serialization fields to exclude.
RETURNS (Language): The modified `Language` object.
DOCS: https://spacy.io/api/language#from_disk
DOCS: https://nightly.spacy.io/api/language#from_disk
"""
def deserialize_meta(path: Path) -> None:
@ -1656,7 +1657,7 @@ class Language:
exclude (list): Names of components or serialization fields to exclude.
RETURNS (bytes): The serialized form of the `Language` object.
DOCS: https://spacy.io/api/language#to_bytes
DOCS: https://nightly.spacy.io/api/language#to_bytes
"""
serializers = {}
serializers["vocab"] = lambda: self.vocab.to_bytes()
@ -1680,7 +1681,7 @@ class Language:
exclude (list): Names of components or serialization fields to exclude.
RETURNS (Language): The `Language` object.
DOCS: https://spacy.io/api/language#from_bytes
DOCS: https://nightly.spacy.io/api/language#from_bytes
"""
def deserialize_meta(b):

2
spacy/lexeme.pyx
View File

@ -30,7 +30,7 @@ cdef class Lexeme:
tag, dependency parse, or lemma (lemmatization depends on the
part-of-speech tag).
DOCS: https://spacy.io/api/lexeme
DOCS: https://nightly.spacy.io/api/lexeme
"""
def __init__(self, Vocab vocab, attr_t orth):
"""Create a Lexeme object.

28
spacy/lookups.py
View File

@ -57,7 +57,7 @@ class Table(OrderedDict):
data (dict): The dictionary.
name (str): Optional table name for reference.
DOCS: https://spacy.io/api/lookups#table.from_dict
DOCS: https://nightly.spacy.io/api/lookups#table.from_dict
"""
self = cls(name=name)
self.update(data)
@ -69,7 +69,7 @@ class Table(OrderedDict):
name (str): Optional table name for reference.
data (dict): Initial data, used to hint Bloom Filter.
DOCS: https://spacy.io/api/lookups#table.init
DOCS: https://nightly.spacy.io/api/lookups#table.init
"""
OrderedDict.__init__(self)
self.name = name
@ -135,7 +135,7 @@ class Table(OrderedDict):
RETURNS (bytes): The serialized table.
DOCS: https://spacy.io/api/lookups#table.to_bytes
DOCS: https://nightly.spacy.io/api/lookups#table.to_bytes
"""
data = {
"name": self.name,
@ -150,7 +150,7 @@ class Table(OrderedDict):
bytes_data (bytes): The data to load.
RETURNS (Table): The loaded table.
DOCS: https://spacy.io/api/lookups#table.from_bytes
DOCS: https://nightly.spacy.io/api/lookups#table.from_bytes
"""
loaded = srsly.msgpack_loads(bytes_data)
data = loaded.get("dict", {})
@ -172,7 +172,7 @@ class Lookups:
def __init__(self) -> None:
"""Initialize the Lookups object.
DOCS: https://spacy.io/api/lookups#init
DOCS: https://nightly.spacy.io/api/lookups#init
"""
self._tables = {}
@ -201,7 +201,7 @@ class Lookups:
data (dict): Optional data to add to the table.
RETURNS (Table): The newly added table.
DOCS: https://spacy.io/api/lookups#add_table
DOCS: https://nightly.spacy.io/api/lookups#add_table
"""
if name in self.tables:
raise ValueError(Errors.E158.format(name=name))
@ -215,7 +215,7 @@ class Lookups:
name (str): Name of the table to set.
table (Table): The Table to set.
DOCS: https://spacy.io/api/lookups#set_table
DOCS: https://nightly.spacy.io/api/lookups#set_table
"""
self._tables[name] = table
@ -227,7 +227,7 @@ class Lookups:
default (Any): Optional default value to return if table doesn't exist.
RETURNS (Table): The table.
DOCS: https://spacy.io/api/lookups#get_table
DOCS: https://nightly.spacy.io/api/lookups#get_table
"""
if name not in self._tables:
if default == UNSET:
@ -241,7 +241,7 @@ class Lookups:
name (str): Name of the table to remove.
RETURNS (Table): The removed table.
DOCS: https://spacy.io/api/lookups#remove_table
DOCS: https://nightly.spacy.io/api/lookups#remove_table
"""
if name not in self._tables:
raise KeyError(Errors.E159.format(name=name, tables=self.tables))
@ -253,7 +253,7 @@ class Lookups:
name (str): Name of the table.
RETURNS (bool): Whether a table of that name exists.
DOCS: https://spacy.io/api/lookups#has_table
DOCS: https://nightly.spacy.io/api/lookups#has_table
"""
return name in self._tables
@ -262,7 +262,7 @@ class Lookups:
RETURNS (bytes): The serialized Lookups.
DOCS: https://spacy.io/api/lookups#to_bytes
DOCS: https://nightly.spacy.io/api/lookups#to_bytes
"""
return srsly.msgpack_dumps(self._tables)
@ -272,7 +272,7 @@ class Lookups:
bytes_data (bytes): The data to load.
RETURNS (Lookups): The loaded Lookups.
DOCS: https://spacy.io/api/lookups#from_bytes
DOCS: https://nightly.spacy.io/api/lookups#from_bytes
"""
self._tables = {}
for key, value in srsly.msgpack_loads(bytes_data).items():
@ -287,7 +287,7 @@ class Lookups:
path (str / Path): The file path.
DOCS: https://spacy.io/api/lookups#to_disk
DOCS: https://nightly.spacy.io/api/lookups#to_disk
"""
if len(self._tables):
path = ensure_path(path)
@ -306,7 +306,7 @@ class Lookups:
path (str / Path): The directory path.
RETURNS (Lookups): The loaded lookups.
DOCS: https://spacy.io/api/lookups#from_disk
DOCS: https://nightly.spacy.io/api/lookups#from_disk
"""
path = ensure_path(path)
filepath = path / filename

186
spacy/matcher/dependencymatcher.pyx
View File

@ -1,16 +1,16 @@
# cython: infer_types=True, profile=True
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from libcpp cimport bool
from typing import List
import numpy
from cymem.cymem cimport Pool
from .matcher cimport Matcher
from ..vocab cimport Vocab
from ..tokens.doc cimport Doc
from .matcher import unpickle_matcher
from ..errors import Errors
from ..tokens import Span
DELIMITER = "||"
@ -22,36 +22,52 @@ cdef class DependencyMatcher:
"""Match dependency parse tree based on pattern rules."""
cdef Pool mem
cdef readonly Vocab vocab
cdef readonly Matcher token_matcher
cdef readonly Matcher matcher
cdef public object _patterns
cdef public object _raw_patterns
cdef public object _keys_to_token
cdef public object _root
cdef public object _entities
cdef public object _callbacks
cdef public object _nodes
cdef public object _tree
cdef public object _ops
def __init__(self, vocab):
def __init__(self, vocab, *, validate=False):
"""Create the DependencyMatcher.
vocab (Vocab): The vocabulary object, which must be shared with the
documents the matcher will operate on.
validate (bool): Whether patterns should be validated, passed to
Matcher as `validate`
"""
size = 20
# TODO: make matcher work with validation
self.token_matcher = Matcher(vocab, validate=False)
self.matcher = Matcher(vocab, validate=validate)
self._keys_to_token = {}
self._patterns = {}
self._raw_patterns = {}
self._root = {}
self._nodes = {}
self._tree = {}
self._entities = {}
self._callbacks = {}
self.vocab = vocab
self.mem = Pool()
self._ops = {
"<": self.dep,
">": self.gov,
"<<": self.dep_chain,
">>": self.gov_chain,
".": self.imm_precede,
".*": self.precede,
";": self.imm_follow,
";*": self.follow,
"$+": self.imm_right_sib,
"$-": self.imm_left_sib,
"$++": self.right_sib,
"$--": self.left_sib,
}
def __reduce__(self):
data = (self.vocab, self._patterns,self._tree, self._callbacks)
data = (self.vocab, self._raw_patterns, self._callbacks)
return (unpickle_matcher, data, None, None)
def __len__(self):
@ -74,54 +90,61 @@ cdef class DependencyMatcher:
idx = 0
visited_nodes = {}
for relation in pattern:
if "PATTERN" not in relation or "SPEC" not in relation:
if not isinstance(relation, dict):
raise ValueError(Errors.E1008)
if "RIGHT_ATTRS" not in relation and "RIGHT_ID" not in relation:
raise ValueError(Errors.E098.format(key=key))
if idx == 0:
if not(
"NODE_NAME" in relation["SPEC"]
and "NBOR_RELOP" not in relation["SPEC"]
and "NBOR_NAME" not in relation["SPEC"]
"RIGHT_ID" in relation
and "REL_OP" not in relation
and "LEFT_ID" not in relation
):
raise ValueError(Errors.E099.format(key=key))
visited_nodes[relation["SPEC"]["NODE_NAME"]] = True
visited_nodes[relation["RIGHT_ID"]] = True
else:
if not(
"NODE_NAME" in relation["SPEC"]
and "NBOR_RELOP" in relation["SPEC"]
and "NBOR_NAME" in relation["SPEC"]
"RIGHT_ID" in relation
and "RIGHT_ATTRS" in relation
and "REL_OP" in relation
and "LEFT_ID" in relation
):
raise ValueError(Errors.E100.format(key=key))
if (
relation["SPEC"]["NODE_NAME"] in visited_nodes
or relation["SPEC"]["NBOR_NAME"] not in visited_nodes
relation["RIGHT_ID"] in visited_nodes
or relation["LEFT_ID"] not in visited_nodes
):
raise ValueError(Errors.E101.format(key=key))
visited_nodes[relation["SPEC"]["NODE_NAME"]] = True
visited_nodes[relation["SPEC"]["NBOR_NAME"]] = True
if relation["REL_OP"] not in self._ops:
raise ValueError(Errors.E1007.format(op=relation["REL_OP"]))
visited_nodes[relation["RIGHT_ID"]] = True
visited_nodes[relation["LEFT_ID"]] = True
idx = idx + 1
def add(self, key, patterns, *_patterns, on_match=None):
def add(self, key, patterns, *, on_match=None):
"""Add a new matcher rule to the matcher.
key (str): The match ID.
patterns (list): The patterns to add for the given key.
on_match (callable): Optional callback executed on match.
"""
if patterns is None or hasattr(patterns, "__call__"): # old API
on_match = patterns
patterns = _patterns
if on_match is not None and not hasattr(on_match, "__call__"):
raise ValueError(Errors.E171.format(arg_type=type(on_match)))
if patterns is None or not isinstance(patterns, List): # old API
raise ValueError(Errors.E948.format(arg_type=type(patterns)))
for pattern in patterns:
if len(pattern) == 0:
raise ValueError(Errors.E012.format(key=key))
self.validate_input(pattern, key)
key = self._normalize_key(key)
self._raw_patterns.setdefault(key, [])
self._raw_patterns[key].extend(patterns)
_patterns = []
for pattern in patterns:
token_patterns = []
for i in range(len(pattern)):
token_pattern = [pattern[i]["PATTERN"]]
token_pattern = [pattern[i]["RIGHT_ATTRS"]]
token_patterns.append(token_pattern)
# self.patterns.append(token_patterns)
_patterns.append(token_patterns)
self._patterns.setdefault(key, [])
self._callbacks[key] = on_match
@ -135,7 +158,7 @@ cdef class DependencyMatcher:
# TODO: Better ways to hash edges in pattern?
for j in range(len(_patterns[i])):
k = self._normalize_key(unicode(key) + DELIMITER + unicode(i) + DELIMITER + unicode(j))
self.token_matcher.add(k, [_patterns[i][j]])
self.matcher.add(k, [_patterns[i][j]])
_keys_to_token[k] = j
_keys_to_token_list.append(_keys_to_token)
self._keys_to_token.setdefault(key, [])
@ -144,7 +167,7 @@ cdef class DependencyMatcher:
for pattern in patterns:
nodes = {}
for i in range(len(pattern)):
nodes[pattern[i]["SPEC"]["NODE_NAME"]] = i
nodes[pattern[i]["RIGHT_ID"]] = i
_nodes_list.append(nodes)
self._nodes.setdefault(key, [])
self._nodes[key].extend(_nodes_list)
@ -161,13 +184,13 @@ cdef class DependencyMatcher:
root = -1
for j in range(len(patterns[i])):
token_pattern = patterns[i][j]
if ("NBOR_RELOP" not in token_pattern["SPEC"]):
if ("REL_OP" not in token_pattern):
heads[j] = ('root', j)
root = j
else:
heads[j] = (
token_pattern["SPEC"]["NBOR_RELOP"],
_nodes_list[i][token_pattern["SPEC"]["NBOR_NAME"]]
token_pattern["REL_OP"],
_nodes_list[i][token_pattern["LEFT_ID"]]
)
_heads_list.append(heads)
_root_list.append(root)
@ -202,11 +225,21 @@ cdef class DependencyMatcher:
RETURNS (tuple): The rule, as an (on_match, patterns) tuple.
"""
key = self._normalize_key(key)
if key not in self._patterns:
if key not in self._raw_patterns:
return default
return (self._callbacks[key], self._patterns[key])
return (self._callbacks[key], self._raw_patterns[key])
def __call__(self, Doc doc):
def remove(self, key):
key = self._normalize_key(key)
if not key in self._patterns:
raise ValueError(Errors.E175.format(key=key))
self._patterns.pop(key)
self._raw_patterns.pop(key)
self._nodes.pop(key)
self._tree.pop(key)
self._root.pop(key)
def __call__(self, object doclike):
"""Find all token sequences matching the supplied pattern.
doclike (Doc or Span): The document to match over.
@ -214,8 +247,14 @@ cdef class DependencyMatcher:
describing the matches. A match tuple describes a span
`doc[start:end]`. The `label_id` and `key` are both integers.
"""
if isinstance(doclike, Doc):
doc = doclike
elif isinstance(doclike, Span):
doc = doclike.as_doc()
else:
raise ValueError(Errors.E195.format(good="Doc or Span", got=type(doclike).__name__))
matched_key_trees = []
matches = self.token_matcher(doc)
matches = self.matcher(doc)
for key in list(self._patterns.keys()):
_patterns_list = self._patterns[key]
_keys_to_token_list = self._keys_to_token[key]
@ -245,25 +284,25 @@ cdef class DependencyMatcher:
matched_trees = []
self.recurse(_tree, id_to_position, _node_operator_map, 0, [], matched_trees)
matched_key_trees.append((key,matched_trees))
for i, (ent_id, nodes) in enumerate(matched_key_trees):
on_match = self._callbacks.get(ent_id)
for matched_tree in matched_trees:
matched_key_trees.append((key, matched_tree))
for i, (match_id, nodes) in enumerate(matched_key_trees):
on_match = self._callbacks.get(match_id)
if on_match is not None:
on_match(self, doc, i, matched_key_trees)
return matched_key_trees
def recurse(self, tree, id_to_position, _node_operator_map, int patternLength, visited_nodes, matched_trees):
cdef bool isValid;
if(patternLength == len(id_to_position.keys())):
cdef bint isValid;
if patternLength == len(id_to_position.keys()):
isValid = True
for node in range(patternLength):
if(node in tree):
if node in tree:
for idx, (relop,nbor) in enumerate(tree[node]):
computed_nbors = numpy.asarray(_node_operator_map[visited_nodes[node]][relop])
isNbor = False
for computed_nbor in computed_nbors:
if(computed_nbor.i == visited_nodes[nbor]):
if computed_nbor.i == visited_nodes[nbor]:
isNbor = True
isValid = isValid & isNbor
if(isValid):
@ -295,24 +334,14 @@ cdef class DependencyMatcher:
_node_operator_map[node] = {}
for operator in all_operators:
_node_operator_map[node][operator] = []
# Used to invoke methods for each operator
switcher = {
"<": self.dep,
">": self.gov,
"<<": self.dep_chain,
">>": self.gov_chain,
".": self.imm_precede,
"$+": self.imm_right_sib,
"$-": self.imm_left_sib,
"$++": self.right_sib,
"$--": self.left_sib
}
for operator in all_operators:
for node in all_nodes:
_node_operator_map[node][operator] = switcher.get(operator)(doc,node)
_node_operator_map[node][operator] = self._ops.get(operator)(doc, node)
return _node_operator_map
def dep(self, doc, node):
if doc[node].head == doc[node]:
return []
return [doc[node].head]
def gov(self,doc,node):
@ -322,36 +351,51 @@ cdef class DependencyMatcher:
return list(doc[node].ancestors)
def gov_chain(self, doc, node):
return list(doc[node].subtree)
return [t for t in doc[node].subtree if t != doc[node]]
def imm_precede(self, doc, node):
if node > 0:
sent = self._get_sent(doc[node])
if node < len(doc) - 1 and doc[node + 1] in sent:
return [doc[node + 1]]
return []
def precede(self, doc, node):
sent = self._get_sent(doc[node])
return [doc[i] for i in range(node + 1, sent.end)]
def imm_follow(self, doc, node):
sent = self._get_sent(doc[node])
if node > 0 and doc[node - 1] in sent:
return [doc[node - 1]]
return []
def follow(self, doc, node):
sent = self._get_sent(doc[node])
return [doc[i] for i in range(sent.start, node)]
def imm_right_sib(self, doc, node):
for child in list(doc[node].head.children):
if child.i == node - 1:
if child.i == node + 1:
return [doc[child.i]]
return []
def imm_left_sib(self, doc, node):
for child in list(doc[node].head.children):
if child.i == node + 1:
if child.i == node - 1:
return [doc[child.i]]
return []
def right_sib(self, doc, node):
candidate_children = []
for child in list(doc[node].head.children):
if child.i < node:
if child.i > node:
candidate_children.append(doc[child.i])
return candidate_children
def left_sib(self, doc, node):
candidate_children = []
for child in list(doc[node].head.children):
if child.i > node:
if child.i < node:
candidate_children.append(doc[child.i])
return candidate_children
@ -360,3 +404,15 @@ cdef class DependencyMatcher:
return self.vocab.strings.add(key)
else:
return key
def _get_sent(self, token):
root = (list(token.ancestors) or [token])[-1]
return token.doc[root.left_edge.i:root.right_edge.i + 1]
def unpickle_matcher(vocab, patterns, callbacks):
matcher = DependencyMatcher(vocab)
for key, pattern in patterns.items():
callback = callbacks.get(key, None)
matcher.add(key, pattern, on_match=callback)
return matcher

13
spacy/matcher/matcher.pyx
View File

@ -31,8 +31,8 @@ DEF PADDING = 5
cdef class Matcher:
"""Match sequences of tokens, based on pattern rules.
DOCS: https://spacy.io/api/matcher
USAGE: https://spacy.io/usage/rule-based-matching
DOCS: https://nightly.spacy.io/api/matcher
USAGE: https://nightly.spacy.io/usage/rule-based-matching
"""
def __init__(self, vocab, validate=True):
@ -829,9 +829,11 @@ def _get_extra_predicates(spec, extra_predicates):
attr = "ORTH"
attr = IDS.get(attr.upper())
if isinstance(value, dict):
processed = False
value_with_upper_keys = {k.upper(): v for k, v in value.items()}
for type_, cls in predicate_types.items():
if type_ in value:
predicate = cls(len(extra_predicates), attr, value[type_], type_)
if type_ in value_with_upper_keys:
predicate = cls(len(extra_predicates), attr, value_with_upper_keys[type_], type_)
# Don't create a redundant predicates.
# This helps with efficiency, as we're caching the results.
if predicate.key in seen_predicates:
@ -840,6 +842,9 @@ def _get_extra_predicates(spec, extra_predicates):
extra_predicates.append(predicate)
output.append(predicate.i)
seen_predicates[predicate.key] = predicate.i
processed = True
if not processed:
warnings.warn(Warnings.W035.format(pattern=value))
return output

16
spacy/matcher/phrasematcher.pyx
View File

@ -19,8 +19,8 @@ cdef class PhraseMatcher:
sequences based on lists of token descriptions, the `PhraseMatcher` accepts
match patterns in the form of `Doc` objects.
DOCS: https://spacy.io/api/phrasematcher
USAGE: https://spacy.io/usage/rule-based-matching#phrasematcher
DOCS: https://nightly.spacy.io/api/phrasematcher
USAGE: https://nightly.spacy.io/usage/rule-based-matching#phrasematcher
Adapted from FlashText: https://github.com/vi3k6i5/flashtext
MIT License (see `LICENSE`)
@ -34,7 +34,7 @@ cdef class PhraseMatcher:
attr (int / str): Token attribute to match on.
validate (bool): Perform additional validation when patterns are added.
DOCS: https://spacy.io/api/phrasematcher#init
DOCS: https://nightly.spacy.io/api/phrasematcher#init
"""
self.vocab = vocab
self._callbacks = {}
@ -61,7 +61,7 @@ cdef class PhraseMatcher:
RETURNS (int): The number of rules.
DOCS: https://spacy.io/api/phrasematcher#len
DOCS: https://nightly.spacy.io/api/phrasematcher#len
"""
return len(self._callbacks)
@ -71,7 +71,7 @@ cdef class PhraseMatcher:
key (str): The match ID.
RETURNS (bool): Whether the matcher contains rules for this match ID.
DOCS: https://spacy.io/api/phrasematcher#contains
DOCS: https://nightly.spacy.io/api/phrasematcher#contains
"""
return key in self._callbacks
@ -85,7 +85,7 @@ cdef class PhraseMatcher:
key (str): The match ID.
DOCS: https://spacy.io/api/phrasematcher#remove
DOCS: https://nightly.spacy.io/api/phrasematcher#remove
"""
if key not in self._docs:
raise KeyError(key)
@ -164,7 +164,7 @@ cdef class PhraseMatcher:
as variable arguments. Will be ignored if a list of patterns is
provided as the second argument.
DOCS: https://spacy.io/api/phrasematcher#add
DOCS: https://nightly.spacy.io/api/phrasematcher#add
"""
if docs is None or hasattr(docs, "__call__"): # old API
on_match = docs
@ -228,7 +228,7 @@ cdef class PhraseMatcher:
`doc[start:end]`. The `match_id` is an integer. If as_spans is set
to True, a list of Span objects is returned.
DOCS: https://spacy.io/api/phrasematcher#call
DOCS: https://nightly.spacy.io/api/phrasematcher#call
"""
matches = []
if doc is None or len(doc) == 0:

6
spacy/ml/models/entity_linker.py
View File

@ -24,7 +24,7 @@ def build_nel_encoder(tok2vec: Model, nO: Optional[int] = None) -> Model:
return model
@registry.assets.register("spacy.KBFromFile.v1")
@registry.misc.register("spacy.KBFromFile.v1")
def load_kb(kb_path: str) -> Callable[[Vocab], KnowledgeBase]:
def kb_from_file(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
@ -34,7 +34,7 @@ def load_kb(kb_path: str) -> Callable[[Vocab], KnowledgeBase]:
return kb_from_file
@registry.assets.register("spacy.EmptyKB.v1")
@registry.misc.register("spacy.EmptyKB.v1")
def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
def empty_kb_factory(vocab):
return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
@ -42,6 +42,6 @@ def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
return empty_kb_factory
@registry.assets.register("spacy.CandidateGenerator.v1")
@registry.misc.register("spacy.CandidateGenerator.v1")
def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
return get_candidates

24
spacy/pipeline/attributeruler.py
View File

@ -38,7 +38,7 @@ class AttributeRuler(Pipe):
"""Set token-level attributes for tokens matched by Matcher patterns.
Additionally supports importing patterns from tag maps and morph rules.
DOCS: https://spacy.io/api/attributeruler
DOCS: https://nightly.spacy.io/api/attributeruler
"""
def __init__(
@ -59,7 +59,7 @@ class AttributeRuler(Pipe):
RETURNS (AttributeRuler): The AttributeRuler component.
DOCS: https://spacy.io/api/attributeruler#init
DOCS: https://nightly.spacy.io/api/attributeruler#init
"""
self.name = name
self.vocab = vocab
@ -77,7 +77,7 @@ class AttributeRuler(Pipe):
doc (Doc): The document to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/attributeruler#call
DOCS: https://nightly.spacy.io/api/attributeruler#call
"""
matches = sorted(self.matcher(doc))
@ -121,7 +121,7 @@ class AttributeRuler(Pipe):
tag_map (dict): The tag map that maps fine-grained tags to
coarse-grained tags and morphological features.
DOCS: https://spacy.io/api/attributeruler#load_from_morph_rules
DOCS: https://nightly.spacy.io/api/attributeruler#load_from_morph_rules
"""
for tag, attrs in tag_map.items():
pattern = [{"TAG": tag}]
@ -139,7 +139,7 @@ class AttributeRuler(Pipe):
fine-grained tags to coarse-grained tags, lemmas and morphological
features.
DOCS: https://spacy.io/api/attributeruler#load_from_morph_rules
DOCS: https://nightly.spacy.io/api/attributeruler#load_from_morph_rules
"""
for tag in morph_rules:
for word in morph_rules[tag]:
@ -163,7 +163,7 @@ class AttributeRuler(Pipe):
index (int): The index of the token in the matched span to modify. May
be negative to index from the end of the span. Defaults to 0.
DOCS: https://spacy.io/api/attributeruler#add
DOCS: https://nightly.spacy.io/api/attributeruler#add
"""
self.matcher.add(len(self.attrs), patterns)
self._attrs_unnormed.append(attrs)
@ -178,7 +178,7 @@ class AttributeRuler(Pipe):
as the arguments to AttributeRuler.add (patterns/attrs/index) to
add as patterns.
DOCS: https://spacy.io/api/attributeruler#add_patterns
DOCS: https://nightly.spacy.io/api/attributeruler#add_patterns
"""
for p in pattern_dicts:
self.add(**p)
@ -203,7 +203,7 @@ class AttributeRuler(Pipe):
Scorer.score_token_attr for the attributes "tag", "pos", "morph"
and "lemma" for the target token attributes.
DOCS: https://spacy.io/api/tagger#score
DOCS: https://nightly.spacy.io/api/tagger#score
"""
validate_examples(examples, "AttributeRuler.score")
results = {}
@ -227,7 +227,7 @@ class AttributeRuler(Pipe):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/attributeruler#to_bytes
DOCS: https://nightly.spacy.io/api/attributeruler#to_bytes
"""
serialize = {}
serialize["vocab"] = self.vocab.to_bytes
@ -243,7 +243,7 @@ class AttributeRuler(Pipe):
exclude (Iterable[str]): String names of serialization fields to exclude.
returns (AttributeRuler): The loaded object.
DOCS: https://spacy.io/api/attributeruler#from_bytes
DOCS: https://nightly.spacy.io/api/attributeruler#from_bytes
"""
def load_patterns(b):
@ -264,7 +264,7 @@ class AttributeRuler(Pipe):
path (Union[Path, str]): A path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/attributeruler#to_disk
DOCS: https://nightly.spacy.io/api/attributeruler#to_disk
"""
serialize = {
"vocab": lambda p: self.vocab.to_disk(p),
@ -279,7 +279,7 @@ class AttributeRuler(Pipe):
path (Union[Path, str]): A path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/attributeruler#from_disk
DOCS: https://nightly.spacy.io/api/attributeruler#from_disk
"""
def load_patterns(p):

6
spacy/pipeline/dep_parser.pyx
View File

@ -105,7 +105,7 @@ def make_parser(
cdef class DependencyParser(Parser):
"""Pipeline component for dependency parsing.
DOCS: https://spacy.io/api/dependencyparser
DOCS: https://nightly.spacy.io/api/dependencyparser
"""
TransitionSystem = ArcEager
@ -146,7 +146,7 @@ cdef class DependencyParser(Parser):
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans
and Scorer.score_deps.
DOCS: https://spacy.io/api/dependencyparser#score
DOCS: https://nightly.spacy.io/api/dependencyparser#score
"""
validate_examples(examples, "DependencyParser.score")
def dep_getter(token, attr):
@ -156,7 +156,7 @@ cdef class DependencyParser(Parser):
results = {}
results.update(Scorer.score_spans(examples, "sents", **kwargs))
kwargs.setdefault("getter", dep_getter)
kwargs.setdefault("ignore_label", ("p", "punct"))
kwargs.setdefault("ignore_labels", ("p", "punct"))
results.update(Scorer.score_deps(examples, "dep", **kwargs))
del results["sents_per_type"]
return results

24
spacy/pipeline/entity_linker.py
View File

@ -39,12 +39,12 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
assigns=["token.ent_kb_id"],
default_config={
"kb_loader": {"@assets": "spacy.EmptyKB.v1", "entity_vector_length": 64},
"kb_loader": {"@misc": "spacy.EmptyKB.v1", "entity_vector_length": 64},
"model": DEFAULT_NEL_MODEL,
"labels_discard": [],
"incl_prior": True,
"incl_context": True,
"get_candidates": {"@assets": "spacy.CandidateGenerator.v1"},
"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
},
)
def make_entity_linker(
@ -83,7 +83,7 @@ def make_entity_linker(
class EntityLinker(Pipe):
"""Pipeline component for named entity linking.
DOCS: https://spacy.io/api/entitylinker
DOCS: https://nightly.spacy.io/api/entitylinker
"""
NIL = "NIL" # string used to refer to a non-existing link
@ -111,7 +111,7 @@ class EntityLinker(Pipe):
incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
incl_context (bool): Whether or not to include the local context in the model.
DOCS: https://spacy.io/api/entitylinker#init
DOCS: https://nightly.spacy.io/api/entitylinker#init
"""
self.vocab = vocab
self.model = model
@ -151,7 +151,7 @@ class EntityLinker(Pipe):
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/entitylinker#begin_training
DOCS: https://nightly.spacy.io/api/entitylinker#begin_training
"""
self.require_kb()
nO = self.kb.entity_vector_length
@ -182,7 +182,7 @@ class EntityLinker(Pipe):
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/entitylinker#update
DOCS: https://nightly.spacy.io/api/entitylinker#update
"""
self.require_kb()
if losses is None:
@ -264,7 +264,7 @@ class EntityLinker(Pipe):
doc (Doc): The document to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/entitylinker#call
DOCS: https://nightly.spacy.io/api/entitylinker#call
"""
kb_ids = self.predict([doc])
self.set_annotations([doc], kb_ids)
@ -279,7 +279,7 @@ class EntityLinker(Pipe):
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/entitylinker#pipe
DOCS: https://nightly.spacy.io/api/entitylinker#pipe
"""
for docs in util.minibatch(stream, size=batch_size):
kb_ids = self.predict(docs)
@ -294,7 +294,7 @@ class EntityLinker(Pipe):
docs (Iterable[Doc]): The documents to predict.
RETURNS (List[int]): The models prediction for each document.
DOCS: https://spacy.io/api/entitylinker#predict
DOCS: https://nightly.spacy.io/api/entitylinker#predict
"""
self.require_kb()
entity_count = 0
@ -391,7 +391,7 @@ class EntityLinker(Pipe):
docs (Iterable[Doc]): The documents to modify.
kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict.
DOCS: https://spacy.io/api/entitylinker#set_annotations
DOCS: https://nightly.spacy.io/api/entitylinker#set_annotations
"""
count_ents = len([ent for doc in docs for ent in doc.ents])
if count_ents != len(kb_ids):
@ -412,7 +412,7 @@ class EntityLinker(Pipe):
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/entitylinker#to_disk
DOCS: https://nightly.spacy.io/api/entitylinker#to_disk
"""
serialize = {}
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
@ -430,7 +430,7 @@ class EntityLinker(Pipe):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (EntityLinker): The modified EntityLinker object.
DOCS: https://spacy.io/api/entitylinker#from_disk
DOCS: https://nightly.spacy.io/api/entitylinker#from_disk
"""
def load_model(p):

26
spacy/pipeline/entityruler.py
View File

@ -53,8 +53,8 @@ class EntityRuler:
purely rule-based entity recognition system. After initialization, the
component is typically added to the pipeline using `nlp.add_pipe`.
DOCS: https://spacy.io/api/entityruler
USAGE: https://spacy.io/usage/rule-based-matching#entityruler
DOCS: https://nightly.spacy.io/api/entityruler
USAGE: https://nightly.spacy.io/usage/rule-based-matching#entityruler
"""
def __init__(
@ -88,7 +88,7 @@ class EntityRuler:
added by the model, overwrite them by matches if necessary.
ent_id_sep (str): Separator used internally for entity IDs.
DOCS: https://spacy.io/api/entityruler#init
DOCS: https://nightly.spacy.io/api/entityruler#init
"""
self.nlp = nlp
self.name = name
@ -127,13 +127,13 @@ class EntityRuler:
doc (Doc): The Doc object in the pipeline.
RETURNS (Doc): The Doc with added entities, if available.
DOCS: https://spacy.io/api/entityruler#call
DOCS: https://nightly.spacy.io/api/entityruler#call
"""
matches = list(self.matcher(doc)) + list(self.phrase_matcher(doc))
matches = set(
[(m_id, start, end) for m_id, start, end in matches if start != end]
)
get_sort_key = lambda m: (m[2] - m[1], m[1])
get_sort_key = lambda m: (m[2] - m[1], -m[1])
matches = sorted(matches, key=get_sort_key, reverse=True)
entities = list(doc.ents)
new_entities = []
@ -165,7 +165,7 @@ class EntityRuler:
RETURNS (set): The string labels.
DOCS: https://spacy.io/api/entityruler#labels
DOCS: https://nightly.spacy.io/api/entityruler#labels
"""
keys = set(self.token_patterns.keys())
keys.update(self.phrase_patterns.keys())
@ -185,7 +185,7 @@ class EntityRuler:
RETURNS (set): The string entity ids.
DOCS: https://spacy.io/api/entityruler#ent_ids
DOCS: https://nightly.spacy.io/api/entityruler#ent_ids
"""
keys = set(self.token_patterns.keys())
keys.update(self.phrase_patterns.keys())
@ -203,7 +203,7 @@ class EntityRuler:
RETURNS (list): The original patterns, one dictionary per pattern.
DOCS: https://spacy.io/api/entityruler#patterns
DOCS: https://nightly.spacy.io/api/entityruler#patterns
"""
all_patterns = []
for label, patterns in self.token_patterns.items():
@ -230,7 +230,7 @@ class EntityRuler:
patterns (list): The patterns to add.
DOCS: https://spacy.io/api/entityruler#add_patterns
DOCS: https://nightly.spacy.io/api/entityruler#add_patterns
"""
# disable the nlp components after this one in case they hadn't been initialized / deserialised yet
@ -324,7 +324,7 @@ class EntityRuler:
patterns_bytes (bytes): The bytestring to load.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_bytes
DOCS: https://nightly.spacy.io/api/entityruler#from_bytes
"""
cfg = srsly.msgpack_loads(patterns_bytes)
self.clear()
@ -346,7 +346,7 @@ class EntityRuler:
RETURNS (bytes): The serialized patterns.
DOCS: https://spacy.io/api/entityruler#to_bytes
DOCS: https://nightly.spacy.io/api/entityruler#to_bytes
"""
serial = {
"overwrite": self.overwrite,
@ -365,7 +365,7 @@ class EntityRuler:
path (str / Path): The JSONL file to load.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#from_disk
DOCS: https://nightly.spacy.io/api/entityruler#from_disk
"""
path = ensure_path(path)
self.clear()
@ -401,7 +401,7 @@ class EntityRuler:
path (str / Path): The JSONL file to save.
DOCS: https://spacy.io/api/entityruler#to_disk
DOCS: https://nightly.spacy.io/api/entityruler#to_disk
"""
path = ensure_path(path)
cfg = {

6
spacy/pipeline/functions.py
View File

@ -15,7 +15,7 @@ def merge_noun_chunks(doc: Doc) -> Doc:
doc (Doc): The Doc object.
RETURNS (Doc): The Doc object with merged noun chunks.
DOCS: https://spacy.io/api/pipeline-functions#merge_noun_chunks
DOCS: https://nightly.spacy.io/api/pipeline-functions#merge_noun_chunks
"""
if not doc.is_parsed:
return doc
@ -37,7 +37,7 @@ def merge_entities(doc: Doc):
doc (Doc): The Doc object.
RETURNS (Doc): The Doc object with merged entities.
DOCS: https://spacy.io/api/pipeline-functions#merge_entities
DOCS: https://nightly.spacy.io/api/pipeline-functions#merge_entities
"""
with doc.retokenize() as retokenizer:
for ent in doc.ents:
@ -54,7 +54,7 @@ def merge_subtokens(doc: Doc, label: str = "subtok") -> Doc:
label (str): The subtoken dependency label.
RETURNS (Doc): The Doc object with merged subtokens.
DOCS: https://spacy.io/api/pipeline-functions#merge_subtokens
DOCS: https://nightly.spacy.io/api/pipeline-functions#merge_subtokens
"""
# TODO: make stateful component with "label" config
merger = Matcher(doc.vocab)

28
spacy/pipeline/lemmatizer.py
View File

@ -43,7 +43,7 @@ class Lemmatizer(Pipe):
The Lemmatizer supports simple part-of-speech-sensitive suffix rules and
lookup tables.
DOCS: https://spacy.io/api/lemmatizer
DOCS: https://nightly.spacy.io/api/lemmatizer
"""
@classmethod
@ -54,7 +54,7 @@ class Lemmatizer(Pipe):
mode (str): The lemmatizer mode.
RETURNS (dict): The lookups configuration settings for this mode.
DOCS: https://spacy.io/api/lemmatizer#get_lookups_config
DOCS: https://nightly.spacy.io/api/lemmatizer#get_lookups_config
"""
if mode == "lookup":
return {
@ -80,7 +80,7 @@ class Lemmatizer(Pipe):
lookups should be loaded.
RETURNS (Lookups): The Lookups object.
DOCS: https://spacy.io/api/lemmatizer#get_lookups_config
DOCS: https://nightly.spacy.io/api/lemmatizer#get_lookups_config
"""
config = cls.get_lookups_config(mode)
required_tables = config.get("required_tables", [])
@ -123,7 +123,7 @@ class Lemmatizer(Pipe):
overwrite (bool): Whether to overwrite existing lemmas. Defaults to
`False`.
DOCS: https://spacy.io/api/lemmatizer#init
DOCS: https://nightly.spacy.io/api/lemmatizer#init
"""
self.vocab = vocab
self.model = model
@ -152,7 +152,7 @@ class Lemmatizer(Pipe):
doc (Doc): The Doc to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/lemmatizer#call
DOCS: https://nightly.spacy.io/api/lemmatizer#call
"""
for token in doc:
if self.overwrite or token.lemma == 0:
@ -168,7 +168,7 @@ class Lemmatizer(Pipe):
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/lemmatizer#pipe
DOCS: https://nightly.spacy.io/api/lemmatizer#pipe
"""
for doc in stream:
doc = self(doc)
@ -180,7 +180,7 @@ class Lemmatizer(Pipe):
token (Token): The token to lemmatize.
RETURNS (list): The available lemmas for the string.
DOCS: https://spacy.io/api/lemmatizer#lookup_lemmatize
DOCS: https://nightly.spacy.io/api/lemmatizer#lookup_lemmatize
"""
lookup_table = self.lookups.get_table("lemma_lookup", {})
result = lookup_table.get(token.text, token.text)
@ -194,7 +194,7 @@ class Lemmatizer(Pipe):
token (Token): The token to lemmatize.
RETURNS (list): The available lemmas for the string.
DOCS: https://spacy.io/api/lemmatizer#rule_lemmatize
DOCS: https://nightly.spacy.io/api/lemmatizer#rule_lemmatize
"""
cache_key = (token.orth, token.pos, token.morph)
if cache_key in self.cache:
@ -260,7 +260,7 @@ class Lemmatizer(Pipe):
token (Token): The token.
RETURNS (bool): Whether the token is a base form.
DOCS: https://spacy.io/api/lemmatizer#is_base_form
DOCS: https://nightly.spacy.io/api/lemmatizer#is_base_form
"""
return False
@ -270,7 +270,7 @@ class Lemmatizer(Pipe):
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores.
DOCS: https://spacy.io/api/lemmatizer#score
DOCS: https://nightly.spacy.io/api/lemmatizer#score
"""
validate_examples(examples, "Lemmatizer.score")
return Scorer.score_token_attr(examples, "lemma", **kwargs)
@ -282,7 +282,7 @@ class Lemmatizer(Pipe):
it doesn't exist.
exclude (list): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/vocab#to_disk
DOCS: https://nightly.spacy.io/api/vocab#to_disk
"""
serialize = {}
serialize["vocab"] = lambda p: self.vocab.to_disk(p)
@ -297,7 +297,7 @@ class Lemmatizer(Pipe):
exclude (list): String names of serialization fields to exclude.
RETURNS (Vocab): The modified `Vocab` object.
DOCS: https://spacy.io/api/vocab#to_disk
DOCS: https://nightly.spacy.io/api/vocab#to_disk
"""
deserialize = {}
deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
@ -310,7 +310,7 @@ class Lemmatizer(Pipe):
exclude (list): String names of serialization fields to exclude.
RETURNS (bytes): The serialized form of the `Vocab` object.
DOCS: https://spacy.io/api/vocab#to_bytes
DOCS: https://nightly.spacy.io/api/vocab#to_bytes
"""
serialize = {}
serialize["vocab"] = self.vocab.to_bytes
@ -324,7 +324,7 @@ class Lemmatizer(Pipe):
exclude (list): String names of serialization fields to exclude.
RETURNS (Vocab): The `Vocab` object.
DOCS: https://spacy.io/api/vocab#from_bytes
DOCS: https://nightly.spacy.io/api/vocab#from_bytes
"""
deserialize = {}
deserialize["vocab"] = lambda b: self.vocab.from_bytes(b)

20
spacy/pipeline/morphologizer.pyx
View File

@ -79,7 +79,7 @@ class Morphologizer(Tagger):
labels_morph (dict): Mapping of morph + POS tags to morph labels.
labels_pos (dict): Mapping of morph + POS tags to POS tags.
DOCS: https://spacy.io/api/morphologizer#init
DOCS: https://nightly.spacy.io/api/morphologizer#init
"""
self.vocab = vocab
self.model = model
@ -106,7 +106,7 @@ class Morphologizer(Tagger):
label (str): The label to add.
RETURNS (int): 0 if label is already present, otherwise 1.
DOCS: https://spacy.io/api/morphologizer#add_label
DOCS: https://nightly.spacy.io/api/morphologizer#add_label
"""
if not isinstance(label, str):
raise ValueError(Errors.E187)
@ -139,7 +139,7 @@ class Morphologizer(Tagger):
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/morphologizer#begin_training
DOCS: https://nightly.spacy.io/api/morphologizer#begin_training
"""
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="Morphologizer", obj=type(get_examples))
@ -169,7 +169,7 @@ class Morphologizer(Tagger):
docs (Iterable[Doc]): The documents to modify.
batch_tag_ids: The IDs to set, produced by Morphologizer.predict.
DOCS: https://spacy.io/api/morphologizer#set_annotations
DOCS: https://nightly.spacy.io/api/morphologizer#set_annotations
"""
if isinstance(docs, Doc):
docs = [docs]
@ -194,7 +194,7 @@ class Morphologizer(Tagger):
scores: Scores representing the model's predictions.
RETUTNRS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/morphologizer#get_loss
DOCS: https://nightly.spacy.io/api/morphologizer#get_loss
"""
validate_examples(examples, "Morphologizer.get_loss")
loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
@ -231,7 +231,7 @@ class Morphologizer(Tagger):
Scorer.score_token_attr for the attributes "pos" and "morph" and
Scorer.score_token_attr_per_feat for the attribute "morph".
DOCS: https://spacy.io/api/morphologizer#score
DOCS: https://nightly.spacy.io/api/morphologizer#score
"""
validate_examples(examples, "Morphologizer.score")
results = {}
@ -247,7 +247,7 @@ class Morphologizer(Tagger):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/morphologizer#to_bytes
DOCS: https://nightly.spacy.io/api/morphologizer#to_bytes
"""
serialize = {}
serialize["model"] = self.model.to_bytes
@ -262,7 +262,7 @@ class Morphologizer(Tagger):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Morphologizer): The loaded Morphologizer.
DOCS: https://spacy.io/api/morphologizer#from_bytes
DOCS: https://nightly.spacy.io/api/morphologizer#from_bytes
"""
def load_model(b):
try:
@ -284,7 +284,7 @@ class Morphologizer(Tagger):
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/morphologizer#to_disk
DOCS: https://nightly.spacy.io/api/morphologizer#to_disk
"""
serialize = {
"vocab": lambda p: self.vocab.to_disk(p),
@ -300,7 +300,7 @@ class Morphologizer(Tagger):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Morphologizer): The modified Morphologizer object.
DOCS: https://spacy.io/api/morphologizer#from_disk
DOCS: https://nightly.spacy.io/api/morphologizer#from_disk
"""
def load_model(p):
with p.open("rb") as file_:

4
spacy/pipeline/ner.pyx
View File

@ -88,7 +88,7 @@ def make_ner(
cdef class EntityRecognizer(Parser):
"""Pipeline component for named entity recognition.
DOCS: https://spacy.io/api/entityrecognizer
DOCS: https://nightly.spacy.io/api/entityrecognizer
"""
TransitionSystem = BiluoPushDown
@ -119,7 +119,7 @@ cdef class EntityRecognizer(Parser):
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans.
DOCS: https://spacy.io/api/entityrecognizer#score
DOCS: https://nightly.spacy.io/api/entityrecognizer#score
"""
validate_examples(examples, "EntityRecognizer.score")
return Scorer.score_spans(examples, "ents", **kwargs)

36
spacy/pipeline/pipe.pyx
View File

@ -15,7 +15,7 @@ cdef class Pipe:
from it and it defines the interface that components should follow to
function as trainable components in a spaCy pipeline.
DOCS: https://spacy.io/api/pipe
DOCS: https://nightly.spacy.io/api/pipe
"""
def __init__(self, vocab, model, name, **cfg):
"""Initialize a pipeline component.
@ -25,7 +25,7 @@ cdef class Pipe:
name (str): The component instance name.
**cfg: Additonal settings and config parameters.
DOCS: https://spacy.io/api/pipe#init
DOCS: https://nightly.spacy.io/api/pipe#init
"""
self.vocab = vocab
self.model = model
@ -40,7 +40,7 @@ cdef class Pipe:
docs (Doc): The Doc to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/pipe#call
DOCS: https://nightly.spacy.io/api/pipe#call
"""
scores = self.predict([doc])
self.set_annotations([doc], scores)
@ -55,7 +55,7 @@ cdef class Pipe:
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/pipe#pipe
DOCS: https://nightly.spacy.io/api/pipe#pipe
"""
for docs in util.minibatch(stream, size=batch_size):
scores = self.predict(docs)
@ -69,7 +69,7 @@ cdef class Pipe:
docs (Iterable[Doc]): The documents to predict.
RETURNS: Vector representations for each token in the documents.
DOCS: https://spacy.io/api/pipe#predict
DOCS: https://nightly.spacy.io/api/pipe#predict
"""
raise NotImplementedError(Errors.E931.format(method="predict", name=self.name))
@ -79,7 +79,7 @@ cdef class Pipe:
docs (Iterable[Doc]): The documents to modify.
scores: The scores to assign.
DOCS: https://spacy.io/api/pipe#set_annotations
DOCS: https://nightly.spacy.io/api/pipe#set_annotations
"""
raise NotImplementedError(Errors.E931.format(method="set_annotations", name=self.name))
@ -96,7 +96,7 @@ cdef class Pipe:
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/pipe#update
DOCS: https://nightly.spacy.io/api/pipe#update
"""
if losses is None:
losses = {}
@ -132,7 +132,7 @@ cdef class Pipe:
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/pipe#rehearse
DOCS: https://nightly.spacy.io/api/pipe#rehearse
"""
pass
@ -144,7 +144,7 @@ cdef class Pipe:
scores: Scores representing the model's predictions.
RETUTNRS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/pipe#get_loss
DOCS: https://nightly.spacy.io/api/pipe#get_loss
"""
raise NotImplementedError(Errors.E931.format(method="get_loss", name=self.name))
@ -156,7 +156,7 @@ cdef class Pipe:
label (str): The label to add.
RETURNS (int): 0 if label is already present, otherwise 1.
DOCS: https://spacy.io/api/pipe#add_label
DOCS: https://nightly.spacy.io/api/pipe#add_label
"""
raise NotImplementedError(Errors.E931.format(method="add_label", name=self.name))
@ -165,7 +165,7 @@ cdef class Pipe:
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/pipe#create_optimizer
DOCS: https://nightly.spacy.io/api/pipe#create_optimizer
"""
return util.create_default_optimizer()
@ -181,7 +181,7 @@ cdef class Pipe:
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/pipe#begin_training
DOCS: https://nightly.spacy.io/api/pipe#begin_training
"""
self.model.initialize()
if sgd is None:
@ -200,7 +200,7 @@ cdef class Pipe:
params (dict): The parameter values to use in the model.
DOCS: https://spacy.io/api/pipe#use_params
DOCS: https://nightly.spacy.io/api/pipe#use_params
"""
with self.model.use_params(params):
yield
@ -211,7 +211,7 @@ cdef class Pipe:
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores.
DOCS: https://spacy.io/api/pipe#score
DOCS: https://nightly.spacy.io/api/pipe#score
"""
return {}
@ -221,7 +221,7 @@ cdef class Pipe:
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/pipe#to_bytes
DOCS: https://nightly.spacy.io/api/pipe#to_bytes
"""
serialize = {}
serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
@ -236,7 +236,7 @@ cdef class Pipe:
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Pipe): The loaded object.
DOCS: https://spacy.io/api/pipe#from_bytes
DOCS: https://nightly.spacy.io/api/pipe#from_bytes
"""
def load_model(b):
@ -259,7 +259,7 @@ cdef class Pipe:
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/pipe#to_disk
DOCS: https://nightly.spacy.io/api/pipe#to_disk
"""
serialize = {}
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
@ -274,7 +274,7 @@ cdef class Pipe:
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Pipe): The loaded object.
DOCS: https://spacy.io/api/pipe#from_disk
DOCS: https://nightly.spacy.io/api/pipe#from_disk
"""
def load_model(p):

18
spacy/pipeline/sentencizer.pyx
View File

@ -29,7 +29,7 @@ def make_sentencizer(
class Sentencizer(Pipe):
"""Segment the Doc into sentences using a rule-based strategy.
DOCS: https://spacy.io/api/sentencizer
DOCS: https://nightly.spacy.io/api/sentencizer
"""
default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
@ -51,7 +51,7 @@ class Sentencizer(Pipe):
serialized with the nlp object.
RETURNS (Sentencizer): The sentencizer component.
DOCS: https://spacy.io/api/sentencizer#init
DOCS: https://nightly.spacy.io/api/sentencizer#init
"""
self.name = name
if punct_chars:
@ -68,7 +68,7 @@ class Sentencizer(Pipe):
doc (Doc): The document to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/sentencizer#call
DOCS: https://nightly.spacy.io/api/sentencizer#call
"""
start = 0
seen_period = False
@ -94,7 +94,7 @@ class Sentencizer(Pipe):
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/sentencizer#pipe
DOCS: https://nightly.spacy.io/api/sentencizer#pipe
"""
for docs in util.minibatch(stream, size=batch_size):
predictions = self.predict(docs)
@ -157,7 +157,7 @@ class Sentencizer(Pipe):
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans.
DOCS: https://spacy.io/api/sentencizer#score
DOCS: https://nightly.spacy.io/api/sentencizer#score
"""
validate_examples(examples, "Sentencizer.score")
results = Scorer.score_spans(examples, "sents", **kwargs)
@ -169,7 +169,7 @@ class Sentencizer(Pipe):
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/sentencizer#to_bytes
DOCS: https://nightly.spacy.io/api/sentencizer#to_bytes
"""
return srsly.msgpack_dumps({"punct_chars": list(self.punct_chars)})
@ -179,7 +179,7 @@ class Sentencizer(Pipe):
bytes_data (bytes): The data to load.
returns (Sentencizer): The loaded object.
DOCS: https://spacy.io/api/sentencizer#from_bytes
DOCS: https://nightly.spacy.io/api/sentencizer#from_bytes
"""
cfg = srsly.msgpack_loads(bytes_data)
self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars))
@ -188,7 +188,7 @@ class Sentencizer(Pipe):
def to_disk(self, path, *, exclude=tuple()):
"""Serialize the sentencizer to disk.
DOCS: https://spacy.io/api/sentencizer#to_disk
DOCS: https://nightly.spacy.io/api/sentencizer#to_disk
"""
path = util.ensure_path(path)
path = path.with_suffix(".json")
@ -198,7 +198,7 @@ class Sentencizer(Pipe):
def from_disk(self, path, *, exclude=tuple()):
"""Load the sentencizer from disk.
DOCS: https://spacy.io/api/sentencizer#from_disk
DOCS: https://nightly.spacy.io/api/sentencizer#from_disk
"""
path = util.ensure_path(path)
path = path.with_suffix(".json")

20
spacy/pipeline/senter.pyx
View File

@ -44,7 +44,7 @@ def make_senter(nlp: Language, name: str, model: Model):
class SentenceRecognizer(Tagger):
"""Pipeline component for sentence segmentation.
DOCS: https://spacy.io/api/sentencerecognizer
DOCS: https://nightly.spacy.io/api/sentencerecognizer
"""
def __init__(self, vocab, model, name="senter"):
"""Initialize a sentence recognizer.
@ -54,7 +54,7 @@ class SentenceRecognizer(Tagger):
name (str): The component instance name, used to add entries to the
losses during training.
DOCS: https://spacy.io/api/sentencerecognizer#init
DOCS: https://nightly.spacy.io/api/sentencerecognizer#init
"""
self.vocab = vocab
self.model = model
@ -76,7 +76,7 @@ class SentenceRecognizer(Tagger):
docs (Iterable[Doc]): The documents to modify.
batch_tag_ids: The IDs to set, produced by SentenceRecognizer.predict.
DOCS: https://spacy.io/api/sentencerecognizer#set_annotations
DOCS: https://nightly.spacy.io/api/sentencerecognizer#set_annotations
"""
if isinstance(docs, Doc):
docs = [docs]
@ -101,7 +101,7 @@ class SentenceRecognizer(Tagger):
scores: Scores representing the model's predictions.
RETUTNRS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/sentencerecognizer#get_loss
DOCS: https://nightly.spacy.io/api/sentencerecognizer#get_loss
"""
validate_examples(examples, "SentenceRecognizer.get_loss")
labels = self.labels
@ -135,7 +135,7 @@ class SentenceRecognizer(Tagger):
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/sentencerecognizer#begin_training
DOCS: https://nightly.spacy.io/api/sentencerecognizer#begin_training
"""
self.set_output(len(self.labels))
self.model.initialize()
@ -151,7 +151,7 @@ class SentenceRecognizer(Tagger):
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans.
DOCS: https://spacy.io/api/sentencerecognizer#score
DOCS: https://nightly.spacy.io/api/sentencerecognizer#score
"""
validate_examples(examples, "SentenceRecognizer.score")
results = Scorer.score_spans(examples, "sents", **kwargs)
@ -164,7 +164,7 @@ class SentenceRecognizer(Tagger):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/sentencerecognizer#to_bytes
DOCS: https://nightly.spacy.io/api/sentencerecognizer#to_bytes
"""
serialize = {}
serialize["model"] = self.model.to_bytes
@ -179,7 +179,7 @@ class SentenceRecognizer(Tagger):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Tagger): The loaded SentenceRecognizer.
DOCS: https://spacy.io/api/sentencerecognizer#from_bytes
DOCS: https://nightly.spacy.io/api/sentencerecognizer#from_bytes
"""
def load_model(b):
try:
@ -201,7 +201,7 @@ class SentenceRecognizer(Tagger):
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/sentencerecognizer#to_disk
DOCS: https://nightly.spacy.io/api/sentencerecognizer#to_disk
"""
serialize = {
"vocab": lambda p: self.vocab.to_disk(p),
@ -217,7 +217,7 @@ class SentenceRecognizer(Tagger):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Tagger): The modified SentenceRecognizer object.
DOCS: https://spacy.io/api/sentencerecognizer#from_disk
DOCS: https://nightly.spacy.io/api/sentencerecognizer#from_disk
"""
def load_model(p):
with p.open("rb") as file_:

2
spacy/pipeline/simple_ner.py
View File

@ -78,7 +78,7 @@ class SimpleNER(Pipe):
def add_label(self, label: str) -> None:
"""Add a new label to the pipe.
label (str): The label to add.
DOCS: https://spacy.io/api/simplener#add_label
DOCS: https://nightly.spacy.io/api/simplener#add_label
"""
if not isinstance(label, str):
raise ValueError(Errors.E187)

34
spacy/pipeline/tagger.pyx
View File

@ -58,7 +58,7 @@ def make_tagger(nlp: Language, name: str, model: Model):
class Tagger(Pipe):
"""Pipeline component for part-of-speech tagging.
DOCS: https://spacy.io/api/tagger
DOCS: https://nightly.spacy.io/api/tagger
"""
def __init__(self, vocab, model, name="tagger", *, labels=None):
"""Initialize a part-of-speech tagger.
@ -69,7 +69,7 @@ class Tagger(Pipe):
losses during training.
labels (List): The set of labels. Defaults to None.
DOCS: https://spacy.io/api/tagger#init
DOCS: https://nightly.spacy.io/api/tagger#init
"""
self.vocab = vocab
self.model = model
@ -86,7 +86,7 @@ class Tagger(Pipe):
RETURNS (Tuple[str]): The labels.
DOCS: https://spacy.io/api/tagger#labels
DOCS: https://nightly.spacy.io/api/tagger#labels
"""
return tuple(self.cfg["labels"])
@ -96,7 +96,7 @@ class Tagger(Pipe):
doc (Doc): The document to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/tagger#call
DOCS: https://nightly.spacy.io/api/tagger#call
"""
tags = self.predict([doc])
self.set_annotations([doc], tags)
@ -111,7 +111,7 @@ class Tagger(Pipe):
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/tagger#pipe
DOCS: https://nightly.spacy.io/api/tagger#pipe
"""
for docs in util.minibatch(stream, size=batch_size):
tag_ids = self.predict(docs)
@ -124,7 +124,7 @@ class Tagger(Pipe):
docs (Iterable[Doc]): The documents to predict.
RETURNS: The models prediction for each document.
DOCS: https://spacy.io/api/tagger#predict
DOCS: https://nightly.spacy.io/api/tagger#predict
"""
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
@ -153,7 +153,7 @@ class Tagger(Pipe):
docs (Iterable[Doc]): The documents to modify.
batch_tag_ids: The IDs to set, produced by Tagger.predict.
DOCS: https://spacy.io/api/tagger#set_annotations
DOCS: https://nightly.spacy.io/api/tagger#set_annotations
"""
if isinstance(docs, Doc):
docs = [docs]
@ -182,7 +182,7 @@ class Tagger(Pipe):
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/tagger#update
DOCS: https://nightly.spacy.io/api/tagger#update
"""
if losses is None:
losses = {}
@ -220,7 +220,7 @@ class Tagger(Pipe):
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/tagger#rehearse
DOCS: https://nightly.spacy.io/api/tagger#rehearse
"""
validate_examples(examples, "Tagger.rehearse")
docs = [eg.predicted for eg in examples]
@ -247,7 +247,7 @@ class Tagger(Pipe):
scores: Scores representing the model's predictions.
RETUTNRS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/tagger#get_loss
DOCS: https://nightly.spacy.io/api/tagger#get_loss
"""
validate_examples(examples, "Tagger.get_loss")
loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
@ -269,7 +269,7 @@ class Tagger(Pipe):
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/tagger#begin_training
DOCS: https://nightly.spacy.io/api/tagger#begin_training
"""
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="Tagger", obj=type(get_examples))
@ -307,7 +307,7 @@ class Tagger(Pipe):
label (str): The label to add.
RETURNS (int): 0 if label is already present, otherwise 1.
DOCS: https://spacy.io/api/tagger#add_label
DOCS: https://nightly.spacy.io/api/tagger#add_label
"""
if not isinstance(label, str):
raise ValueError(Errors.E187)
@ -324,7 +324,7 @@ class Tagger(Pipe):
RETURNS (Dict[str, Any]): The scores, produced by
Scorer.score_token_attr for the attributes "tag".
DOCS: https://spacy.io/api/tagger#score
DOCS: https://nightly.spacy.io/api/tagger#score
"""
validate_examples(examples, "Tagger.score")
return Scorer.score_token_attr(examples, "tag", **kwargs)
@ -335,7 +335,7 @@ class Tagger(Pipe):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/tagger#to_bytes
DOCS: https://nightly.spacy.io/api/tagger#to_bytes
"""
serialize = {}
serialize["model"] = self.model.to_bytes
@ -350,7 +350,7 @@ class Tagger(Pipe):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Tagger): The loaded Tagger.
DOCS: https://spacy.io/api/tagger#from_bytes
DOCS: https://nightly.spacy.io/api/tagger#from_bytes
"""
def load_model(b):
try:
@ -372,7 +372,7 @@ class Tagger(Pipe):
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/tagger#to_disk
DOCS: https://nightly.spacy.io/api/tagger#to_disk
"""
serialize = {
"vocab": lambda p: self.vocab.to_disk(p),
@ -388,7 +388,7 @@ class Tagger(Pipe):
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Tagger): The modified Tagger object.
DOCS: https://spacy.io/api/tagger#from_disk
DOCS: https://nightly.spacy.io/api/tagger#from_disk
"""
def load_model(p):
with p.open("rb") as file_:

24
spacy/pipeline/textcat.py
View File

@ -92,7 +92,7 @@ def make_textcat(
class TextCategorizer(Pipe):
"""Pipeline component for text classification.
DOCS: https://spacy.io/api/textcategorizer
DOCS: https://nightly.spacy.io/api/textcategorizer
"""
def __init__(
@ -111,7 +111,7 @@ class TextCategorizer(Pipe):
losses during training.
labels (Iterable[str]): The labels to use.
DOCS: https://spacy.io/api/textcategorizer#init
DOCS: https://nightly.spacy.io/api/textcategorizer#init
"""
self.vocab = vocab
self.model = model
@ -124,7 +124,7 @@ class TextCategorizer(Pipe):
def labels(self) -> Tuple[str]:
"""RETURNS (Tuple[str]): The labels currently added to the component.
DOCS: https://spacy.io/api/textcategorizer#labels
DOCS: https://nightly.spacy.io/api/textcategorizer#labels
"""
return tuple(self.cfg.setdefault("labels", []))
@ -146,7 +146,7 @@ class TextCategorizer(Pipe):
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/textcategorizer#pipe
DOCS: https://nightly.spacy.io/api/textcategorizer#pipe
"""
for docs in util.minibatch(stream, size=batch_size):
scores = self.predict(docs)
@ -159,7 +159,7 @@ class TextCategorizer(Pipe):
docs (Iterable[Doc]): The documents to predict.
RETURNS: The models prediction for each document.
DOCS: https://spacy.io/api/textcategorizer#predict
DOCS: https://nightly.spacy.io/api/textcategorizer#predict
"""
tensors = [doc.tensor for doc in docs]
if not any(len(doc) for doc in docs):
@ -177,7 +177,7 @@ class TextCategorizer(Pipe):
docs (Iterable[Doc]): The documents to modify.
scores: The scores to set, produced by TextCategorizer.predict.
DOCS: https://spacy.io/api/textcategorizer#set_annotations
DOCS: https://nightly.spacy.io/api/textcategorizer#set_annotations
"""
for i, doc in enumerate(docs):
for j, label in enumerate(self.labels):
@ -204,7 +204,7 @@ class TextCategorizer(Pipe):
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/textcategorizer#update
DOCS: https://nightly.spacy.io/api/textcategorizer#update
"""
if losses is None:
losses = {}
@ -245,7 +245,7 @@ class TextCategorizer(Pipe):
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/textcategorizer#rehearse
DOCS: https://nightly.spacy.io/api/textcategorizer#rehearse
"""
if losses is not None:
losses.setdefault(self.name, 0.0)
@ -289,7 +289,7 @@ class TextCategorizer(Pipe):
scores: Scores representing the model's predictions.
RETUTNRS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/textcategorizer#get_loss
DOCS: https://nightly.spacy.io/api/textcategorizer#get_loss
"""
validate_examples(examples, "TextCategorizer.get_loss")
truths, not_missing = self._examples_to_truth(examples)
@ -305,7 +305,7 @@ class TextCategorizer(Pipe):
label (str): The label to add.
RETURNS (int): 0 if label is already present, otherwise 1.
DOCS: https://spacy.io/api/textcategorizer#add_label
DOCS: https://nightly.spacy.io/api/textcategorizer#add_label
"""
if not isinstance(label, str):
raise ValueError(Errors.E187)
@ -343,7 +343,7 @@ class TextCategorizer(Pipe):
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/textcategorizer#begin_training
DOCS: https://nightly.spacy.io/api/textcategorizer#begin_training
"""
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="TextCategorizer", obj=type(get_examples))
@ -378,7 +378,7 @@ class TextCategorizer(Pipe):
positive_label (str): Optional positive label.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_cats.
DOCS: https://spacy.io/api/textcategorizer#score
DOCS: https://nightly.spacy.io/api/textcategorizer#score
"""
validate_examples(examples, "TextCategorizer.score")
return Scorer.score_cats(

14
spacy/pipeline/tok2vec.py
View File

@ -56,7 +56,7 @@ class Tok2Vec(Pipe):
a list of Doc objects as input, and output a list of 2d float arrays.
name (str): The component instance name.
DOCS: https://spacy.io/api/tok2vec#init
DOCS: https://nightly.spacy.io/api/tok2vec#init
"""
self.vocab = vocab
self.model = model
@ -91,7 +91,7 @@ class Tok2Vec(Pipe):
docs (Doc): The Doc to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/tok2vec#call
DOCS: https://nightly.spacy.io/api/tok2vec#call
"""
tokvecses = self.predict([doc])
self.set_annotations([doc], tokvecses)
@ -106,7 +106,7 @@ class Tok2Vec(Pipe):
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/tok2vec#pipe
DOCS: https://nightly.spacy.io/api/tok2vec#pipe
"""
for docs in minibatch(stream, batch_size):
docs = list(docs)
@ -121,7 +121,7 @@ class Tok2Vec(Pipe):
docs (Iterable[Doc]): The documents to predict.
RETURNS: Vector representations for each token in the documents.
DOCS: https://spacy.io/api/tok2vec#predict
DOCS: https://nightly.spacy.io/api/tok2vec#predict
"""
tokvecs = self.model.predict(docs)
batch_id = Tok2VecListener.get_batch_id(docs)
@ -135,7 +135,7 @@ class Tok2Vec(Pipe):
docs (Iterable[Doc]): The documents to modify.
tokvecses: The tensors to set, produced by Tok2Vec.predict.
DOCS: https://spacy.io/api/tok2vec#set_annotations
DOCS: https://nightly.spacy.io/api/tok2vec#set_annotations
"""
for doc, tokvecs in zip(docs, tokvecses):
assert tokvecs.shape[0] == len(doc)
@ -162,7 +162,7 @@ class Tok2Vec(Pipe):
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/tok2vec#update
DOCS: https://nightly.spacy.io/api/tok2vec#update
"""
if losses is None:
losses = {}
@ -220,7 +220,7 @@ class Tok2Vec(Pipe):
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/tok2vec#begin_training
DOCS: https://nightly.spacy.io/api/tok2vec#begin_training
"""
docs = [Doc(self.vocab, words=["hello"])]
self.model.initialize(X=docs)

14
spacy/schemas.py
View File

@ -57,12 +57,13 @@ def validate_token_pattern(obj: list) -> List[str]:
class TokenPatternString(BaseModel):
REGEX: Optional[StrictStr]
IN: Optional[List[StrictStr]]
NOT_IN: Optional[List[StrictStr]]
REGEX: Optional[StrictStr] = Field(None, alias="regex")
IN: Optional[List[StrictStr]] = Field(None, alias="in")
NOT_IN: Optional[List[StrictStr]] = Field(None, alias="not_in")
class Config:
extra = "forbid"
allow_population_by_field_name = True # allow alias and field name
@validator("*", pre=True, each_item=True, allow_reuse=True)
def raise_for_none(cls, v):
@ -72,9 +73,9 @@ class TokenPatternString(BaseModel):
class TokenPatternNumber(BaseModel):
REGEX: Optional[StrictStr] = None
IN: Optional[List[StrictInt]] = None
NOT_IN: Optional[List[StrictInt]] = None
REGEX: Optional[StrictStr] = Field(None, alias="regex")
IN: Optional[List[StrictInt]] = Field(None, alias="in")
NOT_IN: Optional[List[StrictInt]] = Field(None, alias="not_in")
EQ: Union[StrictInt, StrictFloat] = Field(None, alias="==")
NEQ: Union[StrictInt, StrictFloat] = Field(None, alias="!=")
GEQ: Union[StrictInt, StrictFloat] = Field(None, alias=">=")
@ -84,6 +85,7 @@ class TokenPatternNumber(BaseModel):
class Config:
extra = "forbid"
allow_population_by_field_name = True # allow alias and field name
@validator("*", pre=True, each_item=True, allow_reuse=True)
def raise_for_none(cls, v):

14
spacy/scorer.py
View File

@ -85,7 +85,7 @@ class Scorer:
) -> None:
"""Initialize the Scorer.
DOCS: https://spacy.io/api/scorer#init
DOCS: https://nightly.spacy.io/api/scorer#init
"""
self.nlp = nlp
self.cfg = cfg
@ -101,7 +101,7 @@ class Scorer:
examples (Iterable[Example]): The predicted annotations + correct annotations.
RETURNS (Dict): A dictionary of scores.
DOCS: https://spacy.io/api/scorer#score
DOCS: https://nightly.spacy.io/api/scorer#score
"""
scores = {}
if hasattr(self.nlp.tokenizer, "score"):
@ -121,7 +121,7 @@ class Scorer:
RETURNS (Dict[str, float]): A dictionary containing the scores
token_acc/p/r/f.
DOCS: https://spacy.io/api/scorer#score_tokenization
DOCS: https://nightly.spacy.io/api/scorer#score_tokenization
"""
acc_score = PRFScore()
prf_score = PRFScore()
@ -169,7 +169,7 @@ class Scorer:
RETURNS (Dict[str, float]): A dictionary containing the accuracy score
under the key attr_acc.
DOCS: https://spacy.io/api/scorer#score_token_attr
DOCS: https://nightly.spacy.io/api/scorer#score_token_attr
"""
tag_score = PRFScore()
for example in examples:
@ -263,7 +263,7 @@ class Scorer:
RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under
the keys attr_p/r/f and the per-type PRF scores under attr_per_type.
DOCS: https://spacy.io/api/scorer#score_spans
DOCS: https://nightly.spacy.io/api/scorer#score_spans
"""
score = PRFScore()
score_per_type = dict()
@ -350,7 +350,7 @@ class Scorer:
attr_f_per_type,
attr_auc_per_type
DOCS: https://spacy.io/api/scorer#score_cats
DOCS: https://nightly.spacy.io/api/scorer#score_cats
"""
if threshold is None:
threshold = 0.5 if multi_label else 0.0
@ -467,7 +467,7 @@ class Scorer:
RETURNS (Dict[str, Any]): A dictionary containing the scores:
attr_uas, attr_las, and attr_las_per_type.
DOCS: https://spacy.io/api/scorer#score_deps
DOCS: https://nightly.spacy.io/api/scorer#score_deps
"""
unlabelled = PRFScore()
labelled = PRFScore()

2
spacy/strings.pyx
View File

@ -91,7 +91,7 @@ cdef Utf8Str* _allocate(Pool mem, const unsigned char* chars, uint32_t length) e
cdef class StringStore:
"""Look up strings by 64-bit hashes.
DOCS: https://spacy.io/api/stringstore
DOCS: https://nightly.spacy.io/api/stringstore
"""
def __init__(self, strings=None, freeze=False):
"""Create the StringStore.

10
spacy/tests/conftest.py
View File

@ -44,6 +44,11 @@ def ca_tokenizer():
return get_lang_class("ca")().tokenizer
@pytest.fixture(scope="session")
def cs_tokenizer():
return get_lang_class("cs")().tokenizer
@pytest.fixture(scope="session")
def da_tokenizer():
return get_lang_class("da")().tokenizer
@ -204,6 +209,11 @@ def ru_lemmatizer():
return get_lang_class("ru")().add_pipe("lemmatizer")
@pytest.fixture(scope="session")
def sa_tokenizer():
return get_lang_class("sa")().tokenizer
@pytest.fixture(scope="session")
def sr_tokenizer():
return get_lang_class("sr")().tokenizer

19
spacy/tests/doc/test_doc_api.py
View File

@ -317,7 +317,8 @@ def test_doc_from_array_morph(en_vocab):
def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
en_texts = ["Merging the docs is fun.", "They don't think alike."]
en_texts = ["Merging the docs is fun.", "", "They don't think alike."]
en_texts_without_empty = [t for t in en_texts if len(t)]
de_text = "Wie war die Frage?"
en_docs = [en_tokenizer(text) for text in en_texts]
docs_idx = en_texts[0].index("docs")
@ -338,14 +339,14 @@ def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
Doc.from_docs(en_docs + [de_doc])
m_doc = Doc.from_docs(en_docs)
assert len(en_docs) == len(list(m_doc.sents))
assert len(en_texts_without_empty) == len(list(m_doc.sents))
assert len(str(m_doc)) > len(en_texts[0]) + len(en_texts[1])
assert str(m_doc) == " ".join(en_texts)
assert str(m_doc) == " ".join(en_texts_without_empty)
p_token = m_doc[len(en_docs[0]) - 1]
assert p_token.text == "." and bool(p_token.whitespace_)
en_docs_tokens = [t for doc in en_docs for t in doc]
assert len(m_doc) == len(en_docs_tokens)
think_idx = len(en_texts[0]) + 1 + en_texts[1].index("think")
think_idx = len(en_texts[0]) + 1 + en_texts[2].index("think")
assert m_doc[9].idx == think_idx
with pytest.raises(AttributeError):
# not callable, because it was not set via set_extension
@ -353,14 +354,14 @@ def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
assert len(m_doc.user_data) == len(en_docs[0].user_data) # but it's there
m_doc = Doc.from_docs(en_docs, ensure_whitespace=False)
assert len(en_docs) == len(list(m_doc.sents))
assert len(str(m_doc)) == len(en_texts[0]) + len(en_texts[1])
assert len(en_texts_without_empty) == len(list(m_doc.sents))
assert len(str(m_doc)) == sum(len(t) for t in en_texts)
assert str(m_doc) == "".join(en_texts)
p_token = m_doc[len(en_docs[0]) - 1]
assert p_token.text == "." and not bool(p_token.whitespace_)
en_docs_tokens = [t for doc in en_docs for t in doc]
assert len(m_doc) == len(en_docs_tokens)
think_idx = len(en_texts[0]) + 0 + en_texts[1].index("think")
think_idx = len(en_texts[0]) + 0 + en_texts[2].index("think")
assert m_doc[9].idx == think_idx
m_doc = Doc.from_docs(en_docs, attrs=["lemma", "length", "pos"])
@ -369,12 +370,12 @@ def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
assert list(m_doc.sents)
assert len(str(m_doc)) > len(en_texts[0]) + len(en_texts[1])
# space delimiter considered, although spacy attribute was missing
assert str(m_doc) == " ".join(en_texts)
assert str(m_doc) == " ".join(en_texts_without_empty)
p_token = m_doc[len(en_docs[0]) - 1]
assert p_token.text == "." and bool(p_token.whitespace_)
en_docs_tokens = [t for doc in en_docs for t in doc]
assert len(m_doc) == len(en_docs_tokens)
think_idx = len(en_texts[0]) + 1 + en_texts[1].index("think")
think_idx = len(en_texts[0]) + 1 + en_texts[2].index("think")
assert m_doc[9].idx == think_idx

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

@ -162,11 +162,36 @@ def test_spans_are_hashable(en_tokenizer):
def test_spans_by_character(doc):
span1 = doc[1:-2]
# default and specified alignment mode "strict"
span2 = doc.char_span(span1.start_char, span1.end_char, label="GPE")
assert span1.start_char == span2.start_char
assert span1.end_char == span2.end_char
assert span2.label_ == "GPE"
span2 = doc.char_span(
span1.start_char, span1.end_char, label="GPE", alignment_mode="strict"
)
assert span1.start_char == span2.start_char
assert span1.end_char == span2.end_char
assert span2.label_ == "GPE"
# alignment mode "contract"
span2 = doc.char_span(
span1.start_char - 3, span1.end_char, label="GPE", alignment_mode="contract"
)
assert span1.start_char == span2.start_char
assert span1.end_char == span2.end_char
assert span2.label_ == "GPE"
# alignment mode "expand"
span2 = doc.char_span(
span1.start_char + 1, span1.end_char, label="GPE", alignment_mode="expand"
)
assert span1.start_char == span2.start_char
assert span1.end_char == span2.end_char
assert span2.label_ == "GPE"
def test_span_to_array(doc):
span = doc[1:-2]

0
spacy/tests/lang/cs/__init__.py Normal file
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23
spacy/tests/lang/cs/test_text.py Normal file
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@ -0,0 +1,23 @@
import pytest
@pytest.mark.parametrize(
"text,match",
[
("10", True),
("1", True),
("10.000", True),
("1000", True),
("999,0", True),
("devatenáct", True),
("osmdesát", True),
("kvadrilion", True),
("Pes", False),
(",", False),
("1/2", True),
],
)
def test_lex_attrs_like_number(cs_tokenizer, text, match):
tokens = cs_tokenizer(text)
assert len(tokens) == 1
assert tokens[0].like_num == match

5
spacy/tests/lang/en/test_text.py
View File

@ -56,6 +56,11 @@ def test_lex_attrs_like_number(en_tokenizer, text, match):
assert tokens[0].like_num == match
@pytest.mark.parametrize("word", ["third", "Millionth", "100th", "Hundredth"])
def test_en_lex_attrs_like_number_for_ordinal(word):
assert like_num(word)
@pytest.mark.parametrize("word", ["eleven"])
def test_en_lex_attrs_capitals(word):
assert like_num(word)

28
spacy/tests/lang/he/test_tokenizer.py
View File

@ -1,4 +1,5 @@
import pytest
from spacy.lang.he.lex_attrs import like_num
@pytest.mark.parametrize(
@ -39,3 +40,30 @@ def test_he_tokenizer_handles_abbreviation(he_tokenizer, text, expected_tokens):
def test_he_tokenizer_handles_punct(he_tokenizer, text, expected_tokens):
tokens = he_tokenizer(text)
assert expected_tokens == [token.text for token in tokens]
@pytest.mark.parametrize(
"text,match",
[
("10", True),
("1", True),
("10,000", True),
("10,00", True),
("999.0", True),
("אחד", True),
("שתיים", True),
("מליון", True),
("כלב", False),
(",", False),
("1/2", True),
],
)
def test_lex_attrs_like_number(he_tokenizer, text, match):
tokens = he_tokenizer(text)
assert len(tokens) == 1
assert tokens[0].like_num == match
@pytest.mark.parametrize("word", ["שלישי", "מליון", "עשירי", "מאה", "עשר", "אחד עשר"])
def test_he_lex_attrs_like_number_for_ordinal(word):
assert like_num(word)

3
spacy/tests/lang/ne/test_text.py
View File

@ -1,6 +1,3 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest

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