mirror of https://github.com/explosion/spaCy.git
Merge branch 'develop' into spacy.io
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Ines Montani
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
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The SCA applies to any contribution that you make to any product or project
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managed by us (the **"project"**), and sets out the intellectual property rights
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you grant to us in the contributed materials. The term **"us"** shall mean
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[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
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**"you"** shall mean the person or entity identified below.
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If you agree to be bound by these terms, fill in the information requested
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below and include the filled-in version with your first pull request, under the
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folder [`.github/contributors/`](/.github/contributors/). The name of the file
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should be your GitHub username, with the extension `.md`. For example, the user
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example_user would create the file `.github/contributors/example_user.md`.
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Read this agreement carefully before signing. These terms and conditions
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constitute a binding legal agreement.
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## Contributor Agreement
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1. The term "contribution" or "contributed materials" means any source code,
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object code, patch, tool, sample, graphic, specification, manual,
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documentation, or any other material posted or submitted by you to the project.
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2. With respect to any worldwide copyrights, or copyright applications and
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registrations, in your contribution:
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* you hereby assign to us joint ownership, and to the extent that such
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assignment is or becomes invalid, ineffective or unenforceable, you hereby
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grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
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royalty-free, unrestricted license to exercise all rights under those
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copyrights. This includes, at our option, the right to sublicense these same
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rights to third parties through multiple levels of sublicensees or other
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licensing arrangements;
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* you agree that each of us can do all things in relation to your
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contribution as if each of us were the sole owners, and if one of us makes
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a derivative work of your contribution, the one who makes the derivative
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work (or has it made will be the sole owner of that derivative work;
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* you agree that you will not assert any moral rights in your contribution
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against us, our licensees or transferees;
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* you agree that we may register a copyright in your contribution and
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exercise all ownership rights associated with it; and
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* you agree that neither of us has any duty to consult with, obtain the
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consent of, pay or render an accounting to the other for any use or
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distribution of your contribution.
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3. With respect to any patents you own, or that you can license without payment
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to any third party, you hereby grant to us a perpetual, irrevocable,
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non-exclusive, worldwide, no-charge, royalty-free license to:
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* make, have made, use, sell, offer to sell, import, and otherwise transfer
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your contribution in whole or in part, alone or in combination with or
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included in any product, work or materials arising out of the project to
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which your contribution was submitted, and
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* at our option, to sublicense these same rights to third parties through
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multiple levels of sublicensees or other licensing arrangements.
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4. Except as set out above, you keep all right, title, and interest in your
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contribution. The rights that you grant to us under these terms are effective
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on the date you first submitted a contribution to us, even if your submission
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took place before the date you sign these terms.
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5. You covenant, represent, warrant and agree that:
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* Each contribution that you submit is and shall be an original work of
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authorship and you can legally grant the rights set out in this SCA;
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* to the best of your knowledge, each contribution will not violate any
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third party's copyrights, trademarks, patents, or other intellectual
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property rights; and
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* each contribution shall be in compliance with U.S. export control laws and
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other applicable export and import laws. You agree to notify us if you
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become aware of any circumstance which would make any of the foregoing
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representations inaccurate in any respect. We may publicly disclose your
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participation in the project, including the fact that you have signed the SCA.
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6. This SCA is governed by the laws of the State of California and applicable
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U.S. Federal law. Any choice of law rules will not apply.
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7. Please place an “x” on one of the applicable statement below. Please do NOT
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mark both statements:
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* [x] I am signing on behalf of myself as an individual and no other person
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or entity, including my employer, has or will have rights with respect to my
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contributions.
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* [ ] I am signing on behalf of my employer or a legal entity and I have the
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actual authority to contractually bind that entity.
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## Contributor Details
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| Field | Entry |
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|------------------------------- | -------------------- |
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| Name | Roshni Biswas |
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| Company name (if applicable) | |
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| Title or role (if applicable) | |
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| Date | 02-17-2019 |
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| GitHub username | roshni-b |
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| Website (optional) | |
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@ -0,0 +1,17 @@
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# coding: utf8
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from __future__ import unicode_literals
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"""
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Example sentences to test spaCy and its language models.
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>>> from spacy.lang.bn.examples import sentences
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>>> docs = nlp.pipe(sentences)
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"""
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sentences = [
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'তুই খুব ভালো',
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'আজ আমরা ডাক্তার দেখতে যাবো',
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'আমি জানি না '
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]
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@ -194,6 +194,14 @@ MORPH_RULES = {
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"Poss": "Yes",
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"Case": "Nom",
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},
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"তাহাার": {
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LEMMA: PRON_LEMMA,
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"Number": "Sing",
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"Person": "Three",
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"PronType": "Prs",
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"Poss": "Yes",
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"Case": "Nom",
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},
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"তোমাদের": {
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LEMMA: PRON_LEMMA,
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"Number": "Plur",
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@ -38,6 +38,7 @@ def test_issue_1971_2(en_vocab):
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@pytest.mark.xfail
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def test_issue_1971_3(en_vocab):
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"""Test that pattern matches correctly for multiple extension attributes."""
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Token.set_extension("a", default=1)
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Token.set_extension("b", default=2)
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doc = Doc(en_vocab, words=["hello", "world"])
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@ -47,3 +48,20 @@ def test_issue_1971_3(en_vocab):
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matches = sorted((en_vocab.strings[m_id], s, e) for m_id, s, e in matcher(doc))
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assert len(matches) == 4
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assert matches == sorted([("A", 0, 1), ("A", 1, 2), ("B", 0, 1), ("B", 1, 2)])
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# @pytest.mark.xfail
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def test_issue_1971_4(en_vocab):
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"""Test that pattern matches correctly with multiple extension attribute
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values on a single token.
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"""
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Token.set_extension("ext_a", default="str_a")
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Token.set_extension("ext_b", default="str_b")
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matcher = Matcher(en_vocab)
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doc = Doc(en_vocab, words=["this", "is", "text"])
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pattern = [{"_": {"ext_a": "str_a", "ext_b": "str_b"}}] * 3
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matcher.add("TEST", None, pattern)
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matches = matcher(doc)
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# Interesting: uncommenting this causes a segmentation fault, so there's
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# definitely something going on here
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# assert len(matches) == 1
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# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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import numpy
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from spacy import displacy
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from ..util import get_doc
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@pytest.mark.xfail
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def test_issue3288(en_vocab):
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"""Test that retokenization works correctly via displaCy when punctuation
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is merged onto the preceeding token and tensor is resized."""
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words = ["Hello", "World", "!", "When", "is", "this", "breaking", "?"]
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heads = [1, 0, -1, 1, 0, 1, -2, -3]
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deps = ["intj", "ROOT", "punct", "advmod", "ROOT", "det", "nsubj", "punct"]
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doc = get_doc(en_vocab, words=words, heads=heads, deps=deps)
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doc.tensor = numpy.zeros((len(words), 96), dtype="float32")
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displacy.render(doc)
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# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from spacy.lang.en import English
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@pytest.mark.xfail
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def test_issue3289():
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"""Test that Language.to_bytes handles serializing a pipeline component
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with an uninitialized model."""
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nlp = English()
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nlp.add_pipe(nlp.create_pipe("textcat"))
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bytes_data = nlp.to_bytes()
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new_nlp = English()
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new_nlp.add_pipe(nlp.create_pipe("textcat"))
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new_nlp.from_bytes(bytes_data)
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@ -292,7 +292,7 @@ that they are listed as "User name: {username}". The name itself may contain any
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character, but no whitespace – so you'll know it will be handled as one token.
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```python
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[{'ORTH': 'User'}, {'ORTH': 'name'}, {'ORTH': ':'}, {}]
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[{"ORTH": "User"}, {"ORTH": "name"}, {"ORTH": ":"}, {}]
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```
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### Adding on_match rules {#on_match}
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@ -301,36 +301,34 @@ To move on to a more realistic example, let's say you're working with a large
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corpus of blog articles, and you want to match all mentions of "Google I/O"
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(which spaCy tokenizes as `['Google', 'I', '/', 'O'`]). To be safe, you only
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match on the uppercase versions, in case someone has written it as "Google i/o".
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You also add a second pattern with an added `{IS_DIGIT: True}` token – this will
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make sure you also match on "Google I/O 2017". If your pattern matches, spaCy
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should execute your custom callback function `add_event_ent`.
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```python
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### {executable="true"}
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import spacy
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from spacy.matcher import Matcher
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from spacy.tokens import Span
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nlp = spacy.load("en_core_web_sm")
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matcher = Matcher(nlp.vocab)
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# Get the ID of the 'EVENT' entity type. This is required to set an entity.
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EVENT = nlp.vocab.strings["EVENT"]
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def add_event_ent(matcher, doc, i, matches):
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# Get the current match and create tuple of entity label, start and end.
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# Append entity to the doc's entity. (Don't overwrite doc.ents!)
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match_id, start, end = matches[i]
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entity = (EVENT, start, end)
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entity = Span(doc, start, end, label="EVENT")
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doc.ents += (entity,)
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print(doc[start:end].text, entity)
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print(entity.text)
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matcher.add("GoogleIO", add_event_ent,
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[{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}],
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[{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}, {"IS_DIGIT": True}],)
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doc = nlp(u"This is a text about Google I/O 2015.")
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pattern = [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}]
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matcher.add("GoogleIO", add_event_ent, pattern)
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doc = nlp(u"This is a text about Google I/O.")
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matches = matcher(doc)
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```
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A very similar logic has been implemented in the built-in
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[`EntityRuler`](/api/entityruler) by the way. It also takes care of handling
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overlapping matches, which you would otherwise have to take care of yourself.
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> #### Tip: Visualizing matches
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>
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> When working with entities, you can use [displaCy](/api/top-level#displacy) to
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@ -22,6 +22,43 @@ the changes, see [this table](/usage/v2#incompat) and the notes on
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</Infobox>
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### Serializing the pipeline
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When serializing the pipeline, keep in mind that this will only save out the
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**binary data for the individual components** to allow spaCy to restore them –
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not the entire objects. This is a good thing, because it makes serialization
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safe. But it also means that you have to take care of storing the language name
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and pipeline component names as well, and restoring them separately before you
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can load in the data.
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> #### Saving the model meta
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>
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> The `nlp.meta` attribute is a JSON-serializable dictionary and contains all
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> model meta information, like the language and pipeline, but also author and
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> license information.
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```python
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### Serialize
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bytes_data = nlp.to_bytes()
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lang = nlp.meta["lang"] # "en"
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pipeline = nlp.meta["pipeline"] # ["tagger", "parser", "ner"]
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```
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```python
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### Deserialize
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nlp = spacy.blank(lang)
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for pipe_name in pipeline:
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pipe = nlp.create_pipe(pipe_name)
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nlp.add_pipe(pipe)
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nlp.from_bytes(bytes_data)
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```
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This is also how spaCy does it under the hood when loading a model: it loads the
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model's `meta.json` containing the language and pipeline information,
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initializes the language class, creates and adds the pipeline components and
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_then_ loads in the binary data. You can read more about this process
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[here](/usage/processing-pipelines#pipelines).
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### Using Pickle {#pickle}
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> #### Example
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|
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@ -102,7 +102,7 @@
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{ "code": "te", "name": "Telugu", "example": "ఇది ఒక వాక్యం.", "has_examples": true },
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{ "code": "si", "name": "Sinhala", "example": "මෙය වාක්යයකි.", "has_examples": true },
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{ "code": "ga", "name": "Irish" },
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{ "code": "bn", "name": "Bengali" },
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{ "code": "bn", "name": "Bengali", "has_examples": true },
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{ "code": "hi", "name": "Hindi", "example": "यह एक वाक्य है।", "has_examples": true },
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{ "code": "kn", "name": "Kannada" },
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{ "code": "ta", "name": "Tamil", "has_examples": true },
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