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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 | Ranty Baptiste |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 11/06/2019 |
| GitHub username | Azagh3l |
| 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:
* [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 | Gregory Werner |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 5/29/2019 |
| GitHub username | demongolem |
| 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 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 | Breno Faria |
| Company name (if applicable) | IntraFind |
| Title or role (if applicable) | Product Lead |
| Date | 03.06.2019 |
| GitHub username | intrafindBreno |
| Website (optional) | |

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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 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 | Kabir Khan |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2019-04-08 |
| GitHub username | kabirkhan |
| Website (optional) | |

51
bin/train_word_vectors.py
View File

@ -13,23 +13,21 @@ logger = logging.getLogger(__name__)
class Corpus(object):
def __init__(self, directory, min_freq=10):
def __init__(self, directory, nlp):
self.directory = directory
self.counts = PreshCounter()
self.strings = {}
self.min_freq = min_freq
def count_doc(self, doc):
# Get counts for this document
for word in doc:
self.counts.inc(word.orth, 1)
return len(doc)
self.nlp = nlp
def __iter__(self):
for text_loc in iter_dir(self.directory):
with text_loc.open("r", encoding="utf-8") as file_:
text = file_.read()
yield text
# This is to keep the input to the blank model (which doesn't
# sentencize) from being too long. It works particularly well with
# the output of [WikiExtractor](https://github.com/attardi/wikiextractor)
paragraphs = text.split('\n\n')
for par in paragraphs:
yield [word.orth_ for word in self.nlp(par)]
def iter_dir(loc):
@ -62,12 +60,15 @@ def main(
window=5,
size=128,
min_count=10,
nr_iter=2,
nr_iter=5,
):
logging.basicConfig(
format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO
)
nlp = spacy.blank(lang)
corpus = Corpus(in_dir, nlp)
model = Word2Vec(
sentences=corpus,
size=size,
window=window,
min_count=min_count,
@ -75,33 +76,7 @@ def main(
sample=1e-5,
negative=negative,
)
nlp = spacy.blank(lang)
corpus = Corpus(in_dir)
total_words = 0
total_sents = 0
for text_no, text_loc in enumerate(iter_dir(corpus.directory)):
with text_loc.open("r", encoding="utf-8") as file_:
text = file_.read()
total_sents += text.count("\n")
doc = nlp(text)
total_words += corpus.count_doc(doc)
logger.info(
"PROGRESS: at batch #%i, processed %i words, keeping %i word types",
text_no,
total_words,
len(corpus.strings),
)
model.corpus_count = total_sents
model.raw_vocab = defaultdict(int)
for orth, freq in corpus.counts:
if freq >= min_count:
model.raw_vocab[nlp.vocab.strings[orth]] = freq
model.scale_vocab()
model.finalize_vocab()
model.iter = nr_iter
model.train(corpus)
model.save(out_loc)
if __name__ == "__main__":
plac.call(main)

2
requirements.txt
View File

@ -5,7 +5,7 @@ thinc>=7.0.2,<7.1.0
blis>=0.2.2,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.2.0,<1.1.0
srsly>=0.0.5,<1.1.0
srsly>=0.0.6,<1.1.0
# Third party dependencies
numpy>=1.15.0
requests>=2.13.0,<3.0.0

2
setup.py
View File

@ -233,7 +233,7 @@ def setup_package():
"plac<1.0.0,>=0.9.6",
"requests>=2.13.0,<3.0.0",
"wasabi>=0.2.0,<1.1.0",
"srsly>=0.0.5,<1.1.0",
"srsly>=0.0.6,<1.1.0",
'pathlib==1.0.1; python_version < "3.4"',
],
setup_requires=["wheel"],

45
spacy/cli/pretrain.py
View File

@ -13,11 +13,13 @@ from thinc.neural.util import prefer_gpu, get_array_module
from wasabi import Printer
import srsly
from ..errors import Errors
from ..tokens import Doc
from ..attrs import ID, HEAD
from .._ml import Tok2Vec, flatten, chain, create_default_optimizer
from .._ml import masked_language_model
from .. import util
from .train import _load_pretrained_tok2vec
@plac.annotations(
@ -33,9 +35,15 @@ from .. import util
batch_size=("Number of words per training batch", "option", "bs", int),
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),
seed=("Seed for random number generators", "option", "s", int),
n_iter=("Number of iterations to pretrain", "option", "i", int),
n_save_every=("Save model every X batches.", "option", "se", int),
init_tok2vec=(
"Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.",
"option",
"t2v",
Path,
),
)
def pretrain(
texts_loc,
@ -53,6 +61,7 @@ def pretrain(
min_length=5,
seed=0,
n_save_every=None,
init_tok2vec=None,
):
"""
Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
@ -70,6 +79,9 @@ def pretrain(
errors around this need some improvement.
"""
config = dict(locals())
for key in config:
if isinstance(config[key], Path):
config[key] = str(config[key])
msg = Printer()
util.fix_random_seed(seed)
@ -90,6 +102,8 @@ def pretrain(
msg.fail("Input text file doesn't exist", texts_loc, exits=1)
with msg.loading("Loading input texts..."):
texts = list(srsly.read_jsonl(texts_loc))
if not texts:
msg.fail("Input file is empty", texts_loc, exits=1)
msg.good("Loaded input texts")
random.shuffle(texts)
else: # reading from stdin
@ -112,6 +126,10 @@ def pretrain(
subword_features=True, # Set to False for Chinese etc
),
)
# Load in pre-trained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
optimizer = create_default_optimizer(model.ops)
tracker = ProgressTracker(frequency=10000)
msg.divider("Pre-training tok2vec layer")
@ -134,16 +152,18 @@ def pretrain(
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
skip_counter = 0
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(
docs, count = make_docs(
nlp,
[text for (text, _) in batch],
max_length=max_length,
min_length=min_length,
)
skip_counter += count
loss = make_update(
model, docs, optimizer, objective=loss_func, drop=dropout
)
@ -159,6 +179,9 @@ def pretrain(
if texts_loc != "-":
# Reshuffle the texts if texts were loaded from a file
random.shuffle(texts)
if skip_counter > 0:
msg.warn("Skipped {count} empty values".format(count=str(skip_counter)))
msg.good("Successfully finished pretrain")
def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
@ -180,12 +203,24 @@ def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
def make_docs(nlp, batch, min_length, max_length):
docs = []
skip_count = 0
for record in batch:
if not isinstance(record, dict):
raise TypeError(Errors.E137.format(type=type(record), line=record))
if "tokens" in record:
doc = Doc(nlp.vocab, words=record["tokens"])
else:
words = record["tokens"]
if not words:
skip_count += 1
continue
doc = Doc(nlp.vocab, words=words)
elif "text" in record:
text = record["text"]
if not text:
skip_count += 1
continue
doc = nlp.make_doc(text)
else:
raise ValueError(Errors.E138.format(text=record))
if "heads" in record:
heads = record["heads"]
heads = numpy.asarray(heads, dtype="uint64")
@ -193,7 +228,7 @@ def make_docs(nlp, batch, min_length, max_length):
doc = doc.from_array([HEAD], heads)
if len(doc) >= min_length and len(doc) < max_length:
docs.append(doc)
return docs
return docs, skip_count
def get_vectors_loss(ops, docs, prediction, objective="L2"):

6
spacy/errors.py
View File

@ -393,6 +393,12 @@ class Errors(object):
"`nlp.replace_pipe('{name}', nlp.create_pipe('{name}'))`")
E136 = ("This additional feature requires the jsonschema library to be "
"installed:\npip install jsonschema")
E137 = ("Expected 'dict' type, but got '{type}' from '{line}'. Make sure to provide a valid JSON "
"object as input with either the `text` or `tokens` key. For more info, see the docs:\n"
"https://spacy.io/api/cli#pretrain-jsonl")
E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input includes either the "
"`text` or `tokens` key. For more info, see the docs:\n"
"https://spacy.io/api/cli#pretrain-jsonl")
@add_codes

4
spacy/lang/fr/examples.py
View File

@ -11,7 +11,7 @@ Example sentences to test spaCy and its language models.
sentences = [
"Apple cherche à acheter une startup anglaise pour 1 milliard de dollars",
"Apple cherche à acheter une start-up anglaise pour 1 milliard de dollars",
"Les voitures autonomes déplacent la responsabilité de l'assurance vers les constructeurs",
"San Francisco envisage d'interdire les robots coursiers sur les trottoirs",
"Londres est une grande ville du Royaume-Uni",
@ -21,6 +21,6 @@ sentences = [
"Nouvelles attaques de Trump contre le maire de Londres",
"Où es-tu ?",
"Qui est le président de la France ?",
"Où est la capitale des Etats-Unis ?",
"Où est la capitale des États-Unis ?",
"Quand est né Barack Obama ?",
]

6
spacy/lang/fr/lex_attrs.py
View File

@ -8,7 +8,7 @@ _num_words = set(
"""
zero un deux trois quatre cinq six sept huit neuf dix
onze douze treize quatorze quinze seize dix-sept dix-huit dix-neuf
vingt trente quanrante cinquante soixante septante quatre-vingt huitante nonante
vingt trente quarante cinquante soixante soixante-dix septante quatre-vingt huitante quatre-vingt-dix nonante
cent mille mil million milliard billion quadrillion quintillion
sextillion septillion octillion nonillion decillion
""".split()
@ -17,8 +17,8 @@ sextillion septillion octillion nonillion decillion
_ordinal_words = set(
"""
premier deuxième second troisième quatrième cinquième sixième septième huitième neuvième dixième
onzième douzième treizième quatorzième quinzième seizième dix-septième dix-huitième dix-neufième
vingtième trentième quanrantième cinquantième soixantième septantième quatre-vingtième huitantième nonantième
onzième douzième treizième quatorzième quinzième seizième dix-septième dix-huitième dix-neuvième
vingtième trentième quarantième cinquantième soixantième soixante-dixième septantième quatre-vingtième huitantième quatre-vingt-dixième nonantième
centième millième millionnième milliardième billionnième quadrillionnième quintillionnième
sextillionnième septillionnième octillionnième nonillionnième decillionnième
""".split()

4
spacy/matcher/__init__.py
View File

@ -3,6 +3,6 @@ from __future__ import unicode_literals
from .matcher import Matcher
from .phrasematcher import PhraseMatcher
from .dependencymatcher import DependencyTreeMatcher
from .dependencymatcher import DependencyMatcher
__all__ = ["Matcher", "PhraseMatcher", "DependencyTreeMatcher"]
__all__ = ["Matcher", "PhraseMatcher", "DependencyMatcher"]

107
spacy/matcher/dependencymatcher.pyx
View File

@ -12,13 +12,15 @@ from ..tokens.doc cimport Doc
from .matcher import unpickle_matcher
from ..errors import Errors
from libcpp cimport bool
import numpy
DELIMITER = "||"
INDEX_HEAD = 1
INDEX_RELOP = 0
cdef class DependencyTreeMatcher:
cdef class DependencyMatcher:
"""Match dependency parse tree based on pattern rules."""
cdef Pool mem
cdef readonly Vocab vocab
@ -32,11 +34,11 @@ cdef class DependencyTreeMatcher:
cdef public object _tree
def __init__(self, vocab):
"""Create the DependencyTreeMatcher.
"""Create the DependencyMatcher.
vocab (Vocab): The vocabulary object, which must be shared with the
documents the matcher will operate on.
RETURNS (DependencyTreeMatcher): The newly constructed object.
RETURNS (DependencyMatcher): The newly constructed object.
"""
size = 20
self.token_matcher = Matcher(vocab)
@ -199,7 +201,7 @@ cdef class DependencyTreeMatcher:
return (self._callbacks[key], self._patterns[key])
def __call__(self, Doc doc):
matched_trees = []
matched_key_trees = []
matches = self.token_matcher(doc)
for key in list(self._patterns.keys()):
_patterns_list = self._patterns[key]
@ -227,51 +229,36 @@ cdef class DependencyTreeMatcher:
_nodes,_root
)
length = len(_nodes)
if _root in id_to_position:
candidates = id_to_position[_root]
for candidate in candidates:
isVisited = {}
self.dfs(
candidate,
_root,_tree,
id_to_position,
doc,
isVisited,
_node_operator_map
)
# To check if the subtree pattern is completely
# identified. This is a heuristic. This is done to
# reduce the complexity of exponential unordered subtree
# matching. Will give approximate matches in some cases.
if(len(isVisited) == length):
matched_trees.append((key,list(isVisited)))
for i, (ent_id, nodes) in enumerate(matched_trees):
matched_trees = []
self.recurse(_tree,id_to_position,_node_operator_map,0,[],matched_trees)
matched_key_trees.append((key,matched_trees))
for i, (ent_id, nodes) in enumerate(matched_key_trees):
on_match = self._callbacks.get(ent_id)
if on_match is not None:
on_match(self, doc, i, matches)
return matched_trees
return matched_key_trees
def dfs(self,candidate,root,tree,id_to_position,doc,isVisited,_node_operator_map):
if (root in id_to_position and candidate in id_to_position[root]):
# Color the node since it is valid
isVisited[candidate] = True
if root in tree:
for root_child in tree[root]:
if (
candidate in _node_operator_map
and root_child[INDEX_RELOP] in _node_operator_map[candidate]
):
candidate_children = _node_operator_map[candidate][root_child[INDEX_RELOP]]
for candidate_child in candidate_children:
result = self.dfs(
candidate_child.i,
root_child[INDEX_HEAD],
tree,
id_to_position,
doc,
isVisited,
_node_operator_map
)
def recurse(self,tree,id_to_position,_node_operator_map,int patternLength,visitedNodes,matched_trees):
cdef bool isValid;
if(patternLength == len(id_to_position.keys())):
isValid = True
for node in range(patternLength):
if(node in tree):
for idx, (relop,nbor) in enumerate(tree[node]):
computed_nbors = numpy.asarray(_node_operator_map[visitedNodes[node]][relop])
isNbor = False
for computed_nbor in computed_nbors:
if(computed_nbor.i == visitedNodes[nbor]):
isNbor = True
isValid = isValid & isNbor
if(isValid):
matched_trees.append(visitedNodes)
return
allPatternNodes = numpy.asarray(id_to_position[patternLength])
for patternNode in allPatternNodes:
self.recurse(tree,id_to_position,_node_operator_map,patternLength+1,visitedNodes+[patternNode],matched_trees)
# Given a node and an edge operator, to return the list of nodes
# from the doc that belong to node+operator. This is used to store
@ -299,8 +286,8 @@ cdef class DependencyTreeMatcher:
switcher = {
"<": self.dep,
">": self.gov,
">>": self.dep_chain,
"<<": self.gov_chain,
"<<": self.dep_chain,
">>": self.gov_chain,
".": self.imm_precede,
"$+": self.imm_right_sib,
"$-": self.imm_left_sib,
@ -313,7 +300,7 @@ cdef class DependencyTreeMatcher:
return _node_operator_map
def dep(self, doc, node):
return list(doc[node].head)
return [doc[node].head]
def gov(self,doc,node):
return list(doc[node].children)
@ -330,29 +317,29 @@ cdef class DependencyTreeMatcher:
return []
def imm_right_sib(self, doc, node):
for idx in range(list(doc[node].head.children)):
if idx == node - 1:
return [doc[idx]]
for child in list(doc[node].head.children):
if child.i == node - 1:
return [doc[child.i]]
return []
def imm_left_sib(self, doc, node):
for idx in range(list(doc[node].head.children)):
if idx == node + 1:
return [doc[idx]]
for child in list(doc[node].head.children):
if child.i == node + 1:
return [doc[child.i]]
return []
def right_sib(self, doc, node):
candidate_children = []
for idx in range(list(doc[node].head.children)):
if idx < node:
candidate_children.append(doc[idx])
for child in list(doc[node].head.children):
if child.i < node:
candidate_children.append(doc[child.i])
return candidate_children
def left_sib(self, doc, node):
candidate_children = []
for idx in range(list(doc[node].head.children)):
if idx > node:
candidate_children.append(doc[idx])
for child in list(doc[node].head.children):
if child.i > node:
candidate_children.append(doc[child.i])
return candidate_children
def _normalize_key(self, key):

64
spacy/pipeline/entityruler.py
View File

@ -48,6 +48,7 @@ class EntityRuler(object):
self.phrase_patterns = defaultdict(list)
self.matcher = Matcher(nlp.vocab)
self.phrase_matcher = PhraseMatcher(nlp.vocab)
self.ent_id_sep = cfg.get("ent_id_sep", "||")
patterns = cfg.get("patterns")
if patterns is not None:
self.add_patterns(patterns)
@ -84,7 +85,16 @@ class EntityRuler(object):
continue
# check for end - 1 here because boundaries are inclusive
if start not in seen_tokens and end - 1 not in seen_tokens:
new_entities.append(Span(doc, start, end, label=match_id))
if self.ent_ids:
label_ = self.nlp.vocab.strings[match_id]
ent_label, ent_id = self._split_label(label_)
span = Span(doc, start, end, label=ent_label)
if ent_id:
for token in span:
token.ent_id_ = ent_id
else:
span = Span(doc, start, end, label=match_id)
new_entities.append(span)
entities = [
e for e in entities if not (e.start < end and e.end > start)
]
@ -104,6 +114,21 @@ class EntityRuler(object):
all_labels.update(self.phrase_patterns.keys())
return tuple(all_labels)
@property
def ent_ids(self):
"""All entity ids present in the match patterns meta dicts.
RETURNS (set): The string entity ids.
DOCS: https://spacy.io/api/entityruler#labels
"""
all_ent_ids = set()
for l in self.labels:
if self.ent_id_sep in l:
_, ent_id = self._split_label(l)
all_ent_ids.add(ent_id)
return tuple(all_ent_ids)
@property
def patterns(self):
"""Get all patterns that were added to the entity ruler.
@ -115,10 +140,19 @@ class EntityRuler(object):
all_patterns = []
for label, patterns in self.token_patterns.items():
for pattern in patterns:
all_patterns.append({"label": label, "pattern": pattern})
ent_label, ent_id = self._split_label(label)
p = {"label": ent_label, "pattern": pattern}
if ent_id:
p["id"] = ent_id
all_patterns.append(p)
for label, patterns in self.phrase_patterns.items():
for pattern in patterns:
all_patterns.append({"label": label, "pattern": pattern.text})
ent_label, ent_id = self._split_label(label)
p = {"label": ent_label, "pattern": pattern.text}
if ent_id:
p["id"] = ent_id
all_patterns.append(p)
return all_patterns
def add_patterns(self, patterns):
@ -133,6 +167,8 @@ class EntityRuler(object):
"""
for entry in patterns:
label = entry["label"]
if "id" in entry:
label = self._create_label(label, entry["id"])
pattern = entry["pattern"]
if isinstance(pattern, basestring_):
self.phrase_patterns[label].append(self.nlp(pattern))
@ -145,6 +181,28 @@ class EntityRuler(object):
for label, patterns in self.phrase_patterns.items():
self.phrase_matcher.add(label, None, *patterns)
def _split_label(self, label):
"""Split Entity label into ent_label and ent_id if it contains self.ent_id_sep
RETURNS (tuple): ent_label, ent_id
"""
if self.ent_id_sep in label:
ent_label, ent_id = label.rsplit(self.ent_id_sep, 1)
else:
ent_label = label
ent_id = None
return ent_label, ent_id
def _create_label(self, label, ent_id):
"""Join Entity label with ent_id if the pattern has an `id` attribute
RETURNS (str): The ent_label joined with configured `ent_id_sep`
"""
if isinstance(ent_id, basestring_):
label = "{}{}{}".format(label, self.ent_id_sep, ent_id)
return label
def from_bytes(self, patterns_bytes, **kwargs):
"""Load the entity ruler from a bytestring.

3
spacy/pipeline/pipes.pyx
View File

@ -14,6 +14,7 @@ from thinc.misc import LayerNorm
from thinc.neural.util import to_categorical
from thinc.neural.util import get_array_module
from .functions import merge_subtokens
from ..tokens.doc cimport Doc
from ..syntax.nn_parser cimport Parser
from ..syntax.ner cimport BiluoPushDown
@ -1001,7 +1002,7 @@ cdef class DependencyParser(Parser):
@property
def postprocesses(self):
return [nonproj.deprojectivize]
return [nonproj.deprojectivize, merge_subtokens]
def add_multitask_objective(self, target):
if target == "cloze":

51
spacy/tests/matcher/test_matcher_api.py
View File

@ -3,7 +3,7 @@ from __future__ import unicode_literals
import pytest
import re
from spacy.matcher import Matcher, DependencyTreeMatcher
from spacy.matcher import Matcher, DependencyMatcher
from spacy.tokens import Doc, Token
from ..util import get_doc
@ -285,45 +285,44 @@ def deps():
@pytest.fixture
def dependency_tree_matcher(en_vocab):
def dependency_matcher(en_vocab):
def is_brown_yellow(text):
return bool(re.compile(r"brown|yellow|over").match(text))
IS_BROWN_YELLOW = en_vocab.add_flag(is_brown_yellow)
pattern1 = [
{"SPEC": {"NODE_NAME": "fox"}, "PATTERN": {"ORTH": "fox"}},
{
"SPEC": {"NODE_NAME": "q", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},
"PATTERN": {"LOWER": "quick"},
},
{
"SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},
"PATTERN": {IS_BROWN_YELLOW: True},
},
{"SPEC": {"NODE_NAME": "q", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},"PATTERN": {"ORTH": "quick", "DEP": "amod"}},
{"SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">", "NBOR_NAME": "fox"}, "PATTERN": {IS_BROWN_YELLOW: True}},
]
pattern2 = [
{"SPEC": {"NODE_NAME": "jumped"}, "PATTERN": {"ORTH": "jumped"}},
{
"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"},
"PATTERN": {"LOWER": "fox"},
},
{
"SPEC": {"NODE_NAME": "over", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},
"PATTERN": {IS_BROWN_YELLOW: True},
},
{"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}},
{"SPEC": {"NODE_NAME": "quick", "NBOR_RELOP": ".", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}}
]
matcher = DependencyTreeMatcher(en_vocab)
pattern3 = [
{"SPEC": {"NODE_NAME": "jumped"}, "PATTERN": {"ORTH": "jumped"}},
{"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}},
{"SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">>", "NBOR_NAME": "fox"}, "PATTERN": {"ORTH": "brown"}}
]
matcher = DependencyMatcher(en_vocab)
matcher.add("pattern1", None, pattern1)
matcher.add("pattern2", None, pattern2)
matcher.add("pattern3", None, pattern3)
return matcher
def test_dependency_tree_matcher_compile(dependency_tree_matcher):
assert len(dependency_tree_matcher) == 2
def test_dependency_matcher_compile(dependency_matcher):
assert len(dependency_matcher) == 3
def test_dependency_tree_matcher(dependency_tree_matcher, text, heads, deps):
doc = get_doc(dependency_tree_matcher.vocab, text.split(), heads=heads, deps=deps)
matches = dependency_tree_matcher(doc)
assert len(matches) == 2
def test_dependency_matcher(dependency_matcher, text, heads, deps):
doc = get_doc(dependency_matcher.vocab, text.split(), heads=heads, deps=deps)
matches = dependency_matcher(doc)
# assert matches[0][1] == [[3, 1, 2]]
# assert matches[1][1] == [[4, 3, 3]]
# assert matches[2][1] == [[4, 3, 2]]

26
spacy/tests/pipeline/test_entity_ruler.py
View File

@ -19,6 +19,7 @@ def patterns():
{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
]
@ -34,7 +35,7 @@ def add_ent():
def test_entity_ruler_init(nlp, patterns):
ruler = EntityRuler(nlp, patterns=patterns)
assert len(ruler) == len(patterns)
assert len(ruler.labels) == 3
assert len(ruler.labels) == 4
assert "HELLO" in ruler
assert "BYE" in ruler
nlp.add_pipe(ruler)
@ -77,14 +78,33 @@ def test_entity_ruler_existing_complex(nlp, patterns, add_ent):
assert len(doc.ents[1]) == 2
def test_entity_ruler_entity_id(nlp, patterns):
ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
nlp.add_pipe(ruler)
doc = nlp("Apple is a technology company")
assert len(doc.ents) == 1
assert doc.ents[0].label_ == "TECH_ORG"
assert doc.ents[0].ent_id_ == "a1"
def test_entity_ruler_cfg_ent_id_sep(nlp, patterns):
ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True, ent_id_sep="**")
assert "TECH_ORG**a1" in ruler.phrase_patterns
nlp.add_pipe(ruler)
doc = nlp("Apple is a technology company")
assert len(doc.ents) == 1
assert doc.ents[0].label_ == "TECH_ORG"
assert doc.ents[0].ent_id_ == "a1"
def test_entity_ruler_serialize_bytes(nlp, patterns):
ruler = EntityRuler(nlp, patterns=patterns)
assert len(ruler) == len(patterns)
assert len(ruler.labels) == 3
assert len(ruler.labels) == 4
ruler_bytes = ruler.to_bytes()
new_ruler = EntityRuler(nlp)
assert len(new_ruler) == 0
assert len(new_ruler.labels) == 0
new_ruler = new_ruler.from_bytes(ruler_bytes)
assert len(ruler) == len(patterns)
assert len(ruler.labels) == 3
assert len(ruler.labels) == 4

2
spacy/tests/regression/test_issue3803.py
View File

@ -1,8 +1,6 @@
# coding: utf8
from __future__ import unicode_literals
import pytest
from spacy.lang.es import Spanish

23
spacy/tests/regression/test_issue3839.py Normal file
View File

@ -0,0 +1,23 @@
# coding: utf8
from __future__ import unicode_literals
import pytest
from spacy.matcher import Matcher
from spacy.tokens import Doc
@pytest.mark.xfail
def test_issue3839(en_vocab):
"""Test that match IDs returned by the matcher are correct, are in the string """
doc = Doc(en_vocab, words=["terrific", "group", "of", "people"])
matcher = Matcher(en_vocab)
match_id = "PATTERN"
pattern1 = [{"LOWER": "terrific"}, {"OP": "?"}, {"LOWER": "group"}]
pattern2 = [{"LOWER": "terrific"}, {"OP": "?"}, {"OP": "?"}, {"LOWER": "group"}]
matcher.add(match_id, None, pattern1)
matches = matcher(doc)
assert matches[0][0] == en_vocab.strings[match_id]
matcher = Matcher(en_vocab)
matcher.add(match_id, None, pattern2)
matches = matcher(doc)
assert matches[0][0] == en_vocab.strings[match_id]

46
spacy/tests/test_cli.py
View File

@ -1,7 +1,11 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
from spacy.lang.en import English
from spacy.cli.converters import conllu2json
from spacy.cli.pretrain import make_docs
def test_cli_converters_conllu2json():
@ -26,3 +30,45 @@ def test_cli_converters_conllu2json():
assert [t["head"] for t in tokens] == [1, 2, -1, 0]
assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT"]
assert [t["ner"] for t in tokens] == ["O", "B-PER", "L-PER", "O"]
def test_pretrain_make_docs():
nlp = English()
valid_jsonl_text = {"text": "Some text"}
docs, skip_count = make_docs(nlp, [valid_jsonl_text], 1, 10)
assert len(docs) == 1
assert skip_count == 0
valid_jsonl_tokens = {"tokens": ["Some", "tokens"]}
docs, skip_count = make_docs(nlp, [valid_jsonl_tokens], 1, 10)
assert len(docs) == 1
assert skip_count == 0
invalid_jsonl_type = 0
with pytest.raises(TypeError):
make_docs(nlp, [invalid_jsonl_type], 1, 100)
invalid_jsonl_key = {"invalid": "Does not matter"}
with pytest.raises(ValueError):
make_docs(nlp, [invalid_jsonl_key], 1, 100)
empty_jsonl_text = {"text": ""}
docs, skip_count = make_docs(nlp, [empty_jsonl_text], 1, 10)
assert len(docs) == 0
assert skip_count == 1
empty_jsonl_tokens = {"tokens": []}
docs, skip_count = make_docs(nlp, [empty_jsonl_tokens], 1, 10)
assert len(docs) == 0
assert skip_count == 1
too_short_jsonl = {"text": "This text is not long enough"}
docs, skip_count = make_docs(nlp, [too_short_jsonl], 10, 15)
assert len(docs) == 0
assert skip_count == 0
too_long_jsonl = {"text": "This text contains way too much tokens for this test"}
docs, skip_count = make_docs(nlp, [too_long_jsonl], 1, 5)
assert len(docs) == 0
assert skip_count == 0

3
website/docs/api/cli.md
View File

@ -291,7 +291,7 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
| Argument | Type | Description |
| ----------------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `texts_loc` | positional | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"`. [See here](#pretrain-jsonl) for details. |
| `texts_loc` | positional | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"` or tokens as the key `tokens`. [See here](#pretrain-jsonl) for details. |
| `vectors_model` | positional | Name or path to spaCy model with vectors to learn from. |
| `output_dir` | positional | Directory to write models to on each epoch. |
| `--width`, `-cw` | option | Width of CNN layers. |
@ -305,6 +305,7 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
| `--n-iter`, `-i` | option | Number of iterations to pretrain. |
| `--use-vectors`, `-uv` | flag | Whether to use the static vectors as input features. |
| `--n-save_every`, `-se` | option | Save model every X batches. |
| `--init-tok2vec`, `-t2v` <Tag variant="new">2.1</Tag> | option | Path to pretrained weights for the token-to-vector parts of the models. See `spacy pretrain`. Experimental.|
| **CREATES** | weights | The pre-trained weights that can be used to initialize `spacy train`. |
### JSONL format for raw text {#pretrain-jsonl}

7
website/docs/api/tokenizer.md
View File

@ -16,11 +16,14 @@ Create a `Tokenizer`, to create `Doc` objects given unicode text.
> ```python
> # Construction 1
> from spacy.tokenizer import Tokenizer
> from spacy.lang.en import English
> nlp = English()
> tokenizer = Tokenizer(nlp.vocab)
>
> # Construction 2
> from spacy.lang.en import English
> tokenizer = English().Defaults.create_tokenizer(nlp)
> nlp = English()
> tokenizer = nlp.Defaults.create_tokenizer(nlp)
> ```
| Name | Type | Description |
@ -62,7 +65,7 @@ Tokenize a stream of texts.
> ```
| Name | Type | Description |
| ------------ | ----- | -------------------------------------------------------- |
| ------------ | ----- | ---------------------------------------------------------------------------- |
| `texts` | - | A sequence of unicode texts. |
| `batch_size` | int | The number of texts to accumulate in an internal buffer. Defaults to `1000`. |
| **YIELDS** | `Doc` | A sequence of Doc objects, in order. |

2
website/docs/usage/processing-pipelines.md
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@ -41,7 +41,7 @@ components. spaCy then does the following:
`Language` class contains the shared vocabulary, tokenization rules and the
language-specific annotation scheme.
2. Iterate over the **pipeline names** and create each component using
[`create_pipe`](/api/anguage#create_pipe), which looks them up in
[`create_pipe`](/api/language#create_pipe), which looks them up in
`Language.factories`.
3. Add each pipeline component to the pipeline in order, using
[`add_pipe`](/api/language#add_pipe).

22
website/meta/universe.json
View File

@ -1092,7 +1092,7 @@
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"type": "education",
"id": "podcast-nlp-highlights",
"title": "NLP Highlights 78: Where do corpora come from?",
"title": "NLP Highlights #78: Where do corpora come from?",
"slogan": "January 2019",
"description": "Most NLP projects rely crucially on the quality of annotations used for training and evaluating models. In this episode, Matt and Ines of Explosion AI tell us how Prodigy can improve data annotation and model development workflows. Prodigy is an annotation tool implemented as a python library, and it comes with a web application and a command line interface. A developer can define input data streams and design simple annotation interfaces. Prodigy can help break down complex annotation decisions into a series of binary decisions, and it provides easy integration with spaCy models. Developers can specify how models should be modified as new annotations come in in an active learning framework.",
"soundcloud": "559200912",
@ -1107,7 +1107,7 @@
{
"type": "education",
"id": "podcast-init",
"title": "Podcast.__init__ 87: spaCy with Matthew Honnibal",
"title": "Podcast.__init__ #87: spaCy with Matthew Honnibal",
"slogan": "December 2017",
"description": "As the amount of text available on the internet and in businesses continues to increase, the need for fast and accurate language analysis becomes more prominent. This week Matthew Honnibal, the creator of SpaCy, talks about his experiences researching natural language processing and creating a library to make his findings accessible to industry.",
"iframe": "https://www.pythonpodcast.com/wp-content/plugins/podlove-podcasting-plugin-for-wordpress/lib/modules/podlove_web_player/player_v4/dist/share.html?episode=https://www.pythonpodcast.com/?podlove_player4=176",
@ -1123,7 +1123,7 @@
{
"type": "education",
"id": "talk-python-podcast",
"title": "Talk Python 202: Building a software business",
"title": "Talk Python #202: Building a software business",
"slogan": "March 2019",
"description": "One core question around open source is how do you fund it? Well, there is always that PayPal donate button. But that's been a tremendous failure for many projects. Often the go-to answer is consulting. But what if you don't want to trade time for money? You could take things up a notch and change the equation, exchanging value for money. That's what Ines Montani and her co-founder did when they started Explosion AI with spaCy as the foundation.",
"thumb": "https://i.imgur.com/q1twuK8.png",
@ -1151,6 +1151,22 @@
},
"category": ["podcasts"]
},
{
"type": "education",
"id": "analytics-vidhya",
"title": "DataHack Radio #23: The Brains behind spaCy",
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"description": "\"What would you do if you had the chance to pick the brains behind one of the most popular Natural Language Processing (NLP) libraries of our era? A library that has helped usher in the current boom in NLP applications and nurtured tons of NLP scientists? Well – you invite the creators on our popular DataHack Radio podcast and let them do the talking! We are delighted to welcome Ines Montani and Matt Honnibal, the developers of spaCy – a powerful and advanced library for NLP.\"",
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"website": "https://www.analyticsvidhya.com",
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"id": "adam_qas",
"title": "ADAM: Question Answering System",