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106
.github/contributors/bjascob.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 | Brad Jascob |
| Company name (if applicable) | n/a |
| Title or role (if applicable) | Software Engineer |
| Date | 04/25/2019 |
| GitHub username | bjascob |
| Website (optional) | n/a |

2
spacy/cli/evaluate.py
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@ -17,7 +17,7 @@ from .. import displacy
gpu_id=("Use GPU", "option", "g", int),
displacy_path=("Directory to output rendered parses as HTML", "option", "dp", str),
displacy_limit=("Limit of parses to render as HTML", "option", "dl", int),
return_scores=("Return dict containing model scores", "flag", "r", bool),
return_scores=("Return dict containing model scores", "flag", "R", bool),
)
def evaluate(
model,

43
spacy/cli/pretrain.py
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@ -34,7 +34,8 @@ from .. import util
max_length=("Max words per example.", "option", "xw", int),
min_length=("Min words per example.", "option", "nw", int),
seed=("Seed for random number generators", "option", "s", float),
nr_iter=("Number of iterations to pretrain", "option", "i", int),
n_iter=("Number of iterations to pretrain", "option", "i", int),
n_save_every=("Save model every X batches.", "option", "se", int),
)
def pretrain(
texts_loc,
@ -46,11 +47,12 @@ def pretrain(
loss_func="cosine",
use_vectors=False,
dropout=0.2,
nr_iter=1000,
n_iter=1000,
batch_size=3000,
max_length=500,
min_length=5,
seed=0,
n_save_every=None,
):
"""
Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
@ -115,9 +117,26 @@ def pretrain(
msg.divider("Pre-training tok2vec layer")
row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
for epoch in range(nr_iter):
for batch in util.minibatch_by_words(
((text, None) for text in texts), size=batch_size
def _save_model(epoch, is_temp=False):
is_temp_str = ".temp" if is_temp else ""
with model.use_params(optimizer.averages):
with (output_dir / ("model%d%s.bin" % (epoch, is_temp_str))).open(
"wb"
) as file_:
file_.write(model.tok2vec.to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
"epoch_loss": tracker.epoch_loss,
"epoch": epoch,
}
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
for epoch in range(n_iter):
for batch_id, batch in enumerate(
util.minibatch_by_words(((text, None) for text in texts), size=batch_size)
):
docs = make_docs(
nlp,
@ -133,17 +152,9 @@ def pretrain(
msg.row(progress, **row_settings)
if texts_loc == "-" and tracker.words_per_epoch[epoch] >= 10 ** 7:
break
with model.use_params(optimizer.averages):
with (output_dir / ("model%d.bin" % epoch)).open("wb") as file_:
file_.write(model.tok2vec.to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
"epoch_loss": tracker.epoch_loss,
"epoch": epoch,
}
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
if n_save_every and (batch_id % n_save_every == 0):
_save_model(epoch, is_temp=True)
_save_model(epoch)
tracker.epoch_loss = 0.0
if texts_loc != "-":
# Reshuffle the texts if texts were loaded from a file

45
spacy/cli/train.py
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@ -35,7 +35,12 @@ from .. import about
pipeline=("Comma-separated names of pipeline components", "option", "p", str),
vectors=("Model to load vectors from", "option", "v", str),
n_iter=("Number of iterations", "option", "n", int),
early_stopping_iter=("Maximum number of training epochs without dev accuracy improvement", "option", "e", int),
n_early_stopping=(
"Maximum number of training epochs without dev accuracy improvement",
"option",
"ne",
int,
),
n_examples=("Number of examples", "option", "ns", int),
use_gpu=("Use GPU", "option", "g", int),
version=("Model version", "option", "V", str),
@ -75,7 +80,7 @@ def train(
pipeline="tagger,parser,ner",
vectors=None,
n_iter=30,
early_stopping_iter=None,
n_early_stopping=None,
n_examples=0,
use_gpu=-1,
version="0.0.0",
@ -226,7 +231,7 @@ def train(
msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
try:
iter_since_best = 0
best_score = 0.
best_score = 0.0
for i in range(n_iter):
train_docs = corpus.train_docs(
nlp, noise_level=noise_level, gold_preproc=gold_preproc, max_length=0
@ -335,17 +340,23 @@ def train(
gpu_wps=gpu_wps,
)
msg.row(progress, **row_settings)
# early stopping
if early_stopping_iter is not None:
# Early stopping
if n_early_stopping is not None:
current_score = _score_for_model(meta)
if current_score < best_score:
iter_since_best += 1
else:
iter_since_best = 0
best_score = current_score
if iter_since_best >= early_stopping_iter:
msg.text("Early stopping, best iteration is: {}".format(i-iter_since_best))
msg.text("Best score = {}; Final iteration score = {}".format(best_score, current_score))
if iter_since_best >= n_early_stopping:
msg.text(
"Early stopping, best iteration "
"is: {}".format(i - iter_since_best)
)
msg.text(
"Best score = {}; Final iteration "
"score = {}".format(best_score, current_score)
)
break
finally:
with nlp.use_params(optimizer.averages):
@ -356,19 +367,21 @@ def train(
best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
msg.good("Created best model", best_model_path)
def _score_for_model(meta):
""" Returns mean score between tasks in pipeline that can be used for early stopping. """
mean_acc = list()
pipes = meta['pipeline']
acc = meta['accuracy']
if 'tagger' in pipes:
mean_acc.append(acc['tags_acc'])
if 'parser' in pipes:
mean_acc.append((acc['uas']+acc['las']) / 2)
if 'ner' in pipes:
mean_acc.append((acc['ents_p']+acc['ents_r']+acc['ents_f']) / 3)
pipes = meta["pipeline"]
acc = meta["accuracy"]
if "tagger" in pipes:
mean_acc.append(acc["tags_acc"])
if "parser" in pipes:
mean_acc.append((acc["uas"] + acc["las"]) / 2)
if "ner" in pipes:
mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]) / 3)
return sum(mean_acc) / len(mean_acc)
@contextlib.contextmanager
def _create_progress_bar(total):
if int(os.environ.get("LOG_FRIENDLY", 0)):

7
spacy/displacy/__init__.py
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@ -19,7 +19,7 @@ RENDER_WRAPPER = None
def render(
docs, style="dep", page=False, minify=False, jupyter=False, options={}, manual=False
docs, style="dep", page=False, minify=False, jupyter=None, options={}, manual=False
):
"""Render displaCy visualisation.
@ -27,7 +27,7 @@ def render(
style (unicode): Visualisation style, 'dep' or 'ent'.
page (bool): Render markup as full HTML page.
minify (bool): Minify HTML markup.
jupyter (bool): Experimental, use Jupyter's `display()` to output markup.
jupyter (bool): Override Jupyter auto-detection.
options (dict): Visualiser-specific options, e.g. colors.
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
RETURNS (unicode): Rendered HTML markup.
@ -53,7 +53,8 @@ def render(
html = _html["parsed"]
if RENDER_WRAPPER is not None:
html = RENDER_WRAPPER(html)
if jupyter or is_in_jupyter(): # return HTML rendered by IPython display()
if jupyter or (jupyter is None and is_in_jupyter()):
# return HTML rendered by IPython display()
from IPython.core.display import display, HTML
return display(HTML(html))

10
spacy/lang/th/__init__.py
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@ -4,11 +4,13 @@ from __future__ import unicode_literals
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .tag_map import TAG_MAP
from .stop_words import STOP_WORDS
from .norm_exceptions import NORM_EXCEPTIONS
from ...attrs import LANG
from ..norm_exceptions import BASE_NORMS
from ...attrs import LANG, NORM
from ...language import Language
from ...tokens import Doc
from ...util import DummyTokenizer
from ...util import DummyTokenizer, add_lookups
class ThaiTokenizer(DummyTokenizer):
@ -33,7 +35,9 @@ class ThaiTokenizer(DummyTokenizer):
class ThaiDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda _text: "th"
lex_attr_getters[NORM] = add_lookups(
Language.Defaults.lex_attr_getters[NORM], BASE_NORMS, NORM_EXCEPTIONS
)
tokenizer_exceptions = dict(TOKENIZER_EXCEPTIONS)
tag_map = TAG_MAP
stop_words = STOP_WORDS

114
spacy/lang/th/norm_exceptions.py Normal file
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@ -0,0 +1,114 @@
# coding: utf8
from __future__ import unicode_literals
_exc = {
# Conjugation and Diversion invalid to Tonal form (ผันอักษรและเสียงไม่ตรงกับรูปวรรณยุกต์)
"สนุ๊กเกอร์": "สนุกเกอร์",
"โน้ต": "โน้ต",
# Misspelled because of being lazy or hustle (สะกดผิดเพราะขี้เกียจพิมพ์ หรือเร่งรีบ)
"โทสับ": "โทรศัพท์",
"พุ่งนี้": "พรุ่งนี้",
# Strange (ให้ดูแปลกตา)
"ชะมะ": "ใช่ไหม",
"ชิมิ": "ใช่ไหม",
"ชะ": "ใช่ไหม",
"ช่ายมะ": "ใช่ไหม",
"ป่าว": "เปล่า",
"ป่ะ": "เปล่า",
"ปล่าว": "เปล่า",
"คัย": "ใคร",
"ไค": "ใคร",
"คราย": "ใคร",
"เตง": "ตัวเอง",
"ตะเอง": "ตัวเอง",
"รึ": "หรือ",
"เหรอ": "หรือ",
"หรา": "หรือ",
"หรอ": "หรือ",
"ชั้น": "ฉัน",
"ชั้ล": "ฉัน",
"ช้าน": "ฉัน",
"เทอ": "เธอ",
"เทอร์": "เธอ",
"เทอว์": "เธอ",
"แกร": "แก",
"ป๋ม": "ผม",
"บ่องตง": "บอกตรงๆ",
"ถ่ามตง": "ถามตรงๆ",
"ต่อมตง": "ตอบตรงๆ",
"เพิ่ล": "เพื่อน",
"จอบอ": "จอบอ",
"ดั้ย": "ได้",
"ขอบคุง": "ขอบคุณ",
"ยังงัย": "ยังไง",
"Inw": "เทพ",
"uou": "นอน",
"Lกรีeu": "เกรียน",
# Misspelled to express emotions (คำที่สะกดผิดเพื่อแสดงอารมณ์)
"เปงราย": "เป็นอะไร",
"เปนรัย": "เป็นอะไร",
"เปงรัย": "เป็นอะไร",
"เป็นอัลไล": "เป็นอะไร",
"ทามมาย": "ทำไม",
"ทามมัย": "ทำไม",
"จังรุย": "จังเลย",
"จังเยย": "จังเลย",
"จุงเบย": "จังเลย",
"ไม่รู้": "มะรุ",
"เฮ่ย": "เฮ้ย",
"เห้ย": "เฮ้ย",
"น่าร็อค": "น่ารัก",
"น่าร๊าก": "น่ารัก",
"ตั้ลล๊าก": "น่ารัก",
"คือร๊ะ": "คืออะไร",
"โอป่ะ": "โอเคหรือเปล่า",
"น่ามคาน": "น่ารำคาญ",
"น่ามสาร": "น่าสงสาร",
"วงวาร": "สงสาร",
"บับว่า": "แบบว่า",
"อัลไล": "อะไร",
"อิจ": "อิจฉา",
# Reduce rough words or Avoid to software filter (คำที่สะกดผิดเพื่อลดความหยาบของคำ หรืออาจใช้หลีกเลี่ยงการกรองคำหยาบของซอฟต์แวร์)
"กรู": "กู",
"กุ": "กู",
"กรุ": "กู",
"ตู": "กู",
"ตรู": "กู",
"มรึง": "มึง",
"เมิง": "มึง",
"มืง": "มึง",
"มุง": "มึง",
"สาด": "สัตว์",
"สัส": "สัตว์",
"สัก": "สัตว์",
"แสรด": "สัตว์",
"โคโตะ": "โคตร",
"โคด": "โคตร",
"โครต": "โคตร",
"โคตะระ": "โคตร",
"พ่อง": "พ่อมึง",
"แม่เมิง": "แม่มึง",
"เชี่ย": "เหี้ย",
# Imitate words (คำเลียนเสียง โดยส่วนใหญ่จะเพิ่มทัณฑฆาต หรือซ้ำตัวอักษร)
"แอร๊ยย": "อ๊าย",
"อร๊ายยย": "อ๊าย",
"มันส์": "มัน",
"วู๊วววววววว์": "วู้",
# Acronym (แบบคำย่อ)
"หมาลัย": "มหาวิทยาลัย",
"วิดวะ": "วิศวะ",
"สินสาด ": "ศิลปศาสตร์",
"สินกำ ": "ศิลปกรรมศาสตร์",
"เสารีย์ ": "อนุเสาวรีย์ชัยสมรภูมิ",
"เมกา ": "อเมริกา",
"มอไซค์ ": "มอเตอร์ไซค์",
}
NORM_EXCEPTIONS = {}
for string, norm in _exc.items():
NORM_EXCEPTIONS[string] = norm
NORM_EXCEPTIONS[string.title()] = norm

22
website/meta/universe.json
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@ -1316,6 +1316,28 @@
"author_links": {
"github": "oterrier"
}
},
{
"id": "pyInflect",
"slogan": "A python module for word inflections",
"description": "This package uses the [spaCy 2.0 extensions](https://spacy.io/usage/processing-pipelines#extensions) to add word inflections to the system.",
"github": "bjascob/pyInflect",
"pip": "pyinflect",
"code_example": [
"import spacy",
"import pyinflect",
"",
"nlp = spacy.load('en_core_web_sm')",
"doc = nlp('This is an example.')",
"doc[3].tag_ # NN",
"doc[3]._.inflect('NNS') # examples"
],
"author": "Brad Jascob",
"author_links": {
"github": "bjascob"
},
"category": ["pipeline"],
"tags": ["inflection"]
}
],
"categories": [