Merge pull request #5619 from explosion/master-tmp

.github/contributors/Arvindcheenu.md

@@ -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                           | Arvind Srinivasan    |
+| Company name (if applicable)   |                      |
+| Title or role (if applicable)  |                      |
+| Date                           | 2020-06-13           |
+| GitHub username                | arvindcheenu         |
+| Website (optional)             |                      |

.github/contributors/JannisTriesToCode.md

@@ -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                           | Jannis Rauschke               |
+| Company name (if applicable)   |                               |
+| Title or role (if applicable)  |                               |
+| Date                           | 22.05.2020                    |
+| GitHub username                | JannisTriesToCode             |
+| Website (optional)             | https://twitter.com/JRauschke |

.github/contributors/MartinoMensio.md

@@ -99,8 +99,8 @@ mark both statements:
 | Field                          | Entry                              |
 |------------------------------- | --------------------               |
 | Name                           | Martino Mensio                     |
-| Company name (if applicable)   | Polytechnic University of Turin    |
+| Company name (if applicable)   | The Open University                |
-| Title or role (if applicable)  | Student                            |
+| Title or role (if applicable)  | PhD Student                        |
 | Date                           | 17 November 2017                   |
 | GitHub username                | MartinoMensio                      |
 | Website (optional)             | https://martinomensio.github.io/   |

.github/contributors/R1j1t.md

@@ -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                           | Rajat                     |
+| Company name (if applicable)   |                      |
+| Title or role (if applicable)  |                      |
+| Date                           |  24 May 2020                    |
+| GitHub username                |  R1j1t                    |
+| Website (optional)             |                      |

.github/contributors/hiroshi-matsuda-rit.md

@@ -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                           | Hiroshi Matsuda      |
+| Company name (if applicable)   | Megagon Labs, Tokyo  |
+| Title or role (if applicable)  | Research Scientist   |
+| Date                           | June 6, 2020         |
+| GitHub username                | hiroshi-matsuda-rit  |
+| Website (optional)             |                      |

.github/contributors/jonesmartins.md

@@ -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                           | Jones Martins        |
+| Company name (if applicable)   |                      |
+| Title or role (if applicable)  |                      |
+| Date                           | 2020-06-10           |
+| GitHub username                | jonesmartins         |
+| Website (optional)             |                      |

.github/contributors/leomrocha.md

@@ -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                           | Leonardo M. Rocha    |
+| Company name (if applicable)   |                      |
+| Title or role (if applicable)  |  Eng.                |
+| Date                           |  31/05/2020          |
+| GitHub username                |  leomrocha           |
+| Website (optional)             |                      |

.github/contributors/lfiedler.md

@@ -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                           | Leander Fiedler      |
+| Company name (if applicable)   |                      |
+| Title or role (if applicable)  |                      |
+| Date                           | 06 April 2020        |
+| GitHub username                | lfiedler             |
+| Website (optional)             |                      |

.github/contributors/mahnerak.md

@@ -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                           | Karen Hambardzumyan  |
+| Company name (if applicable)   | YerevaNN             |
+| Title or role (if applicable)  | Researcher           |
+| Date                           | 2020-06-19           |
+| GitHub username                | mahnerak             |
+| Website (optional)             | https://mahnerak.com/|

.github/contributors/myavrum.md

@@ -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                           | Marat M. Yavrumyan   |
+| Company name (if applicable)   | YSU, UD_Armenian Project |
+| Title or role (if applicable)  | Dr., Principal Investigator |
+| Date                           | 2020-06-19           |
+| GitHub username                | myavrum              |
+| Website (optional)             | http://armtreebank.yerevann.com/ |

.github/contributors/theudas.md

@@ -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 UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
+**"you"** shall mean the person or entity identified below.
+
+If you agree to be bound by these terms, fill in the information requested
+below and include the filled-in version with your first pull request, under the
+folder [`.github/contributors/`](/.github/contributors/). The name of the file
+should be your GitHub username, with the extension `.md`. For example, the user
+example_user would create the file `.github/contributors/example_user.md`.
+
+Read this agreement carefully before signing. These terms and conditions
+constitute a binding legal agreement.
+
+## Contributor Agreement
+
+1. The term "contribution" or "contributed materials" means any source code,
+object code, patch, tool, sample, graphic, specification, manual,
+documentation, or any other material posted or submitted by you to the project.
+
+2. With respect to any worldwide copyrights, or copyright applications and
+registrations, in your contribution:
+
+    * you hereby assign to us joint ownership, and to the extent that such
+    assignment is or becomes invalid, ineffective or unenforceable, you hereby
+    grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
+    royalty-free, unrestricted license to exercise all rights under those
+    copyrights. This includes, at our option, the right to sublicense these same
+    rights to third parties through multiple levels of sublicensees or other
+    licensing arrangements;
+
+    * you agree that each of us can do all things in relation to your
+    contribution as if each of us were the sole owners, and if one of us makes
+    a derivative work of your contribution, the one who makes the derivative
+    work (or has it made will be the sole owner of that derivative work;
+
+    * you agree that you will not assert any moral rights in your contribution
+    against us, our licensees or transferees;
+
+    * you agree that we may register a copyright in your contribution and
+    exercise all ownership rights associated with it; and
+
+    * you agree that neither of us has any duty to consult with, obtain the
+    consent of, pay or render an accounting to the other for any use or
+    distribution of your contribution.
+
+3. With respect to any patents you own, or that you can license without payment
+to any third party, you hereby grant to us a perpetual, irrevocable,
+non-exclusive, worldwide, no-charge, royalty-free license to:
+
+    * make, have made, use, sell, offer to sell, import, and otherwise transfer
+    your contribution in whole or in part, alone or in combination with or
+    included in any product, work or materials arising out of the project to
+    which your contribution was submitted, and
+
+    * at our option, to sublicense these same rights to third parties through
+    multiple levels of sublicensees or other licensing arrangements.
+
+4. Except as set out above, you keep all right, title, and interest in your
+contribution. The rights that you grant to us under these terms are effective
+on the date you first submitted a contribution to us, even if your submission
+took place before the date you sign these terms.
+
+5. You covenant, represent, warrant and agree that:
+
+    * Each contribution that you submit is and shall be an original work of
+    authorship and you can legally grant the rights set out in this SCA;
+
+    * to the best of your knowledge, each contribution will not violate any
+    third party's copyrights, trademarks, patents, or other intellectual
+    property rights; and
+
+    * each contribution shall be in compliance with U.S. export control laws and
+    other applicable export and import laws. You agree to notify us if you
+    become aware of any circumstance which would make any of the foregoing
+    representations inaccurate in any respect. We may publicly disclose your 
+    participation in the project, including the fact that you have signed the SCA.
+
+6. This SCA is governed by the laws of the State of California and applicable
+U.S. Federal law. Any choice of law rules will not apply.
+
+7. Please place an “x” on one of the applicable statement below. Please do NOT
+mark both statements:
+
+    * [x] I am signing on behalf of myself as an individual and no other person
+    or entity, including my employer, has or will have rights with respect to my
+    contributions.
+
+    * [ ] I am signing on behalf of my employer or a legal entity and I have the
+    actual authority to contractually bind that entity.
+
+## Contributor Details
+
+| Field                          | Entry                    |
+|------------------------------- | ------------------------ |
+| Name                           | Philipp Sodmann          |
+| Company name (if applicable)   | Empolis                  |
+| Title or role (if applicable)  |                          |
+| Date                           | 2017-05-06               |
+| GitHub username                | theudas                  |
+| Website (optional)             |                          |

.github/workflows/issue-manager.yml

@@ -0,0 +1,29 @@
+name: Issue Manager
+
+on:
+  schedule:
+    - cron: "0 0 * * *"
+  issue_comment:
+    types:
+      - created
+      - edited
+  issues:
+    types:
+      - labeled
+
+jobs:
+  issue-manager:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: tiangolo/issue-manager@0.2.1
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          config: >
+            {
+              "resolved": {
+                "delay": "P7D",
+                "message": "This issue has been automatically closed because it was answered and there was no follow-up discussion.",
+                "remove_label_on_comment": true,
+                "remove_label_on_close": true
+              }
+            }

Makefile

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

README.md

@@ -6,12 +6,12 @@ spaCy is a library for advanced Natural Language Processing in Python and
 Cython. It's built on the very latest research, and was designed from day one to
 be used in real products. spaCy comes with
 [pretrained statistical models](https://spacy.io/models) and word vectors, and
-currently supports tokenization for **50+ languages**. It features
+currently supports tokenization for **60+ languages**. It features
 state-of-the-art speed, convolutional **neural network models** for tagging,
 parsing and **named entity recognition** and easy **deep learning** integration.
 It's commercial open-source software, released under the MIT license.
 
-💫 **Version 2.2 out now!**
+💫 **Version 2.3 out now!**
 [Check out the release notes here.](https://github.com/explosion/spaCy/releases)
 
 [![Azure Pipelines](<https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build+(3.x)>)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
@@ -31,7 +31,7 @@ It's commercial open-source software, released under the MIT license.
 | --------------- | -------------------------------------------------------------- |
 | [spaCy 101]     | New to spaCy? Here's everything you need to know!              |
 | [Usage Guides]  | How to use spaCy and its features.                             |
-| [New in v2.2]   | New features, backwards incompatibilities and migration guide. |
+| [New in v2.3]   | New features, backwards incompatibilities and migration guide. |
 | [API Reference] | The detailed reference for spaCy's API.                        |
 | [Models]        | Download statistical language models for spaCy.                |
 | [Universe]      | Libraries, extensions, demos, books and courses.               |
@@ -39,7 +39,7 @@ It's commercial open-source software, released under the MIT license.
 | [Contribute]    | How to contribute to the spaCy project and code base.          |
 
 [spacy 101]: https://spacy.io/usage/spacy-101
-[new in v2.2]: https://spacy.io/usage/v2-2
+[new in v2.3]: https://spacy.io/usage/v2-3
 [usage guides]: https://spacy.io/usage/
 [api reference]: https://spacy.io/api/
 [models]: https://spacy.io/models
@@ -119,12 +119,13 @@ of `v2.0.13`).
 pip install spacy
 ```
 
-To install additional data tables for lemmatization in **spaCy v2.2+** you can
+To install additional data tables for lemmatization and normalization in
-run `pip install spacy[lookups]` or install
+**spaCy v2.2+** you can run `pip install spacy[lookups]` or install
 [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)
 separately. The lookups package is needed to create blank models with
-lemmatization data, and to lemmatize in languages that don't yet come with
+lemmatization data for v2.2+ plus normalization data for v2.3+, and to
-pretrained models and aren't powered by third-party libraries.
+lemmatize in languages that don't yet come with pretrained models and aren't
+powered by third-party libraries.
 
 When using pip it is generally recommended to install packages in a virtual
 environment to avoid modifying system state:

examples/training/train_intent_parser.py

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

setup.cfg

@@ -61,7 +61,7 @@ install_requires =
 
 [options.extras_require]
 lookups =
-    spacy_lookups_data>=0.3.1,<0.4.0
+    spacy_lookups_data>=0.3.2,<0.4.0
 cuda =
     cupy>=5.0.0b4,<9.0.0
 cuda80 =
@@ -80,7 +80,8 @@ cuda102 =
     cupy-cuda102>=5.0.0b4,<9.0.0
 # Language tokenizers with external dependencies
 ja =
-    fugashi>=0.1.3
+    sudachipy>=0.4.5
+    sudachidict_core>=20200330
 ko =
     natto-py==0.9.0
 th =

spacy/errors.py

@@ -3,7 +3,7 @@ def add_codes(err_cls):
 
     class ErrorsWithCodes(err_cls):
         def __getattribute__(self, code):
-            msg = super().__getattribute__(code)
+            msg = super(ErrorsWithCodes, self).__getattribute__(code)
             if code.startswith("__"):  # python system attributes like __class__
                 return msg
             else:
@@ -111,6 +111,25 @@ class Warnings(object):
             "`spacy.gold.biluo_tags_from_offsets(nlp.make_doc(text), entities)`"
             " to check the alignment. Misaligned entities ('-') will be "
             "ignored during training.")
+    W031 = ("Model '{model}' ({model_version}) requires spaCy {version} and "
+            "is incompatible with the current spaCy version ({current}). This "
+            "may lead to unexpected results or runtime errors. To resolve "
+            "this, download a newer compatible model or retrain your custom "
+            "model with the current spaCy version. For more details and "
+            "available updates, run: python -m spacy validate")
+    W032 = ("Unable to determine model compatibility for model '{model}' "
+            "({model_version}) with the current spaCy version ({current}). "
+            "This may lead to unexpected results or runtime errors. To resolve "
+            "this, download a newer compatible model or retrain your custom "
+            "model with the current spaCy version. For more details and "
+            "available updates, run: python -m spacy validate")
+    W033 = ("Training a new {model} using a model with no lexeme normalization "
+            "table. This may degrade the performance of the model to some "
+            "degree. If this is intentional or the language you're using "
+            "doesn't have a normalization table, please ignore this warning. "
+            "If this is surprising, make sure you have the spacy-lookups-data "
+            "package installed. The languages with lexeme normalization tables "
+            "are currently: da, de, el, en, id, lb, pt, ru, sr, ta, th.")
 
     # TODO: fix numbering after merging develop into master
     W094 = ("Model '{model}' ({model_version}) specifies an under-constrained "
@@ -578,6 +597,9 @@ class Errors(object):
     E197 = ("Row out of bounds, unable to add row {row} for key {key}.")
     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.")
 
     # TODO: fix numbering after merging develop into master
     E983 = ("Invalid key for '{dict_name}': {key}. Available keys: "

spacy/gold.pyx

@@ -1052,6 +1052,7 @@ cdef class GoldParse:
             representing the external IDs in a knowledge base (KB)
             mapped to either 1.0 or 0.0, indicating positive and
             negative examples respectively.
+        make_projective (bool): Whether to projectivize the dependency tree.
         RETURNS (GoldParse): The newly constructed object.
         """
         self.mem = Pool()

spacy/kb.pyx

@@ -446,6 +446,8 @@ cdef class Writer:
             assert not path.isdir(loc), f"{loc} is directory"
         if isinstance(loc, Path):
             loc = bytes(loc)
+        if path.exists(loc):
+            assert not path.isdir(loc), "%s is directory." % loc
         cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
         self._fp = fopen(<char*>bytes_loc, 'wb')
         if not self._fp:
@@ -487,10 +489,10 @@ cdef class Writer:
 
 cdef class Reader:
     def __init__(self, object loc):
-        assert path.exists(loc)
-        assert not path.isdir(loc)
         if isinstance(loc, Path):
             loc = bytes(loc)
+        assert path.exists(loc)
+        assert not path.isdir(loc)
         cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
         self._fp = fopen(<char*>bytes_loc, 'rb')
         if not self._fp:

spacy/lang/el/syntax_iterators.py

@@ -20,29 +20,25 @@ def noun_chunks(doclike):
     conj = doc.vocab.strings.add("conj")
     nmod = doc.vocab.strings.add("nmod")
     np_label = doc.vocab.strings.add("NP")
-    seen = set()
+    prev_end = -1
     for i, word in enumerate(doclike):
         if word.pos not in (NOUN, PROPN, PRON):
             continue
         # Prevent nested chunks from being produced
-        if word.i in seen:
+        if word.left_edge.i <= prev_end:
             continue
         if word.dep in np_deps:
-            if any(w.i in seen for w in word.subtree):
-                continue
             flag = False
             if word.pos == NOUN:
                 #  check for patterns such as γραμμή παραγωγής
                 for potential_nmod in word.rights:
                     if potential_nmod.dep == nmod:
-                        seen.update(
+                        prev_end = potential_nmod.i
-                            j for j in range(word.left_edge.i, potential_nmod.i + 1)
-                        )
                         yield word.left_edge.i, potential_nmod.i + 1, np_label
                         flag = True
                         break
             if flag is False:
-                seen.update(j for j in range(word.left_edge.i, word.i + 1))
+                prev_end = word.i
                 yield word.left_edge.i, word.i + 1, np_label
         elif word.dep == conj:
             # covers the case: έχει όμορφα και έξυπνα παιδιά
@@ -51,9 +47,7 @@ def noun_chunks(doclike):
                 head = head.head
             # If the head is an NP, and we're coordinated to it, we're an NP
             if head.dep in np_deps:
-                if any(w.i in seen for w in word.subtree):
+                prev_end = word.i
-                    continue
-                seen.update(j for j in range(word.left_edge.i, word.i + 1))
                 yield word.left_edge.i, word.i + 1, np_label
 
 

spacy/lang/en/syntax_iterators.py

@@ -25,17 +25,15 @@ def noun_chunks(doclike):
     np_deps = [doc.vocab.strings.add(label) for label in labels]
     conj = doc.vocab.strings.add("conj")
     np_label = doc.vocab.strings.add("NP")
-    seen = set()
+    prev_end = -1
     for i, word in enumerate(doclike):
         if word.pos not in (NOUN, PROPN, PRON):
             continue
         # Prevent nested chunks from being produced
-        if word.i in seen:
+        if word.left_edge.i <= prev_end:
             continue
         if word.dep in np_deps:
-            if any(w.i in seen for w in word.subtree):
+            prev_end = word.i
-                continue
-            seen.update(j for j in range(word.left_edge.i, word.i + 1))
             yield word.left_edge.i, word.i + 1, np_label
         elif word.dep == conj:
             head = word.head
@@ -43,9 +41,7 @@ def noun_chunks(doclike):
                 head = head.head
             # If the head is an NP, and we're coordinated to it, we're an NP
             if head.dep in np_deps:
-                if any(w.i in seen for w in word.subtree):
+                prev_end = word.i
-                    continue
-                seen.update(j for j in range(word.left_edge.i, word.i + 1))
                 yield word.left_edge.i, word.i + 1, np_label
 
 

spacy/lang/en/tokenizer_exceptions.py

@@ -136,7 +136,19 @@ for pron in ["he", "she", "it"]:
 
 # W-words, relative pronouns, prepositions etc.
 
-for word in ["who", "what", "when", "where", "why", "how", "there", "that"]:
+for word in [
+    "who",
+    "what",
+    "when",
+    "where",
+    "why",
+    "how",
+    "there",
+    "that",
+    "this",
+    "these",
+    "those",
+]:
     for orth in [word, word.title()]:
         _exc[orth + "'s"] = [
             {ORTH: orth, LEMMA: word, NORM: word},
@@ -396,6 +408,8 @@ _other_exc = {
         {ORTH: "Let", LEMMA: "let", NORM: "let"},
         {ORTH: "'s", LEMMA: PRON_LEMMA, NORM: "us"},
     ],
+    "c'mon": [{ORTH: "c'm", NORM: "come", LEMMA: "come"}, {ORTH: "on"}],
+    "C'mon": [{ORTH: "C'm", NORM: "come", LEMMA: "come"}, {ORTH: "on"}],
 }
 
 _exc.update(_other_exc)

spacy/lang/es/examples.py

@@ -14,5 +14,9 @@ sentences = [
     "El gato come pescado.",
     "Veo al hombre con el telescopio.",
     "La araña come moscas.",
-    "El pingüino incuba en su nido.",
+    "El pingüino incuba en su nido sobre el hielo.",
+    "¿Dónde estais?",
+    "¿Quién es el presidente Francés?",
+    "¿Dónde está encuentra la capital de Argentina?",
+    "¿Cuándo nació José de San Martín?",
 ]

spacy/lang/es/punctuation.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES
 from ..char_classes import LIST_ICONS, CURRENCY, LIST_UNITS, PUNCT
 from ..char_classes import CONCAT_QUOTES, ALPHA_LOWER, ALPHA_UPPER, ALPHA

spacy/lang/es/tokenizer_exceptions.py

@@ -7,8 +7,12 @@ _exc = {
 
 
 for exc_data in [
+    {ORTH: "n°", LEMMA: "número"},
+    {ORTH: "°C", LEMMA: "grados Celcius"},
     {ORTH: "aprox.", LEMMA: "aproximadamente"},
     {ORTH: "dna.", LEMMA: "docena"},
+    {ORTH: "dpto.", LEMMA: "departamento"},
+    {ORTH: "ej.", LEMMA: "ejemplo"},
     {ORTH: "esq.", LEMMA: "esquina"},
     {ORTH: "pág.", LEMMA: "página"},
     {ORTH: "p.ej.", LEMMA: "por ejemplo"},
@@ -16,6 +20,7 @@ for exc_data in [
     {ORTH: "Vd.", LEMMA: PRON_LEMMA, NORM: "usted"},
     {ORTH: "Uds.", LEMMA: PRON_LEMMA, NORM: "ustedes"},
     {ORTH: "Vds.", LEMMA: PRON_LEMMA, NORM: "ustedes"},
+    {ORTH: "vol.", NORM: "volúmen"},
 ]:
     _exc[exc_data[ORTH]] = [exc_data]
 
@@ -35,10 +40,14 @@ for h in range(1, 12 + 1):
 for orth in [
     "a.C.",
     "a.J.C.",
+    "d.C.",
+    "d.J.C.",
     "apdo.",
     "Av.",
     "Avda.",
     "Cía.",
+    "Dr.",
+    "Dra.",
     "EE.UU.",
     "etc.",
     "fig.",
@@ -54,9 +63,9 @@ for orth in [
     "Prof.",
     "Profa.",
     "q.e.p.d.",
-    "S.A.",
+    "Q.E.P.D." "S.A.",
     "S.L.",
-    "s.s.s.",
+    "S.R.L." "s.s.s.",
     "Sr.",
     "Sra.",
     "Srta.",

spacy/lang/fa/syntax_iterators.py

@@ -25,17 +25,15 @@ def noun_chunks(doclike):
     np_deps = [doc.vocab.strings.add(label) for label in labels]
     conj = doc.vocab.strings.add("conj")
     np_label = doc.vocab.strings.add("NP")
-    seen = set()
+    prev_end = -1
     for i, word in enumerate(doclike):
         if word.pos not in (NOUN, PROPN, PRON):
             continue
         # Prevent nested chunks from being produced
-        if word.i in seen:
+        if word.left_edge.i <= prev_end:
             continue
         if word.dep in np_deps:
-            if any(w.i in seen for w in word.subtree):
+            prev_end = word.i
-                continue
-            seen.update(j for j in range(word.left_edge.i, word.i + 1))
             yield word.left_edge.i, word.i + 1, np_label
         elif word.dep == conj:
             head = word.head
@@ -43,9 +41,7 @@ def noun_chunks(doclike):
                 head = head.head
             # If the head is an NP, and we're coordinated to it, we're an NP
             if head.dep in np_deps:
-                if any(w.i in seen for w in word.subtree):
+                prev_end = word.i
-                    continue
-                seen.update(j for j in range(word.left_edge.i, word.i + 1))
                 yield word.left_edge.i, word.i + 1, np_label
 
 

spacy/lang/fr/_tokenizer_exceptions_list.py

@@ -531,7 +531,6 @@ FR_BASE_EXCEPTIONS = [
     "Beaumont-Hamel",
     "Beaumont-Louestault",
     "Beaumont-Monteux",
-    "Beaumont-Pied-de-Bœuf",
     "Beaumont-Pied-de-Bœuf",
     "Beaumont-Sardolles",
     "Beaumont-Village",
@@ -948,7 +947,7 @@ FR_BASE_EXCEPTIONS = [
     "Buxières-sous-les-Côtes",
     "Buzy-Darmont",
     "Byhleguhre-Byhlen",
-    "Bœurs-en-Othe",
+    "Bœurs-en-Othe",
     "Bâle-Campagne",
     "Bâle-Ville",
     "Béard-Géovreissiat",
@@ -1586,11 +1585,11 @@ FR_BASE_EXCEPTIONS = [
     "Cruci-Falgardiens",
     "Cruquius-Oost",
     "Cruviers-Lascours",
-    "Crèvecœur-en-Auge",
+    "Crèvecœur-en-Auge",
-    "Crèvecœur-en-Brie",
+    "Crèvecœur-en-Brie",
-    "Crèvecœur-le-Grand",
+    "Crèvecœur-le-Grand",
-    "Crèvecœur-le-Petit",
+    "Crèvecœur-le-Petit",
-    "Crèvecœur-sur-l'Escaut",
+    "Crèvecœur-sur-l'Escaut",
     "Crécy-Couvé",
     "Créon-d'Armagnac",
     "Cubjac-Auvézère-Val-d'Ans",
@@ -1616,7 +1615,7 @@ FR_BASE_EXCEPTIONS = [
     "Cuxac-Cabardès",
     "Cuxac-d'Aude",
     "Cuyk-Sainte-Agathe",
-    "Cœuvres-et-Valsery",
+    "Cœuvres-et-Valsery",
     "Céaux-d'Allègre",
     "Céleste-Empire",
     "Cénac-et-Saint-Julien",
@@ -1679,7 +1678,7 @@ FR_BASE_EXCEPTIONS = [
     "Devrai-Gondragnières",
     "Dhuys et Morin-en-Brie",
     "Diane-Capelle",
-    "Dieffenbach-lès-Wœrth",
+    "Dieffenbach-lès-Wœrth",
     "Diekhusen-Fahrstedt",
     "Diennes-Aubigny",
     "Diensdorf-Radlow",
@@ -1752,7 +1751,7 @@ FR_BASE_EXCEPTIONS = [
     "Durdat-Larequille",
     "Durfort-Lacapelette",
     "Durfort-et-Saint-Martin-de-Sossenac",
-    "Dœuil-sur-le-Mignon",
+    "Dœuil-sur-le-Mignon",
     "Dão-Lafões",
     "Débats-Rivière-d'Orpra",
     "Décines-Charpieu",
@@ -2687,8 +2686,8 @@ FR_BASE_EXCEPTIONS = [
     "Kuhlen-Wendorf",
     "KwaZulu-Natal",
     "Kyzyl-Arvat",
-    "Kœur-la-Grande",
+    "Kœur-la-Grande",
-    "Kœur-la-Petite",
+    "Kœur-la-Petite",
     "Kölln-Reisiek",
     "Königsbach-Stein",
     "Königshain-Wiederau",
@@ -4024,7 +4023,7 @@ FR_BASE_EXCEPTIONS = [
     "Marcilly-d'Azergues",
     "Marcillé-Raoul",
     "Marcillé-Robert",
-    "Marcq-en-Barœul",
+    "Marcq-en-Barœul",
     "Marcy-l'Etoile",
     "Marcy-l'Étoile",
     "Mareil-Marly",
@@ -4258,7 +4257,7 @@ FR_BASE_EXCEPTIONS = [
     "Monlezun-d'Armagnac",
     "Monléon-Magnoac",
     "Monnetier-Mornex",
-    "Mons-en-Barœul",
+    "Mons-en-Barœul",
     "Monsempron-Libos",
     "Monsteroux-Milieu",
     "Montacher-Villegardin",
@@ -4348,7 +4347,7 @@ FR_BASE_EXCEPTIONS = [
     "Mornay-Berry",
     "Mortain-Bocage",
     "Morteaux-Couliboeuf",
-    "Morteaux-Coulibœuf",
+    "Morteaux-Coulibœuf",
     "Morteaux-Coulibœuf",
     "Mortes-Frontières",
     "Mory-Montcrux",
@@ -4391,7 +4390,7 @@ FR_BASE_EXCEPTIONS = [
     "Muncq-Nieurlet",
     "Murtin-Bogny",
     "Murtin-et-le-Châtelet",
-    "Mœurs-Verdey",
+    "Mœurs-Verdey",
     "Ménestérol-Montignac",
     "Ménil'muche",
     "Ménil-Annelles",
@@ -4612,7 +4611,7 @@ FR_BASE_EXCEPTIONS = [
     "Neuves-Maisons",
     "Neuvic-Entier",
     "Neuvicq-Montguyon",
-    "Neuville-lès-Lœuilly",
+    "Neuville-lès-Lœuilly",
     "Neuvy-Bouin",
     "Neuvy-Deux-Clochers",
     "Neuvy-Grandchamp",
@@ -4773,8 +4772,8 @@ FR_BASE_EXCEPTIONS = [
     "Nuncq-Hautecôte",
     "Nurieux-Volognat",
     "Nuthe-Urstromtal",
-    "Nœux-les-Mines",
+    "Nœux-les-Mines",
-    "Nœux-lès-Auxi",
+    "Nœux-lès-Auxi",
     "Nâves-Parmelan",
     "Nézignan-l'Evêque",
     "Nézignan-l'Évêque",
@@ -5343,7 +5342,7 @@ FR_BASE_EXCEPTIONS = [
     "Quincy-Voisins",
     "Quincy-sous-le-Mont",
     "Quint-Fonsegrives",
-    "Quœux-Haut-Maînil",
+    "Quœux-Haut-Maînil",
     "Quœux-Haut-Maînil",
     "Qwa-Qwa",
     "R.-V.",
@@ -5631,12 +5630,12 @@ FR_BASE_EXCEPTIONS = [
     "Saint Aulaye-Puymangou",
     "Saint Geniez d'Olt et d'Aubrac",
     "Saint Martin de l'If",
-    "Saint-Denœux",
+    "Saint-Denœux",
-    "Saint-Jean-de-Bœuf",
+    "Saint-Jean-de-Bœuf",
-    "Saint-Martin-le-Nœud",
+    "Saint-Martin-le-Nœud",
-    "Saint-Michel-Tubœuf",
+    "Saint-Michel-Tubœuf",
     "Saint-Paul - Flaugnac",
-    "Saint-Pierre-de-Bœuf",
+    "Saint-Pierre-de-Bœuf",
     "Saint-Thegonnec Loc-Eguiner",
     "Sainte-Alvère-Saint-Laurent Les Bâtons",
     "Salignac-Eyvignes",
@@ -6208,7 +6207,7 @@ FR_BASE_EXCEPTIONS = [
     "Tite-Live",
     "Titisee-Neustadt",
     "Tobel-Tägerschen",
-    "Togny-aux-Bœufs",
+    "Togny-aux-Bœufs",
     "Tongre-Notre-Dame",
     "Tonnay-Boutonne",
     "Tonnay-Charente",
@@ -6336,7 +6335,7 @@ FR_BASE_EXCEPTIONS = [
     "Vals-près-le-Puy",
     "Valverde-Enrique",
     "Valzin-en-Petite-Montagne",
-    "Vandœuvre-lès-Nancy",
+    "Vandœuvre-lès-Nancy",
     "Varces-Allières-et-Risset",
     "Varenne-l'Arconce",
     "Varenne-sur-le-Doubs",
@@ -6457,9 +6456,9 @@ FR_BASE_EXCEPTIONS = [
     "Villenave-d'Ornon",
     "Villequier-Aumont",
     "Villerouge-Termenès",
-    "Villers-aux-Nœuds",
+    "Villers-aux-Nœuds",
     "Villez-sur-le-Neubourg",
-    "Villiers-en-Désœuvre",
+    "Villiers-en-Désœuvre",
     "Villieu-Loyes-Mollon",
     "Villingen-Schwenningen",
     "Villié-Morgon",
@@ -6467,7 +6466,7 @@ FR_BASE_EXCEPTIONS = [
     "Vilosnes-Haraumont",
     "Vilters-Wangs",
     "Vincent-Froideville",
-    "Vincy-Manœuvre",
+    "Vincy-Manœuvre",
     "Vincy-Manœuvre",
     "Vincy-Reuil-et-Magny",
     "Vindrac-Alayrac",
@@ -6511,8 +6510,8 @@ FR_BASE_EXCEPTIONS = [
     "Vrigne-Meusiens",
     "Vrijhoeve-Capelle",
     "Vuisternens-devant-Romont",
-    "Vœlfling-lès-Bouzonville",
+    "Vœlfling-lès-Bouzonville",
-    "Vœuil-et-Giget",
+    "Vœuil-et-Giget",
     "Vélez-Blanco",
     "Vélez-Málaga",
     "Vélez-Rubio",
@@ -6615,7 +6614,7 @@ FR_BASE_EXCEPTIONS = [
     "Wust-Fischbeck",
     "Wutha-Farnroda",
     "Wy-dit-Joli-Village",
-    "Wœlfling-lès-Sarreguemines",
+    "Wœlfling-lès-Sarreguemines",
     "Wünnewil-Flamatt",
     "X-SAMPA",
     "X-arbre",

spacy/lang/fr/syntax_iterators.py

@@ -24,17 +24,15 @@ def noun_chunks(doclike):
     np_deps = [doc.vocab.strings[label] for label in labels]
     conj = doc.vocab.strings.add("conj")
     np_label = doc.vocab.strings.add("NP")
-    seen = set()
+    prev_end = -1
     for i, word in enumerate(doclike):
         if word.pos not in (NOUN, PROPN, PRON):
             continue
         # Prevent nested chunks from being produced
-        if word.i in seen:
+        if word.left_edge.i <= prev_end:
             continue
         if word.dep in np_deps:
-            if any(w.i in seen for w in word.subtree):
+            prev_end = word.right_edge.i
-                continue
-            seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
             yield word.left_edge.i, word.right_edge.i + 1, np_label
         elif word.dep == conj:
             head = word.head
@@ -42,9 +40,7 @@ def noun_chunks(doclike):
                 head = head.head
             # If the head is an NP, and we're coordinated to it, we're an NP
             if head.dep in np_deps:
-                if any(w.i in seen for w in word.subtree):
+                prev_end = word.right_edge.i
-                    continue
-                seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
                 yield word.left_edge.i, word.right_edge.i + 1, np_label
 
 

spacy/lang/fr/tokenizer_exceptions.py

@@ -1,7 +1,6 @@
 import re
 
 from .punctuation import ELISION, HYPHENS
-from ..tokenizer_exceptions import URL_PATTERN
 from ..char_classes import ALPHA_LOWER, ALPHA
 from ...symbols import ORTH, LEMMA
 
@@ -452,9 +451,6 @@ _regular_exp += [
     for hc in _hyphen_combination
 ]
 
-# URLs
-_regular_exp.append(URL_PATTERN)
-
 
 TOKENIZER_EXCEPTIONS = _exc
 TOKEN_MATCH = re.compile(

spacy/lang/gu/__init__.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from .stop_words import STOP_WORDS
 
 from ...language import Language

spacy/lang/gu/examples.py

@@ -1,7 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-
 """
 Example sentences to test spaCy and its language models.
 

spacy/lang/gu/stop_words.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 STOP_WORDS = set(
     """
 એમ

spacy/lang/hu/punctuation.py

@@ -7,7 +7,6 @@ _concat_icons = CONCAT_ICONS.replace("\u00B0", "")
 
 _currency = r"\$¢£€¥฿"
 _quotes = CONCAT_QUOTES.replace("'", "")
-_units = UNITS.replace("%", "")
 
 _prefixes = (
     LIST_PUNCT
@@ -18,7 +17,8 @@ _prefixes = (
 )
 
 _suffixes = (
-    LIST_PUNCT
+    [r"\+"]
+    + LIST_PUNCT
     + LIST_ELLIPSES
     + LIST_QUOTES
     + [_concat_icons]
@@ -26,7 +26,7 @@ _suffixes = (
         r"(?<=[0-9])\+",
         r"(?<=°[FfCcKk])\.",
         r"(?<=[0-9])(?:[{c}])".format(c=_currency),
-        r"(?<=[0-9])(?:{u})".format(u=_units),
+        r"(?<=[0-9])(?:{u})".format(u=UNITS),
         r"(?<=[{al}{e}{q}(?:{c})])\.".format(
             al=ALPHA_LOWER, e=r"%²\-\+", q=CONCAT_QUOTES, c=_currency
         ),

spacy/lang/hu/tokenizer_exceptions.py

@@ -1,7 +1,6 @@
 import re
 
 from ..punctuation import ALPHA_LOWER, CURRENCY
-from ..tokenizer_exceptions import URL_PATTERN
 from ...symbols import ORTH
 
 
@@ -646,4 +645,4 @@ _nums = r"(({ne})|({t})|({on})|({c}))({s})?".format(
 
 
 TOKENIZER_EXCEPTIONS = _exc
-TOKEN_MATCH = re.compile(r"^({u})|({n})$".format(u=URL_PATTERN, n=_nums)).match
+TOKEN_MATCH = re.compile(r"^{n}$".format(n=_nums)).match

spacy/lang/hy/__init__.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from .stop_words import STOP_WORDS
 from .lex_attrs import LEX_ATTRS
 from .tag_map import TAG_MAP

spacy/lang/hy/examples.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 """
 Example sentences to test spaCy and its language models.
 >>> from spacy.lang.hy.examples import sentences

spacy/lang/hy/lex_attrs.py

@@ -1,12 +1,9 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from ...attrs import LIKE_NUM
 
 
 _num_words = [
-    "զրօ",
+    "զրո",
-    "մէկ",
+    "մեկ",
     "երկու",
     "երեք",
     "չորս",
@@ -28,10 +25,10 @@ _num_words = [
     "քսան" "երեսուն",
     "քառասուն",
     "հիսուն",
-    "վաթցսուն",
+    "վաթսուն",
     "յոթանասուն",
     "ութսուն",
-    "ինիսուն",
+    "իննսուն",
     "հարյուր",
     "հազար",
     "միլիոն",

spacy/lang/hy/stop_words.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 STOP_WORDS = set(
     """
 նա

spacy/lang/hy/tag_map.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from ...symbols import POS, ADJ, NUM, DET, ADV, ADP, X, VERB, NOUN
 from ...symbols import PROPN, PART, INTJ, PRON, SCONJ, AUX, CCONJ
 

spacy/lang/id/syntax_iterators.py

@@ -24,17 +24,15 @@ def noun_chunks(doclike):
     np_deps = [doc.vocab.strings[label] for label in labels]
     conj = doc.vocab.strings.add("conj")
     np_label = doc.vocab.strings.add("NP")
-    seen = set()
+    prev_end = -1
     for i, word in enumerate(doclike):
         if word.pos not in (NOUN, PROPN, PRON):
             continue
         # Prevent nested chunks from being produced
-        if word.i in seen:
+        if word.left_edge.i <= prev_end:
             continue
         if word.dep in np_deps:
-            if any(w.i in seen for w in word.subtree):
+            prev_end = word.right_edge.i
-                continue
-            seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
             yield word.left_edge.i, word.right_edge.i + 1, np_label
         elif word.dep == conj:
             head = word.head
@@ -42,9 +40,7 @@ def noun_chunks(doclike):
                 head = head.head
             # If the head is an NP, and we're coordinated to it, we're an NP
             if head.dep in np_deps:
-                if any(w.i in seen for w in word.subtree):
+                prev_end = word.right_edge.i
-                    continue
-                seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
                 yield word.left_edge.i, word.right_edge.i + 1, np_label
 
 

spacy/lang/ja/__init__.py

@@ -1,111 +1,266 @@
-import re
+import srsly
-from collections import namedtuple
+from collections import namedtuple, OrderedDict
 
 from .stop_words import STOP_WORDS
+from .syntax_iterators import SYNTAX_ITERATORS
 from .tag_map import TAG_MAP
+from .tag_orth_map import TAG_ORTH_MAP
+from .tag_bigram_map import TAG_BIGRAM_MAP
 from ...attrs import LANG
-from ...language import Language
-from ...tokens import Doc
 from ...compat import copy_reg
+from ...errors import Errors
+from ...language import Language
+from ...symbols import POS
+from ...tokens import Doc
 from ...util import DummyTokenizer
+from ... import util
+
+
+# Hold the attributes we need with convenient names
+DetailedToken = namedtuple("DetailedToken", ["surface", "pos", "lemma"])
 
 # Handling for multiple spaces in a row is somewhat awkward, this simplifies
 # the flow by creating a dummy with the same interface.
-DummyNode = namedtuple("DummyNode", ["surface", "pos", "feature"])
+DummyNode = namedtuple("DummyNode", ["surface", "pos", "lemma"])
-DummyNodeFeatures = namedtuple("DummyNodeFeatures", ["lemma"])
+DummySpace = DummyNode(" ", " ", " ")
-DummySpace = DummyNode(" ", " ", DummyNodeFeatures(" "))
 
 
-def try_fugashi_import():
+def try_sudachi_import(split_mode="A"):
-    """Fugashi is required for Japanese support, so check for it.
+    """SudachiPy is required for Japanese support, so check for it.
-    It it's not available blow up and explain how to fix it."""
+    It it's not available blow up and explain how to fix it.
+    split_mode should be one of these values: "A", "B", "C", None->"A"."""
     try:
-        import fugashi
+        from sudachipy import dictionary, tokenizer
 
-        return fugashi
+        split_mode = {
+            None: tokenizer.Tokenizer.SplitMode.A,
+            "A": tokenizer.Tokenizer.SplitMode.A,
+            "B": tokenizer.Tokenizer.SplitMode.B,
+            "C": tokenizer.Tokenizer.SplitMode.C,
+        }[split_mode]
+        tok = dictionary.Dictionary().create(mode=split_mode)
+        return tok
     except ImportError:
         raise ImportError(
-            "Japanese support requires Fugashi: " "https://github.com/polm/fugashi"
+            "Japanese support requires SudachiPy and SudachiDict-core "
+            "(https://github.com/WorksApplications/SudachiPy). "
+            "Install with `pip install sudachipy sudachidict_core` or "
+            "install spaCy with `pip install spacy[ja]`."
         )
 
 
-def resolve_pos(token):
+def resolve_pos(orth, pos, next_pos):
     """If necessary, add a field to the POS tag for UD mapping.
     Under Universal Dependencies, sometimes the same Unidic POS tag can
     be mapped differently depending on the literal token or its context
-    in the sentence. This function adds information to the POS tag to
+    in the sentence. This function returns resolved POSs for both token
-    resolve ambiguous mappings.
+    and next_token by tuple.
     """
 
-    # this is only used for consecutive ascii spaces
+    # Some tokens have their UD tag decided based on the POS of the following
-    if token.surface == " ":
+    # token.
-        return "空白"
 
-    # TODO: This is a first take. The rules here are crude approximations.
+    # orth based rules
-    # For many of these, full dependencies are needed to properly resolve
+    if pos[0] in TAG_ORTH_MAP:
-    # PoS mappings.
+        orth_map = TAG_ORTH_MAP[pos[0]]
-    if token.pos == "連体詞,*,*,*":
+        if orth in orth_map:
-        if re.match(r"[こそあど此其彼]の", token.surface):
+            return orth_map[orth], None
-            return token.pos + ",DET"
+
-        if re.match(r"[こそあど此其彼]", token.surface):
+    # tag bi-gram mapping
-            return token.pos + ",PRON"
+    if next_pos:
-        return token.pos + ",ADJ"
+        tag_bigram = pos[0], next_pos[0]
-    return token.pos
+        if tag_bigram in TAG_BIGRAM_MAP:
+            bipos = TAG_BIGRAM_MAP[tag_bigram]
+            if bipos[0] is None:
+                return TAG_MAP[pos[0]][POS], bipos[1]
+            else:
+                return bipos
+
+    return TAG_MAP[pos[0]][POS], None
 
 
-def get_words_and_spaces(tokenizer, text):
+# Use a mapping of paired punctuation to avoid splitting quoted sentences.
-    """Get the individual tokens that make up the sentence and handle white space.
+pairpunct = {"「": "」", "『": "』", "【": "】"}
 
-    Japanese doesn't usually use white space, and MeCab's handling of it for
+
-    multiple spaces in a row is somewhat awkward.
+def separate_sentences(doc):
+    """Given a doc, mark tokens that start sentences based on Unidic tags.
     """
 
-    tokens = tokenizer.parseToNodeList(text)
+    stack = []  # save paired punctuation
 
+    for i, token in enumerate(doc[:-2]):
+        # Set all tokens after the first to false by default. This is necessary
+        # for the doc code to be aware we've done sentencization, see
+        # `is_sentenced`.
+        token.sent_start = i == 0
+        if token.tag_:
+            if token.tag_ == "補助記号-括弧開":
+                ts = str(token)
+                if ts in pairpunct:
+                    stack.append(pairpunct[ts])
+                elif stack and ts == stack[-1]:
+                    stack.pop()
+
+            if token.tag_ == "補助記号-句点":
+                next_token = doc[i + 1]
+                if next_token.tag_ != token.tag_ and not stack:
+                    next_token.sent_start = True
+
+
+def get_dtokens(tokenizer, text):
+    tokens = tokenizer.tokenize(text)
     words = []
-    spaces = []
+    for ti, token in enumerate(tokens):
-    for token in tokens:
+        tag = "-".join([xx for xx in token.part_of_speech()[:4] if xx != "*"])
-        # If there's more than one space, spaces after the first become tokens
+        inf = "-".join([xx for xx in token.part_of_speech()[4:] if xx != "*"])
-        for ii in range(len(token.white_space) - 1):
+        dtoken = DetailedToken(token.surface(), (tag, inf), token.dictionary_form())
-            words.append(DummySpace)
+        if ti > 0 and words[-1].pos[0] == "空白" and tag == "空白":
-            spaces.append(False)
+            # don't add multiple space tokens in a row
+            continue
+        words.append(dtoken)
 
-        words.append(token)
+    # remove empty tokens. These can be produced with characters like … that
-        spaces.append(bool(token.white_space))
+    # Sudachi normalizes internally.
-    return words, spaces
+    words = [ww for ww in words if len(ww.surface) > 0]
+    return words
+
+
+def get_words_lemmas_tags_spaces(dtokens, text, gap_tag=("空白", "")):
+    words = [x.surface for x in dtokens]
+    if "".join("".join(words).split()) != "".join(text.split()):
+        raise ValueError(Errors.E194.format(text=text, words=words))
+    text_words = []
+    text_lemmas = []
+    text_tags = []
+    text_spaces = []
+    text_pos = 0
+    # handle empty and whitespace-only texts
+    if len(words) == 0:
+        return text_words, text_lemmas, text_tags, text_spaces
+    elif len([word for word in words if not word.isspace()]) == 0:
+        assert text.isspace()
+        text_words = [text]
+        text_lemmas = [text]
+        text_tags = [gap_tag]
+        text_spaces = [False]
+        return text_words, text_lemmas, text_tags, text_spaces
+    # normalize words to remove all whitespace tokens
+    norm_words, norm_dtokens = zip(
+        *[
+            (word, dtokens)
+            for word, dtokens in zip(words, dtokens)
+            if not word.isspace()
+        ]
+    )
+    # align words with text
+    for word, dtoken in zip(norm_words, norm_dtokens):
+        try:
+            word_start = text[text_pos:].index(word)
+        except ValueError:
+            raise ValueError(Errors.E194.format(text=text, words=words))
+        if word_start > 0:
+            w = text[text_pos : text_pos + word_start]
+            text_words.append(w)
+            text_lemmas.append(w)
+            text_tags.append(gap_tag)
+            text_spaces.append(False)
+            text_pos += word_start
+        text_words.append(word)
+        text_lemmas.append(dtoken.lemma)
+        text_tags.append(dtoken.pos)
+        text_spaces.append(False)
+        text_pos += len(word)
+        if text_pos < len(text) and text[text_pos] == " ":
+            text_spaces[-1] = True
+            text_pos += 1
+    if text_pos < len(text):
+        w = text[text_pos:]
+        text_words.append(w)
+        text_lemmas.append(w)
+        text_tags.append(gap_tag)
+        text_spaces.append(False)
+    return text_words, text_lemmas, text_tags, text_spaces
 
 
 class JapaneseTokenizer(DummyTokenizer):
-    def __init__(self, cls, nlp=None):
+    def __init__(self, cls, nlp=None, config={}):
         self.vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
-        self.tokenizer = try_fugashi_import().Tagger()
+        self.split_mode = config.get("split_mode", None)
-        self.tokenizer.parseToNodeList("")  # see #2901
+        self.tokenizer = try_sudachi_import(self.split_mode)
 
     def __call__(self, text):
-        dtokens, spaces = get_words_and_spaces(self.tokenizer, text)
+        dtokens = get_dtokens(self.tokenizer, text)
-        words = [x.surface for x in dtokens]
+
+        words, lemmas, unidic_tags, spaces = get_words_lemmas_tags_spaces(dtokens, text)
         doc = Doc(self.vocab, words=words, spaces=spaces)
-        unidic_tags = []
+        next_pos = None
-        for token, dtoken in zip(doc, dtokens):
+        for idx, (token, lemma, unidic_tag) in enumerate(zip(doc, lemmas, unidic_tags)):
-            unidic_tags.append(dtoken.pos)
+            token.tag_ = unidic_tag[0]
-            token.tag_ = resolve_pos(dtoken)
+            if next_pos:
+                token.pos = next_pos
+                next_pos = None
+            else:
+                token.pos, next_pos = resolve_pos(
+                    token.orth_,
+                    unidic_tag,
+                    unidic_tags[idx + 1] if idx + 1 < len(unidic_tags) else None,
+                )
 
             # if there's no lemma info (it's an unk) just use the surface
-            token.lemma_ = dtoken.feature.lemma or dtoken.surface
+            token.lemma_ = lemma
         doc.user_data["unidic_tags"] = unidic_tags
+
         return doc
 
+    def _get_config(self):
+        config = OrderedDict((("split_mode", self.split_mode),))
+        return config
+
+    def _set_config(self, config={}):
+        self.split_mode = config.get("split_mode", None)
+
+    def to_bytes(self, **kwargs):
+        serializers = OrderedDict(
+            (("cfg", lambda: srsly.json_dumps(self._get_config())),)
+        )
+        return util.to_bytes(serializers, [])
+
+    def from_bytes(self, data, **kwargs):
+        deserializers = OrderedDict(
+            (("cfg", lambda b: self._set_config(srsly.json_loads(b))),)
+        )
+        util.from_bytes(data, deserializers, [])
+        self.tokenizer = try_sudachi_import(self.split_mode)
+        return self
+
+    def to_disk(self, path, **kwargs):
+        path = util.ensure_path(path)
+        serializers = OrderedDict(
+            (("cfg", lambda p: srsly.write_json(p, self._get_config())),)
+        )
+        return util.to_disk(path, serializers, [])
+
+    def from_disk(self, path, **kwargs):
+        path = util.ensure_path(path)
+        serializers = OrderedDict(
+            (("cfg", lambda p: self._set_config(srsly.read_json(p))),)
+        )
+        util.from_disk(path, serializers, [])
+        self.tokenizer = try_sudachi_import(self.split_mode)
+
 
 class JapaneseDefaults(Language.Defaults):
     lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
     lex_attr_getters[LANG] = lambda _text: "ja"
     stop_words = STOP_WORDS
     tag_map = TAG_MAP
+    syntax_iterators = SYNTAX_ITERATORS
     writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
 
     @classmethod
-    def create_tokenizer(cls, nlp=None):
+    def create_tokenizer(cls, nlp=None, config={}):
-        return JapaneseTokenizer(cls, nlp)
+        return JapaneseTokenizer(cls, nlp, config)
 
 
 class Japanese(Language):

spacy/lang/ja/bunsetu.py

@@ -0,0 +1,176 @@
+POS_PHRASE_MAP = {
+    "NOUN": "NP",
+    "NUM": "NP",
+    "PRON": "NP",
+    "PROPN": "NP",
+    "VERB": "VP",
+    "ADJ": "ADJP",
+    "ADV": "ADVP",
+    "CCONJ": "CCONJP",
+}
+
+
+# return value: [(bunsetu_tokens, phrase_type={'NP', 'VP', 'ADJP', 'ADVP'}, phrase_tokens)]
+def yield_bunsetu(doc, debug=False):
+    bunsetu = []
+    bunsetu_may_end = False
+    phrase_type = None
+    phrase = None
+    prev = None
+    prev_tag = None
+    prev_dep = None
+    prev_head = None
+    for t in doc:
+        pos = t.pos_
+        pos_type = POS_PHRASE_MAP.get(pos, None)
+        tag = t.tag_
+        dep = t.dep_
+        head = t.head.i
+        if debug:
+            print(
+                t.i,
+                t.orth_,
+                pos,
+                pos_type,
+                dep,
+                head,
+                bunsetu_may_end,
+                phrase_type,
+                phrase,
+                bunsetu,
+            )
+
+        # DET is always an individual bunsetu
+        if pos == "DET":
+            if bunsetu:
+                yield bunsetu, phrase_type, phrase
+            yield [t], None, None
+            bunsetu = []
+            bunsetu_may_end = False
+            phrase_type = None
+            phrase = None
+
+        # PRON or Open PUNCT always splits bunsetu
+        elif tag == "補助記号-括弧開":
+            if bunsetu:
+                yield bunsetu, phrase_type, phrase
+            bunsetu = [t]
+            bunsetu_may_end = True
+            phrase_type = None
+            phrase = None
+
+        # bunsetu head not appeared
+        elif phrase_type is None:
+            if bunsetu and prev_tag == "補助記号-読点":
+                yield bunsetu, phrase_type, phrase
+                bunsetu = []
+                bunsetu_may_end = False
+                phrase_type = None
+                phrase = None
+            bunsetu.append(t)
+            if pos_type:  # begin phrase
+                phrase = [t]
+                phrase_type = pos_type
+                if pos_type in {"ADVP", "CCONJP"}:
+                    bunsetu_may_end = True
+
+        # entering new bunsetu
+        elif pos_type and (
+            pos_type != phrase_type
+            or bunsetu_may_end  # different phrase type arises  # same phrase type but bunsetu already ended
+        ):
+            # exceptional case: NOUN to VERB
+            if (
+                phrase_type == "NP"
+                and pos_type == "VP"
+                and prev_dep == "compound"
+                and prev_head == t.i
+            ):
+                bunsetu.append(t)
+                phrase_type = "VP"
+                phrase.append(t)
+            # exceptional case: VERB to NOUN
+            elif (
+                phrase_type == "VP"
+                and pos_type == "NP"
+                and (
+                    prev_dep == "compound"
+                    and prev_head == t.i
+                    or dep == "compound"
+                    and prev == head
+                    or prev_dep == "nmod"
+                    and prev_head == t.i
+                )
+            ):
+                bunsetu.append(t)
+                phrase_type = "NP"
+                phrase.append(t)
+            else:
+                yield bunsetu, phrase_type, phrase
+                bunsetu = [t]
+                bunsetu_may_end = False
+                phrase_type = pos_type
+                phrase = [t]
+
+        # NOUN bunsetu
+        elif phrase_type == "NP":
+            bunsetu.append(t)
+            if not bunsetu_may_end and (
+                (
+                    (pos_type == "NP" or pos == "SYM")
+                    and (prev_head == t.i or prev_head == head)
+                    and prev_dep in {"compound", "nummod"}
+                )
+                or (
+                    pos == "PART"
+                    and (prev == head or prev_head == head)
+                    and dep == "mark"
+                )
+            ):
+                phrase.append(t)
+            else:
+                bunsetu_may_end = True
+
+        # VERB bunsetu
+        elif phrase_type == "VP":
+            bunsetu.append(t)
+            if (
+                not bunsetu_may_end
+                and pos == "VERB"
+                and prev_head == t.i
+                and prev_dep == "compound"
+            ):
+                phrase.append(t)
+            else:
+                bunsetu_may_end = True
+
+        # ADJ bunsetu
+        elif phrase_type == "ADJP" and tag != "連体詞":
+            bunsetu.append(t)
+            if not bunsetu_may_end and (
+                (
+                    pos == "NOUN"
+                    and (prev_head == t.i or prev_head == head)
+                    and prev_dep in {"amod", "compound"}
+                )
+                or (
+                    pos == "PART"
+                    and (prev == head or prev_head == head)
+                    and dep == "mark"
+                )
+            ):
+                phrase.append(t)
+            else:
+                bunsetu_may_end = True
+
+        # other bunsetu
+        else:
+            bunsetu.append(t)
+
+        prev = t.i
+        prev_tag = t.tag_
+        prev_dep = t.dep_
+        prev_head = head
+
+    if bunsetu:
+        yield bunsetu, phrase_type, phrase

spacy/lang/ja/syntax_iterators.py

@@ -0,0 +1,54 @@
+from ...symbols import NOUN, PROPN, PRON, VERB
+
+# XXX this can probably be pruned a bit
+labels = [
+    "nsubj",
+    "nmod",
+    "dobj",
+    "nsubjpass",
+    "pcomp",
+    "pobj",
+    "obj",
+    "obl",
+    "dative",
+    "appos",
+    "attr",
+    "ROOT",
+]
+
+
+def noun_chunks(obj):
+    """
+    Detect base noun phrases from a dependency parse. Works on both Doc and Span.
+    """
+
+    doc = obj.doc  # Ensure works on both Doc and Span.
+    np_deps = [doc.vocab.strings.add(label) for label in labels]
+    doc.vocab.strings.add("conj")
+    np_label = doc.vocab.strings.add("NP")
+    seen = set()
+    for i, word in enumerate(obj):
+        if word.pos not in (NOUN, PROPN, PRON):
+            continue
+        # Prevent nested chunks from being produced
+        if word.i in seen:
+            continue
+        if word.dep in np_deps:
+            unseen = [w.i for w in word.subtree if w.i not in seen]
+            if not unseen:
+                continue
+
+            # this takes care of particles etc.
+            seen.update(j.i for j in word.subtree)
+            # This avoids duplicating embedded clauses
+            seen.update(range(word.i + 1))
+
+            # if the head of this is a verb, mark that and rights seen
+            # Don't do the subtree as that can hide other phrases
+            if word.head.pos == VERB:
+                seen.add(word.head.i)
+                seen.update(w.i for w in word.head.rights)
+            yield unseen[0], word.i + 1, np_label
+
+
+SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

spacy/lang/ja/tag_bigram_map.py

@@ -0,0 +1,28 @@
+from ...symbols import ADJ, AUX, NOUN, PART, VERB
+
+# mapping from tag bi-gram to pos of previous token
+TAG_BIGRAM_MAP = {
+    # This covers only small part of AUX.
+    ("形容詞-非自立可能", "助詞-終助詞"): (AUX, None),
+    ("名詞-普通名詞-形状詞可能", "助動詞"): (ADJ, None),
+    # ("副詞", "名詞-普通名詞-形状詞可能"): (None, ADJ),
+    # This covers acl, advcl, obl and root, but has side effect for compound.
+    ("名詞-普通名詞-サ変可能", "動詞-非自立可能"): (VERB, AUX),
+    # This covers almost all of the deps
+    ("名詞-普通名詞-サ変形状詞可能", "動詞-非自立可能"): (VERB, AUX),
+    ("名詞-普通名詞-副詞可能", "動詞-非自立可能"): (None, VERB),
+    ("副詞", "動詞-非自立可能"): (None, VERB),
+    ("形容詞-一般", "動詞-非自立可能"): (None, VERB),
+    ("形容詞-非自立可能", "動詞-非自立可能"): (None, VERB),
+    ("接頭辞", "動詞-非自立可能"): (None, VERB),
+    ("助詞-係助詞", "動詞-非自立可能"): (None, VERB),
+    ("助詞-副助詞", "動詞-非自立可能"): (None, VERB),
+    ("助詞-格助詞", "動詞-非自立可能"): (None, VERB),
+    ("補助記号-読点", "動詞-非自立可能"): (None, VERB),
+    ("形容詞-一般", "接尾辞-名詞的-一般"): (None, PART),
+    ("助詞-格助詞", "形状詞-助動詞語幹"): (None, NOUN),
+    ("連体詞", "形状詞-助動詞語幹"): (None, NOUN),
+    ("動詞-一般", "助詞-副助詞"): (None, PART),
+    ("動詞-非自立可能", "助詞-副助詞"): (None, PART),
+    ("助動詞", "助詞-副助詞"): (None, PART),
+}

spacy/lang/ja/tag_map.py

@@ -1,79 +1,68 @@
-from ...symbols import POS, PUNCT, INTJ, X, ADJ, AUX, ADP, PART, SCONJ, NOUN
+from ...symbols import POS, PUNCT, INTJ, ADJ, AUX, ADP, PART, SCONJ, NOUN
-from ...symbols import SYM, PRON, VERB, ADV, PROPN, NUM, DET, SPACE
+from ...symbols import SYM, PRON, VERB, ADV, PROPN, NUM, DET, SPACE, CCONJ
 
 
 TAG_MAP = {
     # Explanation of Unidic tags:
     # https://www.gavo.t.u-tokyo.ac.jp/~mine/japanese/nlp+slp/UNIDIC_manual.pdf
-    # Universal Dependencies Mapping:
+    # Universal Dependencies Mapping: (Some of the entries in this mapping are updated to v2.6 in the list below)
     # http://universaldependencies.org/ja/overview/morphology.html
     # http://universaldependencies.org/ja/pos/all.html
-    "記号,一般,*,*": {
+    "記号-一般": {POS: NOUN},  # this includes characters used to represent sounds like ドレミ
-        POS: PUNCT
+    "記号-文字": {
-    },  # this includes characters used to represent sounds like ドレミ
+        POS: NOUN
-    "記号,文字,*,*": {
+    },  # this is for Greek and Latin characters having some meanings, or used as symbols, as in math
-        POS: PUNCT
+    "感動詞-フィラー": {POS: INTJ},
-    },  # this is for Greek and Latin characters used as sumbols, as in math
+    "感動詞-一般": {POS: INTJ},
-    "感動詞,フィラー,*,*": {POS: INTJ},
-    "感動詞,一般,*,*": {POS: INTJ},
-    # this is specifically for unicode full-width space
-    "空白,*,*,*": {POS: X},
-    # This is used when sequential half-width spaces are present
     "空白": {POS: SPACE},
-    "形状詞,一般,*,*": {POS: ADJ},
+    "形状詞-一般": {POS: ADJ},
-    "形状詞,タリ,*,*": {POS: ADJ},
+    "形状詞-タリ": {POS: ADJ},
-    "形状詞,助動詞語幹,*,*": {POS: ADJ},
+    "形状詞-助動詞語幹": {POS: AUX},
-    "形容詞,一般,*,*": {POS: ADJ},
+    "形容詞-一般": {POS: ADJ},
-    "形容詞,非自立可能,*,*": {POS: AUX},  # XXX ADJ if alone, AUX otherwise
+    "形容詞-非自立可能": {POS: ADJ},  # XXX ADJ if alone, AUX otherwise
-    "助詞,格助詞,*,*": {POS: ADP},
+    "助詞-格助詞": {POS: ADP},
-    "助詞,係助詞,*,*": {POS: ADP},
+    "助詞-係助詞": {POS: ADP},
-    "助詞,終助詞,*,*": {POS: PART},
+    "助詞-終助詞": {POS: PART},
-    "助詞,準体助詞,*,*": {POS: SCONJ},  # の as in 走るのが速い
+    "助詞-準体助詞": {POS: SCONJ},  # の as in 走るのが速い
-    "助詞,接続助詞,*,*": {POS: SCONJ},  # verb ending て
+    "助詞-接続助詞": {POS: SCONJ},  # verb ending て0
-    "助詞,副助詞,*,*": {POS: PART},  # ばかり, つつ after a verb
+    "助詞-副助詞": {POS: ADP},  # ばかり, つつ after a verb
-    "助動詞,*,*,*": {POS: AUX},
+    "助動詞": {POS: AUX},
-    "接続詞,*,*,*": {POS: SCONJ},  # XXX: might need refinement
+    "接続詞": {POS: CCONJ},  # XXX: might need refinement
-    "接頭辞,*,*,*": {POS: NOUN},
+    "接頭辞": {POS: NOUN},
-    "接尾辞,形状詞的,*,*": {POS: ADJ},  # がち, チック
+    "接尾辞-形状詞的": {POS: PART},  # がち, チック
-    "接尾辞,形容詞的,*,*": {POS: ADJ},  # -らしい
+    "接尾辞-形容詞的": {POS: AUX},  # -らしい
-    "接尾辞,動詞的,*,*": {POS: NOUN},  # -じみ
+    "接尾辞-動詞的": {POS: PART},  # -じみ
-    "接尾辞,名詞的,サ変可能,*": {POS: NOUN},  # XXX see 名詞,普通名詞,サ変可能,*
+    "接尾辞-名詞的-サ変可能": {POS: NOUN},  # XXX see 名詞,普通名詞,サ変可能,*
-    "接尾辞,名詞的,一般,*": {POS: NOUN},
+    "接尾辞-名詞的-一般": {POS: NOUN},
-    "接尾辞,名詞的,助数詞,*": {POS: NOUN},
+    "接尾辞-名詞的-助数詞": {POS: NOUN},
-    "接尾辞,名詞的,副詞可能,*": {POS: NOUN},  # -後, -過ぎ
+    "接尾辞-名詞的-副詞可能": {POS: NOUN},  # -後, -過ぎ
-    "代名詞,*,*,*": {POS: PRON},
+    "代名詞": {POS: PRON},
-    "動詞,一般,*,*": {POS: VERB},
+    "動詞-一般": {POS: VERB},
-    "動詞,非自立可能,*,*": {POS: VERB},  # XXX VERB if alone, AUX otherwise
+    "動詞-非自立可能": {POS: AUX},  # XXX VERB if alone, AUX otherwise
-    "動詞,非自立可能,*,*,AUX": {POS: AUX},
+    "副詞": {POS: ADV},
-    "動詞,非自立可能,*,*,VERB": {POS: VERB},
+    "補助記号-ＡＡ-一般": {POS: SYM},  # text art
-    "副詞,*,*,*": {POS: ADV},
+    "補助記号-ＡＡ-顔文字": {POS: PUNCT},  # kaomoji
-    "補助記号,ＡＡ,一般,*": {POS: SYM},  # text art
+    "補助記号-一般": {POS: SYM},
-    "補助記号,ＡＡ,顔文字,*": {POS: SYM},  # kaomoji
+    "補助記号-括弧開": {POS: PUNCT},  # open bracket
-    "補助記号,一般,*,*": {POS: SYM},
+    "補助記号-括弧閉": {POS: PUNCT},  # close bracket
-    "補助記号,括弧開,*,*": {POS: PUNCT},  # open bracket
+    "補助記号-句点": {POS: PUNCT},  # period or other EOS marker
-    "補助記号,括弧閉,*,*": {POS: PUNCT},  # close bracket
+    "補助記号-読点": {POS: PUNCT},  # comma
-    "補助記号,句点,*,*": {POS: PUNCT},  # period or other EOS marker
+    "名詞-固有名詞-一般": {POS: PROPN},  # general proper noun
-    "補助記号,読点,*,*": {POS: PUNCT},  # comma
+    "名詞-固有名詞-人名-一般": {POS: PROPN},  # person's name
-    "名詞,固有名詞,一般,*": {POS: PROPN},  # general proper noun
+    "名詞-固有名詞-人名-姓": {POS: PROPN},  # surname
-    "名詞,固有名詞,人名,一般": {POS: PROPN},  # person's name
+    "名詞-固有名詞-人名-名": {POS: PROPN},  # first name
-    "名詞,固有名詞,人名,姓": {POS: PROPN},  # surname
+    "名詞-固有名詞-地名-一般": {POS: PROPN},  # place name
-    "名詞,固有名詞,人名,名": {POS: PROPN},  # first name
+    "名詞-固有名詞-地名-国": {POS: PROPN},  # country name
-    "名詞,固有名詞,地名,一般": {POS: PROPN},  # place name
+    "名詞-助動詞語幹": {POS: AUX},
-    "名詞,固有名詞,地名,国": {POS: PROPN},  # country name
+    "名詞-数詞": {POS: NUM},  # includes Chinese numerals
-    "名詞,助動詞語幹,*,*": {POS: AUX},
+    "名詞-普通名詞-サ変可能": {POS: NOUN},  # XXX: sometimes VERB in UDv2; suru-verb noun
-    "名詞,数詞,*,*": {POS: NUM},  # includes Chinese numerals
+    "名詞-普通名詞-サ変形状詞可能": {POS: NOUN},
-    "名詞,普通名詞,サ変可能,*": {POS: NOUN},  # XXX: sometimes VERB in UDv2; suru-verb noun
+    "名詞-普通名詞-一般": {POS: NOUN},
-    "名詞,普通名詞,サ変可能,*,NOUN": {POS: NOUN},
+    "名詞-普通名詞-形状詞可能": {POS: NOUN},  # XXX: sometimes ADJ in UDv2
-    "名詞,普通名詞,サ変可能,*,VERB": {POS: VERB},
+    "名詞-普通名詞-助数詞可能": {POS: NOUN},  # counter / unit
-    "名詞,普通名詞,サ変形状詞可能,*": {POS: NOUN},  # ex: 下手
+    "名詞-普通名詞-副詞可能": {POS: NOUN},
-    "名詞,普通名詞,一般,*": {POS: NOUN},
+    "連体詞": {POS: DET},  # XXX this has exceptions based on literal token
-    "名詞,普通名詞,形状詞可能,*": {POS: NOUN},  # XXX: sometimes ADJ in UDv2
+    # GSD tags. These aren't in Unidic, but we need them for the GSD data.
-    "名詞,普通名詞,形状詞可能,*,NOUN": {POS: NOUN},
+    "外国語": {POS: PROPN},  # Foreign words
-    "名詞,普通名詞,形状詞可能,*,ADJ": {POS: ADJ},
+    "絵文字・記号等": {POS: SYM},  # emoji / kaomoji ^^;
-    "名詞,普通名詞,助数詞可能,*": {POS: NOUN},  # counter / unit
-    "名詞,普通名詞,副詞可能,*": {POS: NOUN},
-    "連体詞,*,*,*": {POS: ADJ},  # XXX this has exceptions based on literal token
-    "連体詞,*,*,*,ADJ": {POS: ADJ},
-    "連体詞,*,*,*,PRON": {POS: PRON},
-    "連体詞,*,*,*,DET": {POS: DET},
 }

spacy/lang/ja/tag_orth_map.py

@@ -0,0 +1,22 @@
+from ...symbols import DET, PART, PRON, SPACE, X
+
+# mapping from tag bi-gram to pos of previous token
+TAG_ORTH_MAP = {
+    "空白": {" ": SPACE, "　": X},
+    "助詞-副助詞": {"たり": PART},
+    "連体詞": {
+        "あの": DET,
+        "かの": DET,
+        "この": DET,
+        "その": DET,
+        "どの": DET,
+        "彼の": DET,
+        "此の": DET,
+        "其の": DET,
+        "ある": PRON,
+        "こんな": PRON,
+        "そんな": PRON,
+        "どんな": PRON,
+        "あらゆる": PRON,
+    },
+}

spacy/lang/kn/examples.py

@@ -1,7 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-
 """
 Example sentences to test spaCy and its language models.
 

spacy/lang/ml/__init__.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from .stop_words import STOP_WORDS
 
 from ...language import Language

spacy/lang/ml/examples.py

@@ -1,7 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-
 """
 Example sentences to test spaCy and its language models.
 

spacy/lang/ml/lex_attrs.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from ...attrs import LIKE_NUM
 
 

spacy/lang/ml/stop_words.py

@@ -1,7 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-
 STOP_WORDS = set(
     """
 അത്

spacy/lang/nb/syntax_iterators.py

@@ -24,17 +24,15 @@ def noun_chunks(doclike):
     np_deps = [doc.vocab.strings[label] for label in labels]
     conj = doc.vocab.strings.add("conj")
     np_label = doc.vocab.strings.add("NP")
-    seen = set()
+    prev_end = -1
     for i, word in enumerate(doclike):
         if word.pos not in (NOUN, PROPN, PRON):
             continue
         # Prevent nested chunks from being produced
-        if word.i in seen:
+        if word.left_edge.i <= prev_end:
             continue
         if word.dep in np_deps:
-            if any(w.i in seen for w in word.subtree):
+            prev_end = word.right_edge.i
-                continue
-            seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
             yield word.left_edge.i, word.right_edge.i + 1, np_label
         elif word.dep == conj:
             head = word.head
@@ -42,9 +40,7 @@ def noun_chunks(doclike):
                 head = head.head
             # If the head is an NP, and we're coordinated to it, we're an NP
             if head.dep in np_deps:
-                if any(w.i in seen for w in word.subtree):
+                prev_end = word.right_edge.i
-                    continue
-                seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
                 yield word.left_edge.i, word.right_edge.i + 1, np_label
 
 

spacy/lang/pl/lemmatizer.py

@@ -1,103 +1,75 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
 from ...lemmatizer import Lemmatizer
 from ...parts_of_speech import NAMES
 
 
 class PolishLemmatizer(Lemmatizer):
-    # This lemmatizer implements lookup lemmatization based on
+    # This lemmatizer implements lookup lemmatization based on the Morfeusz
-    # the Morfeusz dictionary (morfeusz.sgjp.pl/en) by Institute of Computer Science PAS
+    # dictionary (morfeusz.sgjp.pl/en) by Institute of Computer Science PAS.
-    # It utilizes some prefix based improvements for
+    # It utilizes some prefix based improvements for verb and adjectives
-    # verb and adjectives lemmatization, as well as case-sensitive
+    # lemmatization, as well as case-sensitive lemmatization for nouns.
-    # lemmatization for nouns
-    def __init__(self, lookups, *args, **kwargs):
-        # this lemmatizer is lookup based, so it does not require an index, exceptionlist, or rules
-        super().__init__(lookups)
-        self.lemma_lookups = {}
-        for tag in [
-            "ADJ",
-            "ADP",
-            "ADV",
-            "AUX",
-            "NOUN",
-            "NUM",
-            "PART",
-            "PRON",
-            "VERB",
-            "X",
-        ]:
-            self.lemma_lookups[tag] = self.lookups.get_table(
-                "lemma_lookup_" + tag.lower(), {}
-            )
-        self.lemma_lookups["DET"] = self.lemma_lookups["X"]
-        self.lemma_lookups["PROPN"] = self.lemma_lookups["NOUN"]
-
     def __call__(self, string, univ_pos, morphology=None):
         if isinstance(univ_pos, int):
             univ_pos = NAMES.get(univ_pos, "X")
         univ_pos = univ_pos.upper()
 
+        lookup_pos = univ_pos.lower()
+        if univ_pos == "PROPN":
+            lookup_pos = "noun"
+        lookup_table = self.lookups.get_table("lemma_lookup_" + lookup_pos, {})
+
         if univ_pos == "NOUN":
-            return self.lemmatize_noun(string, morphology)
+            return self.lemmatize_noun(string, morphology, lookup_table)
 
         if univ_pos != "PROPN":
             string = string.lower()
 
         if univ_pos == "ADJ":
-            return self.lemmatize_adj(string, morphology)
+            return self.lemmatize_adj(string, morphology, lookup_table)
         elif univ_pos == "VERB":
-            return self.lemmatize_verb(string, morphology)
+            return self.lemmatize_verb(string, morphology, lookup_table)
 
-        lemma_dict = self.lemma_lookups.get(univ_pos, {})
+        return [lookup_table.get(string, string.lower())]
-        return [lemma_dict.get(string, string.lower())]
 
-    def lemmatize_adj(self, string, morphology):
+    def lemmatize_adj(self, string, morphology, lookup_table):
         # this method utilizes different procedures for adjectives
         # with 'nie' and 'naj' prefixes
-        lemma_dict = self.lemma_lookups["ADJ"]
-
         if string[:3] == "nie":
             search_string = string[3:]
             if search_string[:3] == "naj":
                 naj_search_string = search_string[3:]
-                if naj_search_string in lemma_dict:
+                if naj_search_string in lookup_table:
-                    return [lemma_dict[naj_search_string]]
+                    return [lookup_table[naj_search_string]]
-            if search_string in lemma_dict:
+            if search_string in lookup_table:
-                return [lemma_dict[search_string]]
+                return [lookup_table[search_string]]
 
         if string[:3] == "naj":
             naj_search_string = string[3:]
-            if naj_search_string in lemma_dict:
+            if naj_search_string in lookup_table:
-                return [lemma_dict[naj_search_string]]
+                return [lookup_table[naj_search_string]]
 
-        return [lemma_dict.get(string, string)]
+        return [lookup_table.get(string, string)]
 
-    def lemmatize_verb(self, string, morphology):
+    def lemmatize_verb(self, string, morphology, lookup_table):
         # this method utilizes a different procedure for verbs
         # with 'nie' prefix
-        lemma_dict = self.lemma_lookups["VERB"]
-
         if string[:3] == "nie":
             search_string = string[3:]
-            if search_string in lemma_dict:
+            if search_string in lookup_table:
-                return [lemma_dict[search_string]]
+                return [lookup_table[search_string]]
 
-        return [lemma_dict.get(string, string)]
+        return [lookup_table.get(string, string)]
 
-    def lemmatize_noun(self, string, morphology):
+    def lemmatize_noun(self, string, morphology, lookup_table):
         # this method is case-sensitive, in order to work
         # for incorrectly tagged proper names
-        lemma_dict = self.lemma_lookups["NOUN"]
-
         if string != string.lower():
-            if string.lower() in lemma_dict:
+            if string.lower() in lookup_table:
-                return [lemma_dict[string.lower()]]
+                return [lookup_table[string.lower()]]
-            elif string in lemma_dict:
+            elif string in lookup_table:
-                return [lemma_dict[string]]
+                return [lookup_table[string]]
             return [string.lower()]
 
-        return [lemma_dict.get(string, string)]
+        return [lookup_table.get(string, string)]
 
     def lookup(self, string, orth=None):
         return string.lower()

spacy/lang/sv/lex_attrs.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from ...attrs import LIKE_NUM
 
 

spacy/lang/sv/syntax_iterators.py

@@ -25,17 +25,15 @@ def noun_chunks(doclike):
     np_deps = [doc.vocab.strings[label] for label in labels]
     conj = doc.vocab.strings.add("conj")
     np_label = doc.vocab.strings.add("NP")
-    seen = set()
+    prev_end = -1
     for i, word in enumerate(doclike):
         if word.pos not in (NOUN, PROPN, PRON):
             continue
         # Prevent nested chunks from being produced
-        if word.i in seen:
+        if word.left_edge.i <= prev_end:
             continue
         if word.dep in np_deps:
-            if any(w.i in seen for w in word.subtree):
+            prev_end = word.right_edge.i
-                continue
-            seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
             yield word.left_edge.i, word.right_edge.i + 1, np_label
         elif word.dep == conj:
             head = word.head
@@ -43,9 +41,7 @@ def noun_chunks(doclike):
                 head = head.head
             # If the head is an NP, and we're coordinated to it, we're an NP
             if head.dep in np_deps:
-                if any(w.i in seen for w in word.subtree):
+                prev_end = word.right_edge.i
-                    continue
-                seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
                 yield word.left_edge.i, word.right_edge.i + 1, np_label
 
 

spacy/lang/ta/examples.py

@@ -14,4 +14,9 @@ sentences = [
     "இந்த ஃபோனுடன் சுமார் ரூ.2,990 மதிப்புள்ள போட் ராக்கர்ஸ் நிறுவனத்தின் ஸ்போர்ட் புளூடூத் ஹெட்போன்ஸ்  இலவசமாக வழங்கப்படவுள்ளது.",
     "மட்டக்களப்பில் பல இடங்களில் வீட்டுத் திட்டங்களுக்கு இன்று அடிக்கல் நாட்டல்",
     "ஐ போன்க்கு முகத்தை வைத்து அன்லாக் செய்யும் முறை மற்றும்  விரலால் தொட்டு அன்லாக் செய்யும் முறையை வாட்ஸ் ஆப் நிறுவனம் இதற்கு முன் கண்டுபிடித்தது",
+    "இது ஒரு வாக்கியம்.",
+    "ஆப்பிள் நிறுவனம் யு.கே. தொடக்க நிறுவனத்தை ஒரு லட்சம் கோடிக்கு வாங்கப் பார்க்கிறது",
+    "தன்னாட்சி கார்கள் காப்பீட்டு பொறுப்பை உற்பத்தியாளரிடம் மாற்றுகின்றன",
+    "நடைபாதை விநியோக ரோபோக்களை தடை செய்வதை சான் பிரான்சிஸ்கோ கருதுகிறது",
+    "லண்டன் ஐக்கிய இராச்சியத்தில் ஒரு பெரிய நகரம்.",
 ]

spacy/lang/tokenizer_exceptions.py

@@ -55,7 +55,8 @@ URL_PATTERN = (
     # fmt: on
 ).strip()
 
-TOKEN_MATCH = re.compile("(?u)" + URL_PATTERN).match
+TOKEN_MATCH = None
+URL_MATCH = re.compile("(?u)" + URL_PATTERN).match
 
 
 BASE_EXCEPTIONS = {}

spacy/lang/zh/tag_map.py

@@ -1,5 +1,5 @@
 from ...symbols import POS, PUNCT, ADJ, SCONJ, CCONJ, NUM, DET, ADV, ADP, X
-from ...symbols import NOUN, PART, INTJ, PRON, VERB, SPACE
+from ...symbols import NOUN, PART, INTJ, PRON, VERB, SPACE, PROPN
 
 # The Chinese part-of-speech tagger uses the OntoNotes 5 version of the Penn
 # Treebank tag set. We also map the tags to the simpler Universal Dependencies
@@ -25,7 +25,7 @@ TAG_MAP = {
     "URL": {POS: X},
     "INF": {POS: X},
     "NN": {POS: NOUN},
-    "NR": {POS: NOUN},
+    "NR": {POS: PROPN},
     "NT": {POS: NOUN},
     "VA": {POS: VERB},
     "VC": {POS: VERB},

spacy/language.py

@@ -25,7 +25,7 @@ from .util import link_vectors_to_models, create_default_optimizer, registry
 from .attrs import IS_STOP, LANG, NORM
 from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
 from .lang.punctuation import TOKENIZER_INFIXES
-from .lang.tokenizer_exceptions import TOKEN_MATCH
+from .lang.tokenizer_exceptions import TOKEN_MATCH, URL_MATCH
 from .lang.norm_exceptions import BASE_NORMS
 from .lang.tag_map import TAG_MAP
 from .tokens import Doc
@@ -86,6 +86,7 @@ class BaseDefaults(object):
     def create_tokenizer(cls, nlp=None):
         rules = cls.tokenizer_exceptions
         token_match = cls.token_match
+        url_match = cls.url_match
         prefix_search = (
             util.compile_prefix_regex(cls.prefixes).search if cls.prefixes else None
         )
@@ -103,10 +104,12 @@ class BaseDefaults(object):
             suffix_search=suffix_search,
             infix_finditer=infix_finditer,
             token_match=token_match,
+            url_match=url_match,
         )
 
     pipe_names = ["tagger", "parser", "ner"]
     token_match = TOKEN_MATCH
+    url_match = URL_MATCH
     prefixes = tuple(TOKENIZER_PREFIXES)
     suffixes = tuple(TOKENIZER_SUFFIXES)
     infixes = tuple(TOKENIZER_INFIXES)
@@ -951,9 +954,7 @@ class Language(object):
         serializers["tokenizer"] = lambda p: self.tokenizer.to_disk(
             p, exclude=["vocab"]
         )
-        serializers["meta.json"] = lambda p: p.open("w").write(
+        serializers["meta.json"] = lambda p: srsly.write_json(p, self.meta)
-            srsly.json_dumps(self.meta)
-        )
         serializers["config.cfg"] = lambda p: self.config.to_disk(p)
         for name, proc in self.pipeline:
             if not hasattr(proc, "name"):
@@ -977,17 +978,30 @@ class Language(object):
 
         DOCS: https://spacy.io/api/language#from_disk
         """
+
+        def deserialize_meta(path):
+            if path.exists():
+                data = srsly.read_json(path)
+                self.meta.update(data)
+                # self.meta always overrides meta["vectors"] with the metadata
+                # from self.vocab.vectors, so set the name directly
+                self.vocab.vectors.name = data.get("vectors", {}).get("name")
+
+        def deserialize_vocab(path):
+            if path.exists():
+                self.vocab.from_disk(path)
+            _fix_pretrained_vectors_name(self)
+
         if disable is not None:
             warnings.warn(Warnings.W014, DeprecationWarning)
             exclude = disable
         path = util.ensure_path(path)
+
         deserializers = {}
         if Path(path / "config.cfg").exists():
             deserializers["config.cfg"] = lambda p: self.config.from_disk(p)
-        deserializers["meta.json"] = lambda p: self.meta.update(srsly.read_json(p))
+        deserializers["meta.json"] = deserialize_meta
-        deserializers["vocab"] = lambda p: self.vocab.from_disk(
+        deserializers["vocab"] = deserialize_vocab
-            p
-        ) and _fix_pretrained_vectors_name(self)
         deserializers["tokenizer"] = lambda p: self.tokenizer.from_disk(
             p, exclude=["vocab"]
         )
@@ -1041,15 +1055,25 @@ class Language(object):
 
         DOCS: https://spacy.io/api/language#from_bytes
         """
+
+        def deserialize_meta(b):
+            data = srsly.json_loads(b)
+            self.meta.update(data)
+            # self.meta always overrides meta["vectors"] with the metadata
+            # from self.vocab.vectors, so set the name directly
+            self.vocab.vectors.name = data.get("vectors", {}).get("name")
+
+        def deserialize_vocab(b):
+            self.vocab.from_bytes(b)
+            _fix_pretrained_vectors_name(self)
+
         if disable is not None:
             warnings.warn(Warnings.W014, DeprecationWarning)
             exclude = disable
         deserializers = {}
         deserializers["config.cfg"] = lambda b: self.config.from_bytes(b)
-        deserializers["meta.json"] = lambda b: self.meta.update(srsly.json_loads(b))
+        deserializers["meta.json"] = deserialize_meta
-        deserializers["vocab"] = lambda b: self.vocab.from_bytes(
+        deserializers["vocab"] = deserialize_vocab
-            b
-        ) and _fix_pretrained_vectors_name(self)
         deserializers["tokenizer"] = lambda b: self.tokenizer.from_bytes(
             b, exclude=["vocab"]
         )
@@ -1132,7 +1156,7 @@ class component(object):
 def _fix_pretrained_vectors_name(nlp):
     # TODO: Replace this once we handle vectors consistently as static
     # data
-    if "vectors" in nlp.meta and nlp.meta["vectors"].get("name"):
+    if "vectors" in nlp.meta and "name" in nlp.meta["vectors"]:
         nlp.vocab.vectors.name = nlp.meta["vectors"]["name"]
     elif not nlp.vocab.vectors.size:
         nlp.vocab.vectors.name = None
@@ -1142,7 +1166,7 @@ def _fix_pretrained_vectors_name(nlp):
     else:
         raise ValueError(Errors.E092)
     if nlp.vocab.vectors.size != 0:
-        link_vectors_to_models(nlp.vocab, skip_rank=True)
+        link_vectors_to_models(nlp.vocab)
     for name, proc in nlp.pipeline:
         if not hasattr(proc, "cfg"):
             continue

spacy/lexeme.pyx

@@ -9,7 +9,6 @@ import numpy
 from thinc.api import get_array_module
 import warnings
 
-from libc.stdint cimport UINT64_MAX
 from .typedefs cimport attr_t, flags_t
 from .attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
 from .attrs cimport IS_TITLE, IS_UPPER, LIKE_URL, LIKE_NUM, LIKE_EMAIL, IS_STOP
@@ -20,7 +19,7 @@ from .attrs import intify_attrs
 from .errors import Errors, Warnings
 
 
-OOV_RANK = UINT64_MAX
+OOV_RANK = 0xffffffffffffffff # UINT64_MAX
 memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
 EMPTY_LEXEME.id = OOV_RANK
 

spacy/matcher/phrasematcher.pyx

@@ -328,7 +328,7 @@ def unpickle_matcher(vocab, docs, callbacks, attr):
     matcher = PhraseMatcher(vocab, attr=attr)
     for key, specs in docs.items():
         callback = callbacks.get(key, None)
-        matcher.add(key, callback, *specs)
+        matcher.add(key, specs, on_match=callback)
     return matcher
 
 

spacy/morphology.pyx

@@ -66,7 +66,10 @@ cdef class Morphology:
         self.tags = PreshMap()
         # Add special space symbol. We prefix with underscore, to make sure it
         # always sorts to the end.
-        space_attrs = tag_map.get('SP', {POS: SPACE})
+        if '_SP' in tag_map:
+            space_attrs = tag_map.get('_SP')
+        else:
+            space_attrs = tag_map.get('SP', {POS: SPACE})
         if '_SP' not in tag_map:
             self.strings.add('_SP')
             tag_map = dict(tag_map)

spacy/pipeline/pipes.pyx

@@ -216,7 +216,7 @@ class Pipe(object):
         serialize = {}
         serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
         serialize["vocab"] = lambda p: self.vocab.to_disk(p)
-        serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
+        serialize["model"] = lambda p: self.model.to_disk(p)
         exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
         util.to_disk(path, serialize, exclude)
 
@@ -384,6 +384,8 @@ class Tagger(Pipe):
         lemma_tables = ["lemma_rules", "lemma_index", "lemma_exc", "lemma_lookup"]
         if not any(table in self.vocab.lookups for table in lemma_tables):
             warnings.warn(Warnings.W022)
+        if len(self.vocab.lookups.get_table("lexeme_norm", {})) == 0:
+            warnings.warn(Warnings.W033.format(model="part-of-speech tagger"))
         orig_tag_map = dict(self.vocab.morphology.tag_map)
         new_tag_map = {}
         for example in get_examples():
@@ -395,6 +397,8 @@ class Tagger(Pipe):
 
         cdef Vocab vocab = self.vocab
         if new_tag_map:
+            if "_SP" in orig_tag_map:
+                new_tag_map["_SP"] = orig_tag_map["_SP"]
             vocab.morphology = Morphology(vocab.strings, new_tag_map,
                                           vocab.morphology.lemmatizer,
                                           exc=vocab.morphology.exc)
@@ -485,7 +489,7 @@ class Tagger(Pipe):
         serialize = {
             "vocab": lambda p: self.vocab.to_disk(p),
             "tag_map": lambda p: srsly.write_msgpack(p, tag_map),
-            "model": lambda p: p.open("wb").write(self.model.to_bytes()),
+            "model": lambda p: self.model.to_disk(p),
             "cfg": lambda p: srsly.write_json(p, self.cfg),
         }
         exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
@@ -1129,6 +1133,8 @@ class EntityLinker(Pipe):
             raise ValueError(Errors.E990.format(type=type(self.kb)))
         self.cfg = dict(cfg)
         self.distance = CosineDistance(normalize=False)
+        # how many neightbour sentences to take into account
+        self.n_sents = cfg.get("n_sents", 0)
 
     def require_kb(self):
         # Raise an error if the knowledge base is not initialized.
@@ -1161,6 +1167,9 @@ class EntityLinker(Pipe):
 
         for doc, gold in zip(docs, golds):
             ents_by_offset = dict()
+
+            sentences = [s for s in doc.sents]
+
             for ent in doc.ents:
                 ents_by_offset[(ent.start_char, ent.end_char)] = ent
 
@@ -1173,20 +1182,39 @@ class EntityLinker(Pipe):
                 # the gold annotations should link to proper entities - if this fails, the dataset is likely corrupt
                 if not (start, end) in ents_by_offset:
                     raise RuntimeError(Errors.E188)
+
                 ent = ents_by_offset[(start, end)]
 
                 for kb_id, value in kb_dict.items():
                     # Currently only training on the positive instances - we assume there is at least 1 per doc/gold
                     if value:
                         try:
-                            sentence_docs.append(ent.sent.as_doc())
+                            # find the sentence in the list of sentences.
+                            sent_index = sentences.index(ent.sent)
+
                         except AttributeError:
                             # Catch the exception when ent.sent is None and provide a user-friendly warning
                             raise RuntimeError(Errors.E030)
+
+                        # get n previous sentences, if there are any
+                        start_sentence = max(0, sent_index - self.n_sents)
+
+                        # get n posterior sentences, or as many < n as there are
+                        end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
+
+                        # get token positions
+                        start_token = sentences[start_sentence].start
+                        end_token = sentences[end_sentence].end
+
+                        # append that span as a doc to training
+                        sent_doc = doc[start_token:end_token].as_doc()
+                        sentence_docs.append(sent_doc)
+
         set_dropout_rate(self.model, drop)
         sentence_encodings, bp_context = self.model.begin_update(sentence_docs)
         loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds)
         bp_context(d_scores)
+
         if sgd is not None:
             self.model.finish_update(sgd)
 
@@ -1268,68 +1296,78 @@ class EntityLinker(Pipe):
             docs = [docs]
 
         for i, doc in enumerate(docs):
+            sentences = [s for s in doc.sents]
+
             if len(doc) > 0:
                 # Looping through each sentence and each entity
                 # This may go wrong if there are entities across sentences - which shouldn't happen normally.
-                for sent in doc.sents:
+                for sent_index, sent in enumerate(sentences):
-                    sent_doc = sent.as_doc()
+                    if sent.ents:
-                    # currently, the context is the same for each entity in a sentence (should be refined)
+                        # get n_neightbour sentences, clipped to the length of the document
-                    sentence_encoding = self.model.predict([sent_doc])[0]
+                        start_sentence = max(0, sent_index - self.n_sents)
-                    xp = get_array_module(sentence_encoding)
+                        end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
-                    sentence_encoding_t = sentence_encoding.T
-                    sentence_norm = xp.linalg.norm(sentence_encoding_t)
 
-                    for ent in sent_doc.ents:
+                        start_token = sentences[start_sentence].start
-                        entity_count += 1
+                        end_token = sentences[end_sentence].end
 
-                        to_discard = self.cfg.get("labels_discard", [])
+                        sent_doc = doc[start_token:end_token].as_doc()
-                        if to_discard and ent.label_ in to_discard:
+                        # currently, the context is the same for each entity in a sentence (should be refined)
-                            # ignoring this entity - setting to NIL
+                        sentence_encoding = self.model.predict([sent_doc])[0]
-                            final_kb_ids.append(self.NIL)
+                        xp = get_array_module(sentence_encoding)
-                            final_tensors.append(sentence_encoding)
+                        sentence_encoding_t = sentence_encoding.T
+                        sentence_norm = xp.linalg.norm(sentence_encoding_t)
 
-                        else:
+                        for ent in sent.ents:
-                            candidates = self.kb.get_candidates(ent.text)
+                            entity_count += 1
-                            if not candidates:
+
-                                # no prediction possible for this entity - setting to NIL
+                            to_discard = self.cfg.get("labels_discard", [])
+                            if to_discard and ent.label_ in to_discard:
+                                # ignoring this entity - setting to NIL
                                 final_kb_ids.append(self.NIL)
                                 final_tensors.append(sentence_encoding)
 
-                            elif len(candidates) == 1:
-                                # shortcut for efficiency reasons: take the 1 candidate
-
-                                # TODO: thresholding
-                                final_kb_ids.append(candidates[0].entity_)
-                                final_tensors.append(sentence_encoding)
-
                             else:
-                                random.shuffle(candidates)
+                                candidates = self.kb.get_candidates(ent.text)
+                                if not candidates:
+                                    # no prediction possible for this entity - setting to NIL
+                                    final_kb_ids.append(self.NIL)
+                                    final_tensors.append(sentence_encoding)
 
-                                # this will set all prior probabilities to 0 if they should be excluded from the model
+                                elif len(candidates) == 1:
-                                prior_probs = xp.asarray([c.prior_prob for c in candidates])
+                                    # shortcut for efficiency reasons: take the 1 candidate
-                                if not self.cfg.get("incl_prior", True):
-                                    prior_probs = xp.asarray([0.0 for c in candidates])
-                                scores = prior_probs
 
-                                # add in similarity from the context
+                                    # TODO: thresholding
-                                if self.cfg.get("incl_context", True):
+                                    final_kb_ids.append(candidates[0].entity_)
-                                    entity_encodings = xp.asarray([c.entity_vector for c in candidates])
+                                    final_tensors.append(sentence_encoding)
-                                    entity_norm = xp.linalg.norm(entity_encodings, axis=1)
 
-                                    if len(entity_encodings) != len(prior_probs):
+                                else:
-                                        raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
+                                    random.shuffle(candidates)
 
-                                    # cosine similarity
+                                    # this will set all prior probabilities to 0 if they should be excluded from the model
-                                    sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm)
+                                    prior_probs = xp.asarray([c.prior_prob for c in candidates])
-                                    if sims.shape != prior_probs.shape:
+                                    if not self.cfg.get("incl_prior", True):
-                                        raise ValueError(Errors.E161)
+                                        prior_probs = xp.asarray([0.0 for c in candidates])
-                                    scores = prior_probs + sims - (prior_probs*sims)
+                                    scores = prior_probs
 
-                                # TODO: thresholding
+                                    # add in similarity from the context
-                                best_index = scores.argmax().item()
+                                    if self.cfg.get("incl_context", True):
-                                best_candidate = candidates[best_index]
+                                        entity_encodings = xp.asarray([c.entity_vector for c in candidates])
-                                final_kb_ids.append(best_candidate.entity_)
+                                        entity_norm = xp.linalg.norm(entity_encodings, axis=1)
-                                final_tensors.append(sentence_encoding)
+
+                                        if len(entity_encodings) != len(prior_probs):
+                                            raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
+
+                                        # cosine similarity
+                                        sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm)
+                                        if sims.shape != prior_probs.shape:
+                                            raise ValueError(Errors.E161)
+                                        scores = prior_probs + sims - (prior_probs*sims)
+
+                                    # TODO: thresholding
+                                    best_index = scores.argmax().item()
+                                    best_candidate = candidates[best_index]
+                                    final_kb_ids.append(best_candidate.entity_)
+                                    final_tensors.append(sentence_encoding)
 
         if not (len(final_tensors) == len(final_kb_ids) == entity_count):
             raise RuntimeError(Errors.E147.format(method="predict", msg="result variables not of equal length"))
@@ -1355,7 +1393,7 @@ class EntityLinker(Pipe):
         serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
         serialize["vocab"] = lambda p: self.vocab.to_disk(p)
         serialize["kb"] = lambda p: self.kb.dump(p)
-        serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
+        serialize["model"] = lambda p: self.model.to_disk(p)
         exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
         util.to_disk(path, serialize, exclude)
 

spacy/syntax/nn_parser.pyx

@@ -1,6 +1,7 @@
 # cython: infer_types=True, cdivision=True, boundscheck=False
 cimport cython.parallel
 cimport numpy as np
+from itertools import islice
 from cpython.ref cimport PyObject, Py_XDECREF
 from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
 from libc.math cimport exp
@@ -607,6 +608,8 @@ cdef class Parser:
 
     def begin_training(self, get_examples, pipeline=None, sgd=None, **kwargs):
         self.cfg.update(kwargs)
+        if len(self.vocab.lookups.get_table("lexeme_norm", {})) == 0:
+            warnings.warn(Warnings.W033.format(model="parser or NER"))
         if not hasattr(get_examples, '__call__'):
             gold_tuples = get_examples
             get_examples = lambda: gold_tuples

spacy/tests/conftest.py

@@ -137,7 +137,7 @@ def it_tokenizer():
 
 @pytest.fixture(scope="session")
 def ja_tokenizer():
-    pytest.importorskip("fugashi")
+    pytest.importorskip("sudachipy")
     return get_lang_class("ja").Defaults.create_tokenizer()
 
 

spacy/tests/doc/test_retokenize_merge.py

@@ -429,3 +429,10 @@ def test_retokenize_skip_duplicates(en_vocab):
         retokenizer.merge(doc[0:2])
     assert len(doc) == 2
     assert doc[0].text == "hello world"
+
+
+def test_retokenize_disallow_zero_length(en_vocab):
+    doc = Doc(en_vocab, words=["hello", "world", "!"])
+    with pytest.raises(ValueError):
+        with doc.retokenize() as retokenizer:
+            retokenizer.merge(doc[1:1])

spacy/tests/lang/de/test_noun_chunks.py

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

spacy/tests/lang/el/test_noun_chunks.py

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

spacy/tests/lang/en/test_exceptions.py

@@ -43,7 +43,7 @@ def test_en_tokenizer_doesnt_split_apos_exc(en_tokenizer, text):
     assert tokens[0].text == text
 
 
-@pytest.mark.parametrize("text", ["we'll", "You'll", "there'll"])
+@pytest.mark.parametrize("text", ["we'll", "You'll", "there'll", "this'll", "those'll"])
 def test_en_tokenizer_handles_ll_contraction(en_tokenizer, text):
     tokens = en_tokenizer(text)
     assert len(tokens) == 2

spacy/tests/lang/es/test_noun_chunks.py

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

spacy/tests/lang/fa/test_noun_chunks.py

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

spacy/tests/lang/fr/test_noun_chunks.py

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

spacy/tests/lang/gu/test_text.py

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

spacy/tests/lang/hy/test_text.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 import pytest
 from spacy.lang.hy.lex_attrs import like_num
 

spacy/tests/lang/hy/test_tokenizer.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 import pytest
 
 

spacy/tests/lang/id/test_noun_chunks.py

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

spacy/tests/lang/ja/test_lemmatization.py

@@ -3,7 +3,7 @@ import pytest
 
 @pytest.mark.parametrize(
     "word,lemma",
-    [("新しく", "新しい"), ("赤く", "赤い"), ("すごく", "凄い"), ("いただきました", "頂く"), ("なった", "成る")],
+    [("新しく", "新しい"), ("赤く", "赤い"), ("すごく", "すごい"), ("いただきました", "いただく"), ("なった", "なる")],
 )
 def test_ja_lemmatizer_assigns(ja_tokenizer, word, lemma):
     test_lemma = ja_tokenizer(word)[0].lemma_

spacy/tests/lang/ja/test_serialize.py

@@ -0,0 +1,33 @@
+from spacy.lang.ja import Japanese
+from ...util import make_tempdir
+
+
+def test_ja_tokenizer_serialize(ja_tokenizer):
+    tokenizer_bytes = ja_tokenizer.to_bytes()
+    nlp = Japanese()
+    nlp.tokenizer.from_bytes(tokenizer_bytes)
+    assert tokenizer_bytes == nlp.tokenizer.to_bytes()
+    assert nlp.tokenizer.split_mode is None
+
+    with make_tempdir() as d:
+        file_path = d / "tokenizer"
+        ja_tokenizer.to_disk(file_path)
+        nlp = Japanese()
+        nlp.tokenizer.from_disk(file_path)
+        assert tokenizer_bytes == nlp.tokenizer.to_bytes()
+        assert nlp.tokenizer.split_mode is None
+
+    # split mode is (de)serialized correctly
+    nlp = Japanese(meta={"tokenizer": {"config": {"split_mode": "B"}}})
+    nlp_r = Japanese()
+    nlp_bytes = nlp.to_bytes()
+    nlp_r.from_bytes(nlp_bytes)
+    assert nlp_bytes == nlp_r.to_bytes()
+    assert nlp_r.tokenizer.split_mode == "B"
+
+    with make_tempdir() as d:
+        nlp.to_disk(d)
+        nlp_r = Japanese()
+        nlp_r.from_disk(d)
+        assert nlp_bytes == nlp_r.to_bytes()
+        assert nlp_r.tokenizer.split_mode == "B"

spacy/tests/lang/ja/test_tokenizer.py

@@ -1,5 +1,7 @@
 import pytest
 
+from ...tokenizer.test_naughty_strings import NAUGHTY_STRINGS
+from spacy.lang.ja import Japanese
 
 # fmt: off
 TOKENIZER_TESTS = [
@@ -11,20 +13,25 @@ TOKENIZER_TESTS = [
 ]
 
 TAG_TESTS = [
-    ("日本語だよ", ['名詞,固有名詞,地名,国', '名詞,普通名詞,一般,*', '助動詞,*,*,*', '助詞,終助詞,*,*']),
+    ("日本語だよ", ['名詞-固有名詞-地名-国', '名詞-普通名詞-一般', '助動詞', '助詞-終助詞']),
-    ("東京タワーの近くに住んでいます。", ['名詞,固有名詞,地名,一般', '名詞,普通名詞,一般,*', '助詞,格助詞,*,*', '名詞,普通名詞,副詞可能,*', '助詞,格助詞,*,*', '動詞,一般,*,*', '助詞,接続助詞,*,*', '動詞,非自立可能,*,*', '助動詞,*,*,*', '補助記号,句点,*,*']),
+    ("東京タワーの近くに住んでいます。", ['名詞-固有名詞-地名-一般', '名詞-普通名詞-一般', '助詞-格助詞', '名詞-普通名詞-副詞可能', '助詞-格助詞', '動詞-一般', '助詞-接続助詞', '動詞-非自立可能', '助動詞', '補助記号-句点']),
-    ("吾輩は猫である。", ['代名詞,*,*,*', '助詞,係助詞,*,*', '名詞,普通名詞,一般,*', '助動詞,*,*,*', '動詞,非自立可能,*,*', '補助記号,句点,*,*']),
+    ("吾輩は猫である。", ['代名詞', '助詞-係助詞', '名詞-普通名詞-一般', '助動詞', '動詞-非自立可能', '補助記号-句点']),
-    ("月に代わって、お仕置きよ!", ['名詞,普通名詞,助数詞可能,*', '助詞,格助詞,*,*', '動詞,一般,*,*', '助詞,接続助詞,*,*', '補助記号,読点,*,*', '接頭辞,*,*,*', '名詞,普通名詞,一般,*', '助詞,終助詞,*,*', '補助記号,句点,*,*']),
+    ("月に代わって、お仕置きよ!", ['名詞-普通名詞-助数詞可能', '助詞-格助詞', '動詞-一般', '助詞-接続助詞', '補助記号-読点', '接頭辞', '名詞-普通名詞-一般', '助詞-終助詞', '補助記号-句点']),
-    ("すもももももももものうち", ['名詞,普通名詞,一般,*', '助詞,係助詞,*,*', '名詞,普通名詞,一般,*', '助詞,係助詞,*,*', '名詞,普通名詞,一般,*', '助詞,格助詞,*,*', '名詞,普通名詞,副詞可能,*'])
+    ("すもももももももものうち", ['名詞-普通名詞-一般', '助詞-係助詞', '名詞-普通名詞-一般', '助詞-係助詞', '名詞-普通名詞-一般', '助詞-格助詞', '名詞-普通名詞-副詞可能'])
 ]
 
 POS_TESTS = [
-    ('日本語だよ', ['PROPN', 'NOUN', 'AUX', 'PART']),
+    ('日本語だよ', ['fish', 'NOUN', 'AUX', 'PART']),
     ('東京タワーの近くに住んでいます。', ['PROPN', 'NOUN', 'ADP', 'NOUN', 'ADP', 'VERB', 'SCONJ', 'VERB', 'AUX', 'PUNCT']),
     ('吾輩は猫である。', ['PRON', 'ADP', 'NOUN', 'AUX', 'VERB', 'PUNCT']),
     ('月に代わって、お仕置きよ!', ['NOUN', 'ADP', 'VERB', 'SCONJ', 'PUNCT', 'NOUN', 'NOUN', 'PART', 'PUNCT']),
     ('すもももももももものうち', ['NOUN', 'ADP', 'NOUN', 'ADP', 'NOUN', 'ADP', 'NOUN'])
 ]
+
+SENTENCE_TESTS = [
+    ("あれ。これ。", ["あれ。", "これ。"]),
+    ("「伝染るんです。」という漫画があります。", ["「伝染るんです。」という漫画があります。"]),
+]
 # fmt: on
 
 
@@ -40,14 +47,56 @@ def test_ja_tokenizer_tags(ja_tokenizer, text, expected_tags):
     assert tags == expected_tags
 
 
+# XXX This isn't working? Always passes
 @pytest.mark.parametrize("text,expected_pos", POS_TESTS)
 def test_ja_tokenizer_pos(ja_tokenizer, text, expected_pos):
     pos = [token.pos_ for token in ja_tokenizer(text)]
     assert pos == expected_pos
 
 
-def test_extra_spaces(ja_tokenizer):
+@pytest.mark.skip(reason="sentence segmentation in tokenizer is buggy")
+@pytest.mark.parametrize("text,expected_sents", SENTENCE_TESTS)
+def test_ja_tokenizer_sents(ja_tokenizer, text, expected_sents):
+    sents = [str(sent) for sent in ja_tokenizer(text).sents]
+    assert sents == expected_sents
+
+
+def test_ja_tokenizer_extra_spaces(ja_tokenizer):
     # note: three spaces after "I"
     tokens = ja_tokenizer("I   like cheese.")
-    assert tokens[1].orth_ == " "
+    assert tokens[1].orth_ == "  "
-    assert tokens[2].orth_ == " "
+
+
+@pytest.mark.parametrize("text", NAUGHTY_STRINGS)
+def test_ja_tokenizer_naughty_strings(ja_tokenizer, text):
+    tokens = ja_tokenizer(text)
+    assert tokens.text_with_ws == text
+
+
+@pytest.mark.parametrize(
+    "text,len_a,len_b,len_c",
+    [
+        ("選挙管理委員会", 4, 3, 1),
+        ("客室乗務員", 3, 2, 1),
+        ("労働者協同組合", 4, 3, 1),
+        ("機能性食品", 3, 2, 1),
+    ],
+)
+def test_ja_tokenizer_split_modes(ja_tokenizer, text, len_a, len_b, len_c):
+    nlp_a = Japanese(meta={"tokenizer": {"config": {"split_mode": "A"}}})
+    nlp_b = Japanese(meta={"tokenizer": {"config": {"split_mode": "B"}}})
+    nlp_c = Japanese(meta={"tokenizer": {"config": {"split_mode": "C"}}})
+
+    assert len(ja_tokenizer(text)) == len_a
+    assert len(nlp_a(text)) == len_a
+    assert len(nlp_b(text)) == len_b
+    assert len(nlp_c(text)) == len_c
+
+
+def test_ja_tokenizer_emptyish_texts(ja_tokenizer):
+    doc = ja_tokenizer("")
+    assert len(doc) == 0
+    doc = ja_tokenizer(" ")
+    assert len(doc) == 1
+    doc = ja_tokenizer("\n\n\n \t\t \n\n\n")
+    assert len(doc) == 1

spacy/tests/lang/ml/test_text.py

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

spacy/tests/lang/nb/test_noun_chunks.py

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

spacy/tests/lang/sv/test_lex_attrs.py

@@ -1,6 +1,3 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
 import pytest
 from spacy.lang.sv.lex_attrs import like_num
 

spacy/tests/lang/zh/test_serialize.py

@@ -1,6 +1,3 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
 import pytest
 from spacy.lang.zh import Chinese
 from ...util import make_tempdir

spacy/tests/matcher/test_phrase_matcher.py

@@ -1,4 +1,5 @@
 import pytest
+import srsly
 from mock import Mock
 from spacy.matcher import PhraseMatcher
 from spacy.tokens import Doc
@@ -263,3 +264,26 @@ def test_phrase_matcher_basic_check(en_vocab):
     pattern = Doc(en_vocab, words=["hello", "world"])
     with pytest.raises(ValueError):
         matcher.add("TEST", pattern)
+
+
+def test_phrase_matcher_pickle(en_vocab):
+    matcher = PhraseMatcher(en_vocab)
+    mock = Mock()
+    matcher.add("TEST", [Doc(en_vocab, words=["test"])])
+    matcher.add("TEST2", [Doc(en_vocab, words=["test2"])], on_match=mock)
+    doc = Doc(en_vocab, words=["these", "are", "tests", ":", "test", "test2"])
+    assert len(matcher) == 2
+
+    b = srsly.pickle_dumps(matcher)
+    matcher_unpickled = srsly.pickle_loads(b)
+
+    # call after pickling to avoid recursion error related to mock
+    matches = matcher(doc)
+    matches_unpickled = matcher_unpickled(doc)
+
+    assert len(matcher) == len(matcher_unpickled)
+    assert matches == matches_unpickled
+
+    # clunky way to vaguely check that callback is unpickled
+    (vocab, docs, callbacks, attr) = matcher_unpickled.__reduce__()[1]
+    assert isinstance(callbacks.get("TEST2"), Mock)

spacy/tests/package/test_requirements.py

@@ -10,7 +10,13 @@ def test_build_dependencies():
         "mock",
         "flake8",
     ]
-    libs_ignore_setup = ["fugashi", "natto-py", "pythainlp"]
+    libs_ignore_setup = [
+        "fugashi",
+        "natto-py",
+        "pythainlp",
+        "sudachipy",
+        "sudachidict_core",
+    ]
 
     # check requirements.txt
     req_dict = {}

spacy/tests/parser/test_ner.py

@@ -1,6 +1,9 @@
 import pytest
 from spacy import util
 from spacy.lang.en import English
+
+from spacy.language import Language
+from spacy.lookups import Lookups
 from spacy.pipeline.defaults import default_ner
 from spacy.pipeline import EntityRecognizer, EntityRuler
 from spacy.vocab import Vocab
@@ -349,6 +352,21 @@ def test_overfitting_IO():
         assert ents2[0].label_ == "LOC"
 
 
+def test_ner_warns_no_lookups():
+    nlp = Language()
+    nlp.vocab.lookups = Lookups()
+    assert not len(nlp.vocab.lookups)
+    ner = nlp.create_pipe("ner")
+    nlp.add_pipe(ner)
+    with pytest.warns(UserWarning):
+        nlp.begin_training()
+    nlp.vocab.lookups.add_table("lexeme_norm")
+    nlp.vocab.lookups.get_table("lexeme_norm")["a"] = "A"
+    with pytest.warns(None) as record:
+        nlp.begin_training()
+        assert not record.list
+
+
 class BlockerComponent1(object):
     name = "my_blocker"
 

spacy/tests/regression/test_issue5230.py

@@ -0,0 +1,141 @@
+import warnings
+from unittest import TestCase
+import pytest
+import srsly
+from numpy import zeros
+from spacy.kb import KnowledgeBase, Writer
+from spacy.vectors import Vectors
+from spacy.language import Language
+from spacy.pipeline import Pipe
+
+
+from ..util import make_tempdir
+
+
+def nlp():
+    return Language()
+
+
+def vectors():
+    data = zeros((3, 1), dtype="f")
+    keys = ["cat", "dog", "rat"]
+    return Vectors(data=data, keys=keys)
+
+
+def custom_pipe():
+    # create dummy pipe partially implementing interface -- only want to test to_disk
+    class SerializableDummy(object):
+        def __init__(self, **cfg):
+            if cfg:
+                self.cfg = cfg
+            else:
+                self.cfg = None
+            super(SerializableDummy, self).__init__()
+
+        def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
+            return srsly.msgpack_dumps({"dummy": srsly.json_dumps(None)})
+
+        def from_bytes(self, bytes_data, exclude):
+            return self
+
+        def to_disk(self, path, exclude=tuple(), **kwargs):
+            pass
+
+        def from_disk(self, path, exclude=tuple(), **kwargs):
+            return self
+
+    class MyPipe(Pipe):
+        def __init__(self, vocab, model=True, **cfg):
+            if cfg:
+                self.cfg = cfg
+            else:
+                self.cfg = None
+            self.model = SerializableDummy()
+            self.vocab = SerializableDummy()
+
+    return MyPipe(None)
+
+
+def tagger():
+    nlp = Language()
+    nlp.add_pipe(nlp.create_pipe("tagger"))
+    tagger = nlp.get_pipe("tagger")
+    # need to add model for two reasons:
+    # 1. no model leads to error in serialization,
+    # 2. the affected line is the one for model serialization
+    tagger.begin_training(pipeline=nlp.pipeline)
+    return tagger
+
+
+def entity_linker():
+    nlp = Language()
+    kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    kb.add_entity("test", 0.0, zeros((1, 1), dtype="f"))
+    nlp.add_pipe(nlp.create_pipe("entity_linker", {"kb": kb}))
+    entity_linker = nlp.get_pipe("entity_linker")
+    # need to add model for two reasons:
+    # 1. no model leads to error in serialization,
+    # 2. the affected line is the one for model serialization
+    entity_linker.begin_training(pipeline=nlp.pipeline)
+    return entity_linker
+
+
+objects_to_test = (
+    [nlp(), vectors(), custom_pipe(), tagger(), entity_linker()],
+    ["nlp", "vectors", "custom_pipe", "tagger", "entity_linker"],
+)
+
+
+def write_obj_and_catch_warnings(obj):
+    with make_tempdir() as d:
+        with warnings.catch_warnings(record=True) as warnings_list:
+            warnings.filterwarnings("always", category=ResourceWarning)
+            obj.to_disk(d)
+            # in python3.5 it seems that deprecation warnings are not filtered by filterwarnings
+            return list(filter(lambda x: isinstance(x, ResourceWarning), warnings_list))
+
+
+@pytest.mark.parametrize("obj", objects_to_test[0], ids=objects_to_test[1])
+def test_to_disk_resource_warning(obj):
+    warnings_list = write_obj_and_catch_warnings(obj)
+    assert len(warnings_list) == 0
+
+
+def test_writer_with_path_py35():
+    writer = None
+    with make_tempdir() as d:
+        path = d / "test"
+        try:
+            writer = Writer(path)
+        except Exception as e:
+            pytest.fail(str(e))
+        finally:
+            if writer:
+                writer.close()
+
+
+def test_save_and_load_knowledge_base():
+    nlp = Language()
+    kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    with make_tempdir() as d:
+        path = d / "kb"
+        try:
+            kb.dump(path)
+        except Exception as e:
+            pytest.fail(str(e))
+
+        try:
+            kb_loaded = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+            kb_loaded.load_bulk(path)
+        except Exception as e:
+            pytest.fail(str(e))
+
+
+class TestToDiskResourceWarningUnittest(TestCase):
+    def test_resource_warning(self):
+        scenarios = zip(*objects_to_test)
+
+        for scenario in scenarios:
+            with self.subTest(msg=scenario[1]):
+                warnings_list = write_obj_and_catch_warnings(scenario[0])
+                self.assertEqual(len(warnings_list), 0)

spacy/tests/regression/test_issue5458.py

@@ -0,0 +1,23 @@
+from spacy.lang.en import English
+from spacy.lang.en.syntax_iterators import noun_chunks
+from spacy.tests.util import get_doc
+from spacy.vocab import Vocab
+
+
+def test_issue5458():
+    # Test that the noun chuncker does not generate overlapping spans
+    # fmt: off
+    words = ["In", "an", "era", "where", "markets", "have", "brought", "prosperity", "and", "empowerment", "."]
+    vocab = Vocab(strings=words)
+    dependencies = ["ROOT", "det", "pobj", "advmod", "nsubj", "aux", "relcl", "dobj", "cc", "conj", "punct"]
+    pos_tags = ["ADP", "DET", "NOUN", "ADV", "NOUN", "AUX", "VERB", "NOUN", "CCONJ", "NOUN", "PUNCT"]
+    heads = [0, 1, -2, 6, 2, 1, -4, -1, -1, -2, -10]
+    # fmt: on
+
+    en_doc = get_doc(vocab, words, pos_tags, heads, dependencies)
+    en_doc.noun_chunks_iterator = noun_chunks
+
+    # if there are overlapping spans, this will fail with an E102 error "Can't merge non-disjoint spans"
+    nlp = English()
+    merge_nps = nlp.create_pipe("merge_noun_chunks")
+    merge_nps(en_doc)

spacy/tests/test_lemmatizer.py

@@ -30,7 +30,7 @@ def test_lemmatizer_reflects_lookups_changes():
     assert Doc(new_nlp.vocab, words=["hello"])[0].lemma_ == "world"
 
 
-def test_tagger_warns_no_lemma_lookups():
+def test_tagger_warns_no_lookups():
     nlp = Language()
     nlp.vocab.lookups = Lookups()
     assert not len(nlp.vocab.lookups)
@@ -41,6 +41,8 @@ def test_tagger_warns_no_lemma_lookups():
     with pytest.warns(UserWarning):
         nlp.begin_training()
     nlp.vocab.lookups.add_table("lemma_lookup")
+    nlp.vocab.lookups.add_table("lexeme_norm")
+    nlp.vocab.lookups.get_table("lexeme_norm")["a"] = "A"
     with pytest.warns(None) as record:
         nlp.begin_training()
         assert not record.list

spacy/tests/test_misc.py

@@ -93,6 +93,17 @@ def test_ascii_filenames():
         assert all(ord(c) < 128 for c in path.name), path.name
 
 
+def test_load_model_blank_shortcut():
+    """Test that using a model name like "blank:en" works as a shortcut for
+    spacy.blank("en").
+    """
+    nlp = util.load_model("blank:en")
+    assert nlp.lang == "en"
+    assert nlp.pipeline == []
+    with pytest.raises(ImportError):
+        util.load_model("blank:fjsfijsdof")
+
+
 @pytest.mark.parametrize(
     "version,constraint,compatible",
     [

spacy/tests/tokenizer/test_urls.py

@@ -121,12 +121,12 @@ SUFFIXES = ['"', ":", ">"]
 
 @pytest.mark.parametrize("url", URLS_SHOULD_MATCH)
 def test_should_match(en_tokenizer, url):
-    assert en_tokenizer.token_match(url) is not None
+    assert en_tokenizer.url_match(url) is not None
 
 
 @pytest.mark.parametrize("url", URLS_SHOULD_NOT_MATCH)
 def test_should_not_match(en_tokenizer, url):
-    assert en_tokenizer.token_match(url) is None
+    assert en_tokenizer.url_match(url) is None
 
 
 @pytest.mark.parametrize("url", URLS_BASIC)

spacy/tokenizer.pxd

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

spacy/tokenizer.pyx

@@ -31,7 +31,8 @@ cdef class Tokenizer:
     DOCS: https://spacy.io/api/tokenizer
     """
     def __init__(self, Vocab vocab, rules=None, prefix_search=None,
-                 suffix_search=None, infix_finditer=None, token_match=None):
+                 suffix_search=None, infix_finditer=None, token_match=None,
+                 url_match=None):
         """Create a `Tokenizer`, to create `Doc` objects given unicode text.
 
         vocab (Vocab): A storage container for lexical types.
@@ -44,6 +45,8 @@ cdef class Tokenizer:
             `re.compile(string).finditer` to find infixes.
         token_match (callable): A boolean function matching strings to be
             recognised as tokens.
+        url_match (callable): A boolean function matching strings to be
+            recognised as tokens after considering prefixes and suffixes.
         RETURNS (Tokenizer): The newly constructed object.
 
         EXAMPLE:
@@ -56,6 +59,7 @@ cdef class Tokenizer:
         self._cache = PreshMap()
         self._specials = PreshMap()
         self.token_match = token_match
+        self.url_match = url_match
         self.prefix_search = prefix_search
         self.suffix_search = suffix_search
         self.infix_finditer = infix_finditer
@@ -76,6 +80,14 @@ cdef class Tokenizer:
             if self._property_init_count <= self._property_init_max:
                 self._property_init_count += 1
 
+    property url_match:
+        def __get__(self):
+            return self._url_match
+
+        def __set__(self, url_match):
+            self._url_match = url_match
+            self._flush_cache()
+
     property prefix_search:
         def __get__(self):
             return self._prefix_search
@@ -120,11 +132,12 @@ cdef class Tokenizer:
 
     def __reduce__(self):
         args = (self.vocab,
-                self._rules,
+                self.rules,
                 self.prefix_search,
                 self.suffix_search,
                 self.infix_finditer,
-                self.token_match)
+                self.token_match,
+                self.url_match)
         return (self.__class__, args, None, None)
 
     cpdef Doc tokens_from_list(self, list strings):
@@ -461,7 +474,9 @@ cdef class Tokenizer:
                 cache_hit = self._try_cache(hash_string(string), tokens)
             if specials_hit or cache_hit:
                 pass
-            elif self.token_match and self.token_match(string):
+            elif (self.token_match and self.token_match(string)) or \
+                    (self.url_match and \
+                    self.url_match(string)):
                 # We're always saying 'no' to spaces here -- the caller will
                 # fix up the outermost one, with reference to the original.
                 # See Issue #859
@@ -638,6 +653,11 @@ cdef class Tokenizer:
         suffix_search = self.suffix_search
         infix_finditer = self.infix_finditer
         token_match = self.token_match
+        if token_match is None:
+            token_match = re.compile("a^").match
+        url_match = self.url_match
+        if url_match is None:
+            url_match = re.compile("a^").match
         special_cases = {}
         for orth, special_tokens in self.rules.items():
             special_cases[orth] = [intify_attrs(special_token, strings_map=self.vocab.strings, _do_deprecated=True) for special_token in special_tokens]
@@ -646,6 +666,10 @@ cdef class Tokenizer:
             suffixes = []
             while substring:
                 while prefix_search(substring) or suffix_search(substring):
+                    if token_match(substring):
+                        tokens.append(("TOKEN_MATCH", substring))
+                        substring = ''
+                        break
                     if substring in special_cases:
                         tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
                         substring = ''
@@ -666,12 +690,15 @@ cdef class Tokenizer:
                             break
                         suffixes.append(("SUFFIX", substring[split:]))
                         substring = substring[:split]
-                if substring in special_cases:
+                if token_match(substring):
-                    tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
-                    substring = ''
-                elif token_match(substring):
                     tokens.append(("TOKEN_MATCH", substring))
                     substring = ''
+                elif url_match(substring):
+                    tokens.append(("URL_MATCH", substring))
+                    substring = ''
+                elif substring in special_cases:
+                    tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
+                    substring = ''
                 elif list(infix_finditer(substring)):
                     infixes = infix_finditer(substring)
                     offset = 0
@@ -733,6 +760,7 @@ cdef class Tokenizer:
             "suffix_search": lambda: _get_regex_pattern(self.suffix_search),
             "infix_finditer": lambda: _get_regex_pattern(self.infix_finditer),
             "token_match": lambda: _get_regex_pattern(self.token_match),
+            "url_match": lambda: _get_regex_pattern(self.url_match),
             "exceptions": lambda: dict(sorted(self._rules.items()))
         }
         exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
@@ -754,6 +782,7 @@ cdef class Tokenizer:
             "suffix_search": lambda b: data.setdefault("suffix_search", b),
             "infix_finditer": lambda b: data.setdefault("infix_finditer", b),
             "token_match": lambda b: data.setdefault("token_match", b),
+            "url_match": lambda b: data.setdefault("url_match", b),
             "exceptions": lambda b: data.setdefault("rules", b)
         }
         exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
@@ -766,6 +795,8 @@ cdef class Tokenizer:
             self.infix_finditer = re.compile(data["infix_finditer"]).finditer
         if "token_match" in data and isinstance(data["token_match"], str):
             self.token_match = re.compile(data["token_match"]).match
+        if "url_match" in data and isinstance(data["url_match"], str):
+            self.url_match = re.compile(data["url_match"]).match
         if "rules" in data and isinstance(data["rules"], dict):
             # make sure to hard reset the cache to remove data from the default exceptions
             self._rules = {}

spacy/tokens/_retokenize.pyx

@@ -50,6 +50,8 @@ cdef class Retokenizer:
         """
         if (span.start, span.end) in self._spans_to_merge:
             return
+        if span.end - span.start <= 0:
+            raise ValueError(Errors.E199.format(start=span.start, end=span.end))
         for token in span:
             if token.i in self.tokens_to_merge:
                 raise ValueError(Errors.E102.format(token=repr(token)))

spacy/tokens/morphanalysis.pyx

@@ -45,12 +45,6 @@ cdef class MorphAnalysis:
         """The number of features in the analysis."""
         return self.c.length
 
-    def __str__(self):
-        return self.to_json()
-
-    def __repr__(self):
-        return self.to_json()
-
     def __hash__(self):
         return self.key
 
@@ -79,3 +73,10 @@ cdef class MorphAnalysis:
         """Produce a dict representation.
         """
         return self.vocab.morphology.feats_to_dict(self.to_json())
+
+    def __str__(self):
+        return self.to_json()
+
+    def __repr__(self):
+        return self.to_json()
+

spacy/util.py

@@ -137,6 +137,8 @@ def load_model(name, **overrides):
     RETURNS (Language): `Language` class with the loaded model.
     """
     if isinstance(name, str):  # name or string path
+        if name.startswith("blank:"):  # shortcut for blank model
+            return get_lang_class(name.replace("blank:", ""))()
         if is_package(name):  # installed as package
             return load_model_from_package(name, **overrides)
         if Path(name).exists():  # path to model data directory

spacy/vectors.pyx

@@ -376,8 +376,16 @@ cdef class Vectors:
             save_array = lambda arr, file_: xp.save(file_, arr, allow_pickle=False)
         else:
             save_array = lambda arr, file_: xp.save(file_, arr)
+
+        def save_vectors(path):
+            # the source of numpy.save indicates that the file object is closed after use.
+            # but it seems that somehow this does not happen, as ResourceWarnings are raised here.
+            # in order to not rely on this, wrap in context manager.
+            with path.open("wb") as _file:
+                save_array(self.data, _file)
+
         serializers = {
-            "vectors": lambda p: save_array(self.data, p.open("wb")),
+            "vectors": lambda p: save_vectors(p),
             "key2row": lambda p: srsly.write_msgpack(p, self.key2row)
         }
         return util.to_disk(path, serializers, [])
@@ -410,10 +418,11 @@ cdef class Vectors:
                 self.data = ops.xp.load(str(path))
 
         serializers = {
-            "key2row": load_key2row,
-            "keys": load_keys,
             "vectors": load_vectors,
+            "keys": load_keys,
+            "key2row": load_key2row,
         }
+
         util.from_disk(path, serializers, [])
         self._sync_unset()
         return self

spacy/vocab.pyx

@@ -43,7 +43,8 @@ cdef class Vocab:
             vice versa.
         lookups (Lookups): Container for large lookup tables and dictionaries.
         lookups_extra (Lookups): Container for optional lookup tables and dictionaries.
-        name (unicode): Optional name to identify the vectors table.
+        oov_prob (float): Default OOV probability.
+        vectors_name (unicode): Optional name to identify the vectors table.
         RETURNS (Vocab): The newly constructed object.
         """
         lex_attr_getters = lex_attr_getters if lex_attr_getters is not None else {}

website/docs/api/cli.md

@@ -455,7 +455,7 @@ improvement.
 
 ```bash
 $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir]
-[--width] [--depth] [--cnn-window] [--cnn-pieces] [--use-chars] [--sa-depth]
+[--width] [--conv-depth] [--cnn-window] [--cnn-pieces] [--use-chars] [--sa-depth]
 [--embed-rows] [--loss_func] [--dropout] [--batch-size] [--max-length]
 [--min-length]  [--seed] [--n-iter] [--use-vectors] [--n-save-every]
 [--init-tok2vec] [--epoch-start]
@@ -467,7 +467,7 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir]
 | `vectors_model`                                       | positional | Name or path to spaCy model with vectors to learn from.                                                                                                                         |
 | `output_dir`                                          | positional | Directory to write models to on each epoch.                                                                                                                                     |
 | `--width`, `-cw`                                      | option     | Width of CNN layers.                                                                                                                                                            |
-| `--depth`, `-cd`                                      | option     | Depth of CNN layers.                                                                                                                                                            |
+| `--conv-depth`, `-cd`                                 | option     | Depth of CNN layers.                                                                                                                                                            |
 | `--cnn-window`, `-cW` <Tag variant="new">2.2.2</Tag>  | option     | Window size for CNN layers.                                                                                                                                                     |
 | `--cnn-pieces`, `-cP` <Tag variant="new">2.2.2</Tag>  | option     | Maxout size for CNN layers. `1` for [Mish](https://github.com/digantamisra98/Mish).                                                                                             |
 | `--use-chars`, `-chr` <Tag variant="new">2.2.2</Tag>  | flag       | Whether to use character-based embedding.                                                                                                                                       |
@@ -541,16 +541,17 @@ $ python -m spacy init-model [lang] [output_dir] [--jsonl-loc] [--vectors-loc]
 [--prune-vectors]
 ```
 
-| Argument                | Type       | Description                                                                                                                                                                                                                                            |
+| Argument                                                    | Type       | Description                                                                                                                                                                                                                                            |
-| ----------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| ----------------------------------------------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `lang`                  | positional | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`.                                                                                                                                                           |
+| `lang`                                                      | positional | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`.                                                                                                                                                           |
-| `output_dir`            | positional | Model output directory. Will be created if it doesn't exist.                                                                                                                                                                                           |
+| `output_dir`                                                | positional | Model output directory. Will be created if it doesn't exist.                                                                                                                                                                                           |
-| `--jsonl-loc`, `-j`     | option     | Optional location of JSONL-formatted [vocabulary file](/api/annotation#vocab-jsonl) with lexical attributes.                                                                                                                                           |
+| `--jsonl-loc`, `-j`                                         | option     | Optional location of JSONL-formatted [vocabulary file](/api/annotation#vocab-jsonl) with lexical attributes.                                                                                                                                           |
-| `--vectors-loc`, `-v`   | option     | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. |
+| `--vectors-loc`, `-v`                                       | option     | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. |
-| `--truncate-vectors`, `-t` | option  | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation.                                                                                                                                                      |
+| `--truncate-vectors`, `-t` <Tag variant="new">2.3</Tag>     | option     | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation.                                                                                                                                                      |
-| `--prune-vectors`, `-V` | option     | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning.                                                                                                                                                                         |
+| `--prune-vectors`, `-V`                                     | option     | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning.                                                                                                                                                                         |
-| `--vectors-name`, `-vn` | option     | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`.                                                                                                                                                                  |
+| `--vectors-name`, `-vn`                                     | option     | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`.                                                                                                                                                                  |
-| **CREATES**             | model      | A spaCy model containing the vocab and vectors.                                                                                                                                                                                                        |
+| `--omit-extra-lookups`, `-OEL` <Tag variant="new">2.3</Tag> | flag       | Do not include any of the extra lookups tables (`cluster`/`prob`/`sentiment`) from `spacy-lookups-data` in the model.                                                                                                                                  |
+| **CREATES**                                                 | model      | A spaCy model containing the vocab and vectors.                                                                                                                                                                                                        |
 
 ## Evaluate {#evaluate new="2"}
 

website/docs/api/cython-structs.md

@@ -171,9 +171,6 @@ struct.
 | `shape`     | <Abbr title="uint64_t">`attr_t`</Abbr>  | Transform of the lexeme's string, to show orthographic features.                                                           |
 | `prefix`    | <Abbr title="uint64_t">`attr_t`</Abbr>  | Length-N substring from the start of the lexeme. Defaults to `N=1`.                                                        |
 | `suffix`    | <Abbr title="uint64_t">`attr_t`</Abbr>  | Length-N substring from the end of the lexeme. Defaults to `N=3`.                                                          |
-| `cluster`   | <Abbr title="uint64_t">`attr_t`</Abbr>  | Brown cluster ID.                                                                                                          |
-| `prob`      | `float`                                 | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary).                 |
-| `sentiment` | `float`                                 | A scalar value indicating positivity or negativity.                                                                        |
 
 ### Lexeme.get_struct_attr {#lexeme_get_struct_attr tag="staticmethod, nogil" source="spacy/lexeme.pxd"}