Merge branch 'develop' into spacy.io

.github/contributors/roshni-b.md

@@ -0,0 +1,107 @@
+# 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                           | Roshni Biswas        |
+| Company name (if applicable)   |                      |
+| Title or role (if applicable)  |                      |
+| Date                           | 02-17-2019           |
+| GitHub username                | roshni-b             |
+| Website (optional)             |                      |
+

spacy/lang/bn/examples.py

@@ -0,0 +1,17 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+
+"""
+Example sentences to test spaCy and its language models.
+
+>>> from spacy.lang.bn.examples import sentences
+>>> docs = nlp.pipe(sentences)
+"""
+
+
+sentences = [
+    'তুই খুব ভালো',
+    'আজ আমরা ডাক্তার দেখতে যাবো',
+    'আমি জানি না '
+]

spacy/lang/bn/morph_rules.py

@@ -194,6 +194,14 @@ MORPH_RULES = {
             "Poss": "Yes",
             "Case": "Nom",
         },
+        "তাহাার": {
+            LEMMA: PRON_LEMMA,
+            "Number": "Sing",
+            "Person": "Three",
+            "PronType": "Prs",
+            "Poss": "Yes",
+            "Case": "Nom",
+        },
         "তোমাদের": {
             LEMMA: PRON_LEMMA,
             "Number": "Plur",

spacy/tests/regression/test_issue1971.py

@@ -38,6 +38,7 @@ def test_issue_1971_2(en_vocab):
 
 @pytest.mark.xfail
 def test_issue_1971_3(en_vocab):
+    """Test that pattern matches correctly for multiple extension attributes."""
     Token.set_extension("a", default=1)
     Token.set_extension("b", default=2)
     doc = Doc(en_vocab, words=["hello", "world"])
@@ -47,3 +48,20 @@ def test_issue_1971_3(en_vocab):
     matches = sorted((en_vocab.strings[m_id], s, e) for m_id, s, e in matcher(doc))
     assert len(matches) == 4
     assert matches == sorted([("A", 0, 1), ("A", 1, 2), ("B", 0, 1), ("B", 1, 2)])
+
+
+# @pytest.mark.xfail
+def test_issue_1971_4(en_vocab):
+    """Test that pattern matches correctly with multiple extension attribute
+    values on a single token.
+    """
+    Token.set_extension("ext_a", default="str_a")
+    Token.set_extension("ext_b", default="str_b")
+    matcher = Matcher(en_vocab)
+    doc = Doc(en_vocab, words=["this", "is", "text"])
+    pattern = [{"_": {"ext_a": "str_a", "ext_b": "str_b"}}] * 3
+    matcher.add("TEST", None, pattern)
+    matches = matcher(doc)
+    # Interesting: uncommenting this causes a segmentation fault, so there's
+    # definitely something going on here
+    # assert len(matches) == 1

spacy/tests/regression/test_issue3288.py

@@ -0,0 +1,20 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+import numpy
+from spacy import displacy
+
+from ..util import get_doc
+
+
+@pytest.mark.xfail
+def test_issue3288(en_vocab):
+    """Test that retokenization works correctly via displaCy when punctuation
+    is merged onto the preceeding token and tensor is resized."""
+    words = ["Hello", "World", "!", "When", "is", "this", "breaking", "?"]
+    heads = [1, 0, -1, 1, 0, 1, -2, -3]
+    deps = ["intj", "ROOT", "punct", "advmod", "ROOT", "det", "nsubj", "punct"]
+    doc = get_doc(en_vocab, words=words, heads=heads, deps=deps)
+    doc.tensor = numpy.zeros((len(words), 96), dtype="float32")
+    displacy.render(doc)

spacy/tests/regression/test_issue3289.py

@@ -0,0 +1,17 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+from spacy.lang.en import English
+
+
+@pytest.mark.xfail
+def test_issue3289():
+    """Test that Language.to_bytes handles serializing a pipeline component
+    with an uninitialized model."""
+    nlp = English()
+    nlp.add_pipe(nlp.create_pipe("textcat"))
+    bytes_data = nlp.to_bytes()
+    new_nlp = English()
+    new_nlp.add_pipe(nlp.create_pipe("textcat"))
+    new_nlp.from_bytes(bytes_data)

website/docs/usage/rule-based-matching.md

@@ -292,7 +292,7 @@ that they are listed as "User name: {username}". The name itself may contain any
 character, but no whitespace – so you'll know it will be handled as one token.
 
 ```python
-[{'ORTH': 'User'}, {'ORTH': 'name'}, {'ORTH': ':'}, {}]
+[{"ORTH": "User"}, {"ORTH": "name"}, {"ORTH": ":"}, {}]
 ```
 
 ### Adding on_match rules {#on_match}
@@ -301,36 +301,34 @@ To move on to a more realistic example, let's say you're working with a large
 corpus of blog articles, and you want to match all mentions of "Google I/O"
 (which spaCy tokenizes as `['Google', 'I', '/', 'O'`]). To be safe, you only
 match on the uppercase versions, in case someone has written it as "Google i/o".
-You also add a second pattern with an added `{IS_DIGIT: True}` token – this will
-make sure you also match on "Google I/O 2017". If your pattern matches, spaCy
-should execute your custom callback function `add_event_ent`.
 
 ```python
 ### {executable="true"}
 import spacy
 from spacy.matcher import Matcher
+from spacy.tokens import Span
 
 nlp = spacy.load("en_core_web_sm")
 matcher = Matcher(nlp.vocab)
 
-# Get the ID of the 'EVENT' entity type. This is required to set an entity.
-EVENT = nlp.vocab.strings["EVENT"]
-
 def add_event_ent(matcher, doc, i, matches):
     # Get the current match and create tuple of entity label, start and end.
     # Append entity to the doc's entity. (Don't overwrite doc.ents!)
     match_id, start, end = matches[i]
-    entity = (EVENT, start, end)
+    entity = Span(doc, start, end, label="EVENT")
     doc.ents += (entity,)
-    print(doc[start:end].text, entity)
+    print(entity.text)
 
-matcher.add("GoogleIO", add_event_ent,
-            [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}],
-            [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}, {"IS_DIGIT": True}],)
-doc = nlp(u"This is a text about Google I/O 2015.")
+pattern = [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}]
+matcher.add("GoogleIO", add_event_ent, pattern)
+doc = nlp(u"This is a text about Google I/O.")
 matches = matcher(doc)
 ```
 
+A very similar logic has been implemented in the built-in
+[`EntityRuler`](/api/entityruler) by the way. It also takes care of handling
+overlapping matches, which you would otherwise have to take care of yourself.
+
 > #### Tip: Visualizing matches
 >
 > When working with entities, you can use [displaCy](/api/top-level#displacy) to

website/docs/usage/saving-loading.md

@@ -22,6 +22,43 @@ the changes, see [this table](/usage/v2#incompat) and the notes on
 
 </Infobox>
 
+### Serializing the pipeline
+
+When serializing the pipeline, keep in mind that this will only save out the
+**binary data for the individual components** to allow spaCy to restore them –
+not the entire objects. This is a good thing, because it makes serialization
+safe. But it also means that you have to take care of storing the language name
+and pipeline component names as well, and restoring them separately before you
+can load in the data.
+
+> #### Saving the model meta
+>
+> The `nlp.meta` attribute is a JSON-serializable dictionary and contains all
+> model meta information, like the language and pipeline, but also author and
+> license information.
+
+```python
+### Serialize
+bytes_data = nlp.to_bytes()
+lang = nlp.meta["lang"]  # "en"
+pipeline = nlp.meta["pipeline"]  # ["tagger", "parser", "ner"]
+```
+
+```python
+### Deserialize
+nlp = spacy.blank(lang)
+for pipe_name in pipeline:
+    pipe = nlp.create_pipe(pipe_name)
+    nlp.add_pipe(pipe)
+nlp.from_bytes(bytes_data)
+```
+
+This is also how spaCy does it under the hood when loading a model: it loads the
+model's `meta.json` containing the language and pipeline information,
+initializes the language class, creates and adds the pipeline components and
+_then_ loads in the binary data. You can read more about this process
+[here](/usage/processing-pipelines#pipelines).
+
 ### Using Pickle {#pickle}
 
 > #### Example

website/meta/languages.json

@@ -102,7 +102,7 @@
         { "code": "te", "name": "Telugu", "example": "ఇది ఒక వాక్యం.", "has_examples": true },
         { "code": "si", "name": "Sinhala", "example": "මෙය වාක්‍යයකි.", "has_examples": true },
         { "code": "ga", "name": "Irish" },
-        { "code": "bn", "name": "Bengali" },
+        { "code": "bn", "name": "Bengali", "has_examples": true },
         { "code": "hi", "name": "Hindi", "example": "यह एक वाक्य है।", "has_examples": true },
         { "code": "kn", "name": "Kannada" },
         { "code": "ta", "name": "Tamil", "has_examples": true },