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.github/contributors/isaric.md vendored Normal file
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@ -0,0 +1,106 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Ivan Šarić |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 18.08.2019. |
| GitHub username | isaric |
| Website (optional) | |

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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [ ] 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 | Yanai Elazar |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 14/8/2019 |
| GitHub username | yanaiela |
| Website (optional) | https://yanaiela.github.io |

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.gitignore vendored
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@ -3,6 +3,7 @@ spacy/data/
corpora/
/models/
keys/
*.json.gz
# Website
website/.cache/

16
spacy/_ml.py
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@ -674,14 +674,14 @@ def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
with Model.define_operators({">>": chain, "**": clone}):
# context encoder
tok2vec = Tok2Vec(
width=hidden_width,
embed_size=embed_width,
pretrained_vectors=pretrained_vectors,
cnn_maxout_pieces=cnn_maxout_pieces,
subword_features=True,
conv_depth=conv_depth,
bilstm_depth=0,
)
width=hidden_width,
embed_size=embed_width,
pretrained_vectors=pretrained_vectors,
cnn_maxout_pieces=cnn_maxout_pieces,
subword_features=True,
conv_depth=conv_depth,
bilstm_depth=0,
)
model = (
tok2vec

93
spacy/cli/debug_data.py
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@ -8,7 +8,7 @@ import sys
import srsly
from wasabi import Printer, MESSAGES
from ..gold import GoldCorpus, read_json_object
from ..gold import GoldCorpus
from ..syntax import nonproj
from ..util import load_model, get_lang_class
@ -95,13 +95,19 @@ def debug_data(
corpus = GoldCorpus(train_path, dev_path)
try:
train_docs = list(corpus.train_docs(nlp))
train_docs_unpreprocessed = list(corpus.train_docs_without_preprocessing(nlp))
train_docs_unpreprocessed = list(
corpus.train_docs_without_preprocessing(nlp)
)
except ValueError as e:
loading_train_error_message = "Training data cannot be loaded: {}".format(str(e))
loading_train_error_message = "Training data cannot be loaded: {}".format(
str(e)
)
try:
dev_docs = list(corpus.dev_docs(nlp))
except ValueError as e:
loading_dev_error_message = "Development data cannot be loaded: {}".format(str(e))
loading_dev_error_message = "Development data cannot be loaded: {}".format(
str(e)
)
if loading_train_error_message or loading_dev_error_message:
if loading_train_error_message:
msg.fail(loading_train_error_message)
@ -158,11 +164,15 @@ def debug_data(
)
if gold_train_data["n_misaligned_words"] > 0:
msg.warn(
"{} misaligned tokens in the training data".format(gold_train_data["n_misaligned_words"])
"{} misaligned tokens in the training data".format(
gold_train_data["n_misaligned_words"]
)
)
if gold_dev_data["n_misaligned_words"] > 0:
msg.warn(
"{} misaligned tokens in the dev data".format(gold_dev_data["n_misaligned_words"])
"{} misaligned tokens in the dev data".format(
gold_dev_data["n_misaligned_words"]
)
)
most_common_words = gold_train_data["words"].most_common(10)
msg.text(
@ -184,7 +194,9 @@ def debug_data(
if "ner" in pipeline:
# Get all unique NER labels present in the data
labels = set(label for label in gold_train_data["ner"] if label not in ("O", "-"))
labels = set(
label for label in gold_train_data["ner"] if label not in ("O", "-")
)
label_counts = gold_train_data["ner"]
model_labels = _get_labels_from_model(nlp, "ner")
new_labels = [l for l in labels if l not in model_labels]
@ -222,7 +234,9 @@ def debug_data(
)
if gold_train_data["ws_ents"]:
msg.fail("{} invalid whitespace entity spans".format(gold_train_data["ws_ents"]))
msg.fail(
"{} invalid whitespace entity spans".format(gold_train_data["ws_ents"])
)
has_ws_ents_error = True
for label in new_labels:
@ -323,33 +337,36 @@ def debug_data(
"Found {} sentence{} with an average length of {:.1f} words.".format(
gold_train_data["n_sents"],
"s" if len(train_docs) > 1 else "",
gold_train_data["n_words"] / gold_train_data["n_sents"]
gold_train_data["n_words"] / gold_train_data["n_sents"],
)
)
# profile labels
labels_train = [label for label in gold_train_data["deps"]]
labels_train_unpreprocessed = [label for label in gold_train_unpreprocessed_data["deps"]]
labels_train_unpreprocessed = [
label for label in gold_train_unpreprocessed_data["deps"]
]
labels_dev = [label for label in gold_dev_data["deps"]]
if gold_train_unpreprocessed_data["n_nonproj"] > 0:
msg.info(
"Found {} nonprojective train sentence{}".format(
gold_train_unpreprocessed_data["n_nonproj"],
"s" if gold_train_unpreprocessed_data["n_nonproj"] > 1 else ""
"s" if gold_train_unpreprocessed_data["n_nonproj"] > 1 else "",
)
)
if gold_dev_data["n_nonproj"] > 0:
msg.info(
"Found {} nonprojective dev sentence{}".format(
gold_dev_data["n_nonproj"],
"s" if gold_dev_data["n_nonproj"] > 1 else ""
"s" if gold_dev_data["n_nonproj"] > 1 else "",
)
)
msg.info(
"{} {} in train data".format(
len(labels_train_unpreprocessed), "label" if len(labels_train) == 1 else "labels"
len(labels_train_unpreprocessed),
"label" if len(labels_train) == 1 else "labels",
)
)
msg.info(
@ -373,43 +390,45 @@ def debug_data(
)
has_low_data_warning = True
# rare labels in projectivized train
rare_projectivized_labels = []
for label in gold_train_data["deps"]:
if gold_train_data["deps"][label] <= DEP_LABEL_THRESHOLD and "||" in label:
rare_projectivized_labels.append("{}: {}".format(label, str(gold_train_data["deps"][label])))
rare_projectivized_labels.append(
"{}: {}".format(label, str(gold_train_data["deps"][label]))
)
if len(rare_projectivized_labels) > 0:
msg.warn(
"Low number of examples for {} label{} in the "
"projectivized dependency trees used for training. You may "
"want to projectivize labels such as punct before "
"training in order to improve parser performance.".format(
len(rare_projectivized_labels),
"s" if len(rare_projectivized_labels) > 1 else "")
msg.warn(
"Low number of examples for {} label{} in the "
"projectivized dependency trees used for training. You may "
"want to projectivize labels such as punct before "
"training in order to improve parser performance.".format(
len(rare_projectivized_labels),
"s" if len(rare_projectivized_labels) > 1 else "",
)
msg.warn(
"Projectivized labels with low numbers of examples: "
"{}".format("\n".join(rare_projectivized_labels)),
show=verbose
)
has_low_data_warning = True
)
msg.warn(
"Projectivized labels with low numbers of examples: "
"{}".format("\n".join(rare_projectivized_labels)),
show=verbose,
)
has_low_data_warning = True
# labels only in train
if set(labels_train) - set(labels_dev):
msg.warn(
"The following labels were found only in the train data: "
"{}".format(", ".join(set(labels_train) - set(labels_dev))),
show=verbose
show=verbose,
)
# labels only in dev
if set(labels_dev) - set(labels_train):
msg.warn(
"The following labels were found only in the dev data: " +
", ".join(set(labels_dev) - set(labels_train)),
show=verbose
"The following labels were found only in the dev data: "
+ ", ".join(set(labels_dev) - set(labels_train)),
show=verbose,
)
if has_low_data_warning:
@ -422,8 +441,10 @@ def debug_data(
# multiple root labels
if len(gold_train_unpreprocessed_data["roots"]) > 1:
msg.warn(
"Multiple root labels ({}) ".format(", ".join(gold_train_unpreprocessed_data["roots"])) +
"found in training data. spaCy's parser uses a single root "
"Multiple root labels ({}) ".format(
", ".join(gold_train_unpreprocessed_data["roots"])
)
+ "found in training data. spaCy's parser uses a single root "
"label ROOT so this distinction will not be available."
)
@ -432,14 +453,14 @@ def debug_data(
msg.fail(
"Found {} nonprojective projectivized train sentence{}".format(
gold_train_data["n_nonproj"],
"s" if gold_train_data["n_nonproj"] > 1 else ""
"s" if gold_train_data["n_nonproj"] > 1 else "",
)
)
if gold_train_data["n_cycles"] > 0:
msg.fail(
"Found {} projectivized train sentence{} with cycles".format(
gold_train_data["n_cycles"],
"s" if gold_train_data["n_cycles"] > 1 else ""
"s" if gold_train_data["n_cycles"] > 1 else "",
)
)

12
spacy/cli/evaluate.py
View File

@ -84,12 +84,12 @@ def evaluate(
def render_parses(docs, output_path, model_name="", limit=250, deps=True, ents=True):
docs[0].user_data["title"] = model_name
if ents:
with (output_path / "entities.html").open("w") as file_:
html = displacy.render(docs[:limit], style="ent", page=True)
html = displacy.render(docs[:limit], style="ent", page=True)
with (output_path / "entities.html").open("w", encoding="utf8") as file_:
file_.write(html)
if deps:
with (output_path / "parses.html").open("w") as file_:
html = displacy.render(
docs[:limit], style="dep", page=True, options={"compact": True}
)
html = displacy.render(
docs[:limit], style="dep", page=True, options={"compact": True}
)
with (output_path / "parses.html").open("w", encoding="utf8") as file_:
file_.write(html)

2
spacy/cli/init_model.py
View File

@ -114,7 +114,7 @@ def read_attrs_from_deprecated(freqs_loc, clusters_loc):
probs, _ = read_freqs(freqs_loc)
msg.good("Counted frequencies")
else:
probs, _ = ({}, DEFAULT_OOV_PROB)
probs, _ = ({}, DEFAULT_OOV_PROB) # noqa: F841
if clusters_loc:
with msg.loading("Reading clusters..."):
clusters = read_clusters(clusters_loc)

16
spacy/displacy/render.py
View File

@ -247,6 +247,15 @@ class EntityRenderer(object):
self.direction = DEFAULT_DIR
self.lang = DEFAULT_LANG
template = options.get("template")
if template:
self.ent_template = template
else:
if self.direction == "rtl":
self.ent_template = TPL_ENT_RTL
else:
self.ent_template = TPL_ENT
def render(self, parsed, page=False, minify=False):
"""Render complete markup.
@ -284,6 +293,7 @@ class EntityRenderer(object):
label = span["label"]
start = span["start"]
end = span["end"]
additional_params = span.get("params", {})
entity = escape_html(text[start:end])
fragments = text[offset:start].split("\n")
for i, fragment in enumerate(fragments):
@ -293,10 +303,8 @@ class EntityRenderer(object):
if self.ents is None or label.upper() in self.ents:
color = self.colors.get(label.upper(), self.default_color)
ent_settings = {"label": label, "text": entity, "bg": color}
if self.direction == "rtl":
markup += TPL_ENT_RTL.format(**ent_settings)
else:
markup += TPL_ENT.format(**ent_settings)
ent_settings.update(additional_params)
markup += self.ent_template.format(**ent_settings)
else:
markup += entity
offset = end

1
spacy/errors.py
View File

@ -429,6 +429,7 @@ class Errors(object):
E155 = ("The `nlp` object should have access to pre-trained word vectors, cf. "
"https://spacy.io/usage/models#languages.")
@add_codes
class TempErrors(object):
T003 = ("Resizing pre-trained Tagger models is not currently supported.")

18
spacy/lang/hr/examples.py Normal file
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@ -0,0 +1,18 @@
# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.hr.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Ovo je rečenica.",
"Kako se popravlja auto?",
"Zagreb je udaljen od Ljubljane svega 150 km.",
"Nećete vjerovati što se dogodilo na ovogodišnjem festivalu!",
"Budućnost Apple je upitna nakon dugotrajnog pada vrijednosti dionica firme.",
"Trgovina oružjem predstavlja prijetnju za globalni mir.",
]

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

@ -1,10 +1,8 @@
# encoding: utf8
from __future__ import unicode_literals, print_function
import re
import sys
from .stop_words import STOP_WORDS
from .tag_map import TAG_MAP
from ...attrs import LANG
@ -32,7 +30,7 @@ else:
from typing import NamedTuple
class Morpheme(NamedTuple):
surface = str("")
lemma = str("")
tag = str("")

2
spacy/lang/tokenizer_exceptions.py
View File

@ -109,7 +109,7 @@ for orth in [
emoticons = set(
"""
r"""
:)
:-)
:))

3
spacy/lang/zh/__init__.py
View File

@ -8,6 +8,7 @@ from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .stop_words import STOP_WORDS
from .tag_map import TAG_MAP
class ChineseDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda text: "zh"
@ -45,4 +46,4 @@ class Chinese(Language):
return Doc(self.vocab, words=words, spaces=spaces)
__all__ = ["Chinese"]
__all__ = ["Chinese"]

8
spacy/lang/zh/tag_map.py
View File

@ -1,8 +1,8 @@
# coding: utf8
from __future__ import unicode_literals
from ...symbols import POS, PUNCT, SYM, ADJ, CONJ, CCONJ, NUM, DET, ADV, ADP, X, VERB
from ...symbols import NOUN, PROPN, PART, INTJ, SPACE, PRON, AUX
from ...symbols import POS, PUNCT, ADJ, CONJ, CCONJ, NUM, DET, ADV, ADP, X, VERB
from ...symbols import NOUN, PART, INTJ, PRON
# 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 Google Universal POS tag set.
@ -43,5 +43,5 @@ TAG_MAP = {
"JJ": {POS: ADJ},
"P": {POS: ADP},
"PN": {POS: PRON},
"PU": {POS: PUNCT}
}
"PU": {POS: PUNCT},
}

7
spacy/scorer.py
View File

@ -160,14 +160,15 @@ class Scorer(object):
cand_deps.add((gold_i, gold_head, token.dep_.lower()))
if "-" not in [token[-1] for token in gold.orig_annot]:
# Find all NER labels in gold and doc
ent_labels = set([x[0] for x in gold_ents]
+ [k.label_ for k in doc.ents])
ent_labels = set([x[0] for x in gold_ents] + [k.label_ for k in doc.ents])
# Set up all labels for per type scoring and prepare gold per type
gold_per_ents = {ent_label: set() for ent_label in ent_labels}
for ent_label in ent_labels:
if ent_label not in self.ner_per_ents:
self.ner_per_ents[ent_label] = PRFScore()
gold_per_ents[ent_label].update([x for x in gold_ents if x[0] == ent_label])
gold_per_ents[ent_label].update(
[x for x in gold_ents if x[0] == ent_label]
)
# Find all candidate labels, for all and per type
cand_ents = set()
cand_per_ents = {ent_label: set() for ent_label in ent_labels}

1
spacy/tests/regression/test_issue4002.py
View File

@ -1,7 +1,6 @@
# coding: utf8
from __future__ import unicode_literals
import pytest
from spacy.matcher import PhraseMatcher
from spacy.tokens import Doc

7
spacy/tests/regression/test_issue4104.py
View File

@ -3,12 +3,13 @@ from __future__ import unicode_literals
from ..util import get_doc
def test_issue4104(en_vocab):
"""Test that English lookup lemmatization of spun & dry are correct
expected mapping = {'dry': 'dry', 'spun': 'spin', 'spun-dry': 'spin-dry'}
"""
text = 'dry spun spun-dry'
"""
text = "dry spun spun-dry"
doc = get_doc(en_vocab, [t for t in text.split(" ")])
# using a simple list to preserve order
expected = ['dry', 'spin', 'spin-dry']
expected = ["dry", "spin", "spin-dry"]
assert [token.lemma_ for token in doc] == expected

11
spacy/tests/test_gold.py
View File

@ -6,6 +6,7 @@ from spacy.gold import spans_from_biluo_tags, GoldParse
from spacy.tokens import Doc
import pytest
def test_gold_biluo_U(en_vocab):
words = ["I", "flew", "to", "London", "."]
spaces = [True, True, True, False, True]
@ -32,14 +33,18 @@ def test_gold_biluo_BIL(en_vocab):
tags = biluo_tags_from_offsets(doc, entities)
assert tags == ["O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
def test_gold_biluo_overlap(en_vocab):
words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
spaces = [True, True, True, True, True, False, True]
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
(len("I flew to "), len("I flew to San Francisco"), "LOC")]
entities = [
(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
(len("I flew to "), len("I flew to San Francisco"), "LOC"),
]
with pytest.raises(ValueError):
tags = biluo_tags_from_offsets(doc, entities)
biluo_tags_from_offsets(doc, entities)
def test_gold_biluo_misalign(en_vocab):
words = ["I", "flew", "to", "San", "Francisco", "Valley."]

77
spacy/tests/test_scorer.py
View File

@ -7,67 +7,62 @@ from spacy.scorer import Scorer
from .util import get_doc
test_ner_cardinal = [
[
"100 - 200",
{
"entities": [
[0, 3, "CARDINAL"],
[6, 9, "CARDINAL"]
]
}
]
["100 - 200", {"entities": [[0, 3, "CARDINAL"], [6, 9, "CARDINAL"]]}]
]
test_ner_apple = [
[
"Apple is looking at buying U.K. startup for $1 billion",
{
"entities": [
(0, 5, "ORG"),
(27, 31, "GPE"),
(44, 54, "MONEY"),
]
}
{"entities": [(0, 5, "ORG"), (27, 31, "GPE"), (44, 54, "MONEY")]},
]
]
def test_ner_per_type(en_vocab):
# Gold and Doc are identical
scorer = Scorer()
for input_, annot in test_ner_cardinal:
doc = get_doc(en_vocab, words = input_.split(' '), ents = [[0, 1, 'CARDINAL'], [2, 3, 'CARDINAL']])
gold = GoldParse(doc, entities = annot['entities'])
doc = get_doc(
en_vocab,
words=input_.split(" "),
ents=[[0, 1, "CARDINAL"], [2, 3, "CARDINAL"]],
)
gold = GoldParse(doc, entities=annot["entities"])
scorer.score(doc, gold)
results = scorer.scores
assert results['ents_p'] == 100
assert results['ents_f'] == 100
assert results['ents_r'] == 100
assert results['ents_per_type']['CARDINAL']['p'] == 100
assert results['ents_per_type']['CARDINAL']['f'] == 100
assert results['ents_per_type']['CARDINAL']['r'] == 100
assert results["ents_p"] == 100
assert results["ents_f"] == 100
assert results["ents_r"] == 100
assert results["ents_per_type"]["CARDINAL"]["p"] == 100
assert results["ents_per_type"]["CARDINAL"]["f"] == 100
assert results["ents_per_type"]["CARDINAL"]["r"] == 100
# Doc has one missing and one extra entity
# Entity type MONEY is not present in Doc
scorer = Scorer()
for input_, annot in test_ner_apple:
doc = get_doc(en_vocab, words = input_.split(' '), ents = [[0, 1, 'ORG'], [5, 6, 'GPE'], [6, 7, 'ORG']])
gold = GoldParse(doc, entities = annot['entities'])
doc = get_doc(
en_vocab,
words=input_.split(" "),
ents=[[0, 1, "ORG"], [5, 6, "GPE"], [6, 7, "ORG"]],
)
gold = GoldParse(doc, entities=annot["entities"])
scorer.score(doc, gold)
results = scorer.scores
assert results['ents_p'] == approx(66.66666)
assert results['ents_r'] == approx(66.66666)
assert results['ents_f'] == approx(66.66666)
assert 'GPE' in results['ents_per_type']
assert 'MONEY' in results['ents_per_type']
assert 'ORG' in results['ents_per_type']
assert results['ents_per_type']['GPE']['p'] == 100
assert results['ents_per_type']['GPE']['r'] == 100
assert results['ents_per_type']['GPE']['f'] == 100
assert results['ents_per_type']['MONEY']['p'] == 0
assert results['ents_per_type']['MONEY']['r'] == 0
assert results['ents_per_type']['MONEY']['f'] == 0
assert results['ents_per_type']['ORG']['p'] == 50
assert results['ents_per_type']['ORG']['r'] == 100
assert results['ents_per_type']['ORG']['f'] == approx(66.66666)
assert results["ents_p"] == approx(66.66666)
assert results["ents_r"] == approx(66.66666)
assert results["ents_f"] == approx(66.66666)
assert "GPE" in results["ents_per_type"]
assert "MONEY" in results["ents_per_type"]
assert "ORG" in results["ents_per_type"]
assert results["ents_per_type"]["GPE"]["p"] == 100
assert results["ents_per_type"]["GPE"]["r"] == 100
assert results["ents_per_type"]["GPE"]["f"] == 100
assert results["ents_per_type"]["MONEY"]["p"] == 0
assert results["ents_per_type"]["MONEY"]["r"] == 0
assert results["ents_per_type"]["MONEY"]["f"] == 0
assert results["ents_per_type"]["ORG"]["p"] == 50
assert results["ents_per_type"]["ORG"]["r"] == 100
assert results["ents_per_type"]["ORG"]["f"] == approx(66.66666)

18
website/docs/images/displacy-ent.html
View File

@ -1,18 +0,0 @@
<div class="entities" style="line-height: 2.5; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; font-size: 18px">But
<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Google
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span></mark>is starting from behind. The company made a late push into hardware,
and
<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Apple
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span></mark>’s
<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Siri
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>, available on
<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">iPhones
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>, and
<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Amazon
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span></mark>’s
<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Alexa
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>software, which runs on its
<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Echo
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>and
<mark class="entity" style="background: #bfeeb7; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">Dot
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PRODUCT</span></mark>devices, have clear leads in consumer adoption.</div>

16
website/docs/images/displacy-ent1.html Normal file
View File

@ -0,0 +1,16 @@
<div class="entities" style="line-height: 2.5; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; font-size: 16px">
<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">
Apple
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span>
</mark>
is looking at buying
<mark class="entity" style="background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">
U.K.
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">GPE</span>
</mark>
startup for
<mark class="entity" style="background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">
$1 billion
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">MONEY</span>
</mark>
</div>

18
website/docs/images/displacy-ent2.html Normal file
View File

@ -0,0 +1,18 @@
<div class="entities" style="line-height: 2.5; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; font-size: 18px">
When
<mark class="entity" style="background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">
Sebastian Thrun
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">PERSON</span>
</mark>
started working on self-driving cars at
<mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">
Google
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">ORG</span>
</mark>
in
<mark class="entity" style="background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone">
2007
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem">DATE</span>
</mark>
, few people outside of the company took him seriously.
</div>

4
website/docs/usage/101/_named-entities.md
View File

@ -32,7 +32,7 @@ for ent in doc.ents:
Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
our example sentence and its named entities look like:
import DisplaCyEntHtml from 'images/displacy-ent.html'; import { Iframe } from
import DisplaCyEntHtml from 'images/displacy-ent1.html'; import { Iframe } from
'components/embed'
<Iframe title="displaCy visualization of entities" html={DisplaCyEntHtml} height={450} />
<Iframe title="displaCy visualization of entities" html={DisplaCyEntHtml} height={100} />

11
website/docs/usage/linguistic-features.md
View File

@ -564,19 +564,16 @@ For more details and examples, see the
import spacy
from spacy import displacy
text = """But Google is starting from behind. The company made a late push
into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
software, which runs on its Echo and Dot devices, have clear leads in
consumer adoption."""
text = u"When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously."
nlp = spacy.load("custom_ner_model")
nlp = spacy.load("en_core_web_sm")
doc = nlp(text)
displacy.serve(doc, style="ent")
```
import DisplacyEntHtml from 'images/displacy-ent.html'
import DisplacyEntHtml from 'images/displacy-ent2.html'
<Iframe title="displaCy visualizer for entities" html={DisplacyEntHtml} height={275} />
<Iframe title="displaCy visualizer for entities" html={DisplacyEntHtml} height={180} />
## Tokenization {#tokenization}

26
website/docs/usage/visualizers.md
View File

@ -117,19 +117,16 @@ text.
import spacy
from spacy import displacy
text = """But Google is starting from behind. The company made a late push
into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
software, which runs on its Echo and Dot devices, have clear leads in
consumer adoption."""
text = u"When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously."
nlp = spacy.load("custom_ner_model")
nlp = spacy.load("en_core_web_sm")
doc = nlp(text)
displacy.serve(doc, style="ent")
```
import DisplacyEntHtml from 'images/displacy-ent.html'
import DisplacyEntHtml from 'images/displacy-ent2.html'
<Iframe title="displaCy visualizer for entities" html={DisplacyEntHtml} height={275} />
<Iframe title="displaCy visualizer for entities" html={DisplacyEntHtml} height={180} />
The entity visualizer lets you customize the following `options`:
@ -204,11 +201,14 @@ doc2 = nlp(LONG_NEWS_ARTICLE)
displacy.render(doc2, style="ent")
```
> #### Enabling or disabling Jupyter mode
>
> To explicitly enable or disable "Jupyter mode", you can use the jupyter`
> keyword argument – e.g. to return raw HTML in a notebook, or to force Jupyter
> rendering if auto-detection fails.
<Infobox variant="warning" title="Important note">
To explicitly enable or disable "Jupyter mode", you can use the `jupyter`
keyword argument – e.g. to return raw HTML in a notebook, or to force Jupyter
rendering if auto-detection fails.
</Infobox>
![displaCy visualizer in a Jupyter notebook](../images/displacy_jupyter.jpg)
@ -284,7 +284,7 @@ nlp = spacy.load("en_core_web_sm")
sentences = [u"This is an example.", u"This is another one."]
for sent in sentences:
doc = nlp(sent)
svg = displacy.render(doc, style="dep")
svg = displacy.render(doc, style="dep", jupyter=False)
file_name = '-'.join([w.text for w in doc if not w.is_punct]) + ".svg"
output_path = Path("/images/" + file_name)
output_path.open("w", encoding="utf-8").write(svg)