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Ines Montani 2019-06-02 12:58:24 +02:00
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.github/contributors/munozbravo.md vendored Normal file
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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Germán Muñoz |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2019-06-01 |
| GitHub username | munozbravo |
| Website (optional) | |

2
spacy/lang/es/__init__.py
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@ -4,6 +4,7 @@ from __future__ import unicode_literals
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .tag_map import TAG_MAP
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from .lemmatizer import LOOKUP
from .syntax_iterators import SYNTAX_ITERATORS
@ -16,6 +17,7 @@ from ...util import update_exc, add_lookups
class SpanishDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters.update(LEX_ATTRS)
lex_attr_getters[LANG] = lambda text: "es"
lex_attr_getters[NORM] = add_lookups(
Language.Defaults.lex_attr_getters[NORM], BASE_NORMS

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spacy/lang/es/lex_attrs.py Normal file
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# coding: utf8
from __future__ import unicode_literals
from ...attrs import LIKE_NUM
_num_words = [
"cero",
"uno",
"dos",
"tres",
"cuatro",
"cinco",
"seis",
"siete",
"ocho",
"nueve",
"diez",
"once",
"doce",
"trece",
"catorce",
"quince",
"dieciséis",
"diecisiete",
"dieciocho",
"diecinueve",
"veinte",
"treinta",
"cuarenta",
"cincuenta",
"sesenta",
"setenta",
"ochenta",
"noventa",
"cien",
"mil",
"millón",
"billón",
"trillón",
]
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
if text.lower() in _num_words:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}

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spacy/tests/regression/test_issue3803.py Normal file
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# coding: utf8
from __future__ import unicode_literals
import pytest
from spacy.lang.es import Spanish
def test_issue3803():
"""Test that spanish num-like tokens have True for like_num attribute."""
nlp = Spanish()
text = "2 dos 1000 mil 12 doce"
doc = nlp(text)
assert [t.like_num for t in doc] == [True, True, True, True, True, True]

140
website/meta/universe.json
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@ -1,22 +1,107 @@
{
"resources": [
{
"id": "nlp-architect",
"title": "NLP Architect",
"slogan": "Python lib for exploring Deep NLP & NLU by Intel AI",
"github": "NervanaSystems/nlp-architect",
"pip": "nlp-architect",
"thumb": "https://i.imgur.com/vMideRx.png",
"category": ["standalone", "research"],
"tags": ["pytorch"]
},
{
"id": "NeuroNER",
"title": "NeuroNER",
"slogan": "Named-entity recognition using neural networks",
"github": "Franck-Dernoncourt/NeuroNER",
"pip": "pyneuroner[cpu]",
"code_example": [
"from neuroner import neuromodel",
"nn = neuromodel.NeuroNER(train_model=False, use_pretrained_model=True)"
],
"category": ["ner"],
"tags": ["standalone"]
},
{
"id": "NLPre",
"title": "NLPre",
"slogan": "Natural Language Preprocessing Library for health data and more",
"github": "NIHOPA/NLPre",
"pip": "nlpre",
"code_example": [
"from nlpre import titlecaps, dedash, identify_parenthetical_phrases",
"from nlpre import replace_acronyms, replace_from_dictionary",
"ABBR = identify_parenthetical_phrases()(text)",
"parsers = [dedash(), titlecaps(), replace_acronyms(ABBR),",
" replace_from_dictionary(prefix='MeSH_')]",
"for f in parsers:",
" text = f(text)",
"print(text)"
],
"category": ["scientific"]
},
{
"id": "Chatterbot",
"title": "Chatterbot",
"slogan": "A machine-learning based conversational dialog engine for creating chat bots",
"github": "gunthercox/ChatterBot",
"pip": "chatterbot",
"thumb": "https://i.imgur.com/eyAhwXk.jpg",
"code_example": [
"from chatterbot import ChatBot",
"from chatterbot.trainers import ListTrainer",
"# Create a new chat bot named Charlie",
"chatbot = ChatBot('Charlie')",
"trainer = ListTrainer(chatbot)",
"trainer.train([",
"'Hi, can I help you?',",
"'Sure, I would like to book a flight to Iceland.",
"'Your flight has been booked.'",
"])",
"",
"response = chatbot.get_response('I would like to book a flight.')"
],
"author": "Gunther Cox",
"author_links": {
"github": "gunthercox"
},
"category": ["conversational", "standalone"],
"tags": ["chatbots"]
},
{
"id": "saber",
"title": "saber",
"slogan": "deep-learning based tool for information extraction in the biomedical domain",
"slogan": "Deep-learning based tool for information extraction in the biomedical domain",
"github": "BaderLab/saber",
"pip": "saber",
"thumb": "https://raw.githubusercontent.com/BaderLab/saber/master/docs/img/saber_logo.png",
"code_example": [
">>> from saber.saber import Saber",
">>> saber = Saber()",
">>> saber.load('PRGE')",
"from saber.saber import Saber",
"saber = Saber()",
"saber.load('PRGE')",
"saber.annotate('The phosphorylation of Hdm2 by MK2 promotes the ubiquitination of p53.')"
],
"category": ["research", "biomedical"],
"tags": ["keras"]
"author": "Bader Lab, University of Toronto",
"category": ["scientific"],
"tags": ["keras", "biomedical"]
},
{
"id": "alibi",
"title": "alibi",
"slogan": "Algorithms for monitoring and explaining machine learning models ",
"github": "SeldonIO/alibi",
"pip": "alibi",
"thumb": "https://i.imgur.com/YkzQHRp.png",
"code_example": [
"from alibi.explainers import AnchorTabular",
"explainer = AnchorTabular(predict_fn, feature_names)",
"explainer.fit(X_train)",
"explainer.explain(x)"
],
"author": "Seldon",
"category": ["standalone", "research"]
},
{
"id": "spacymoji",
"slogan": "Emoji handling and meta data as a spaCy pipeline component",
@ -160,7 +245,7 @@
"doc = nlp(my_doc_text)"
],
"author": "tc64",
"author_link": {
"author_links": {
"github": "tc64"
},
"category": ["pipeline"]
@ -363,7 +448,7 @@
"author_links": {
"github": "huggingface"
},
"category": ["standalone", "conversational"],
"category": ["standalone", "conversational", "models"],
"tags": ["coref"]
},
{
@ -555,7 +640,7 @@
"twitter": "allenai_org",
"website": "http://allenai.org"
},
"category": ["models", "research"]
"category": ["scientific", "models", "research"]
},
{
"id": "textacy",
@ -618,7 +703,7 @@
"github": "ahalterman",
"twitter": "ahalterman"
},
"category": ["standalone"]
"category": ["standalone", "scientific"]
},
{
"id": "kindred",
@ -643,7 +728,7 @@
"author_links": {
"github": "jakelever"
},
"category": ["standalone"]
"category": ["standalone", "scientific"]
},
{
"id": "sense2vec",
@ -911,6 +996,23 @@
"author": "Aaron Kramer",
"category": ["courses"]
},
{
"type": "education",
"id": "spacy-course",
"title": "Advanced NLP with spaCy",
"slogan": "spaCy, 2019",
"description": "In this free interactive course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.",
"url": "https://course.spacy.io",
"image": "https://i.imgur.com/JC00pHW.jpg",
"thumb": "https://i.imgur.com/5RXLtrr.jpg",
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines",
"website": "https://ines.io"
},
"category": ["courses"]
},
{
"type": "education",
"id": "video-spacys-ner-model",
@ -1071,7 +1173,7 @@
"github": "ecohealthalliance",
"website": " https://ecohealthalliance.org/"
},
"category": ["research", "standalone"]
"category": ["scientific", "standalone"]
},
{
"id": "self-attentive-parser",
@ -1393,7 +1495,7 @@
"url": "https://github.com/msg-systems/holmes-extractor",
"description": "Holmes is a Python 3 library that supports a number of use cases involving information extraction from English and German texts, including chatbot, structural search, topic matching and supervised document classification.",
"pip": "holmes-extractor",
"category": ["conversational", "research", "standalone"],
"category": ["conversational", "standalone"],
"tags": ["chatbots", "text-processing"],
"code_example": [
"import holmes_extractor as holmes",
@ -1432,6 +1534,11 @@
"title": "Research",
"description": "Frameworks and utilities for developing better NLP models, especially using neural networks"
},
{
"id": "scientific",
"title": "Scientific",
"description": "Frameworks and utilities for scientific text processing"
},
{
"id": "visualizers",
"title": "Visualizers",
@ -1451,6 +1558,11 @@
"id": "standalone",
"title": "Standalone",
"description": "Self-contained libraries or tools that use spaCy under the hood"
},
{
"id": "models",
"title": "Models",
"description": "Third-party pre-trained models for different languages and domains"
}
]
},