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Merge remote-tracking branch 'upstream/master' into bugfix/tokenizer-special-cases-matcher
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Adriane Boyd
commit
ccd94809fa
106
.github/contributors/zqianem.md
vendored
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106
.github/contributors/zqianem.md
vendored
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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 | Em Zhan |
|
||||
| Company name (if applicable) | |
|
||||
| Title or role (if applicable) | |
|
||||
| Date | 2019-09-25 |
|
||||
| GitHub username | zqianem |
|
||||
| Website (optional) | |
|
||||
|
|
@ -38,7 +38,7 @@ It's commercial open-source software, released under the MIT license.
|
|||
| [Contribute] | How to contribute to the spaCy project and code base. |
|
||||
|
||||
[spacy 101]: https://spacy.io/usage/spacy-101
|
||||
[new in v2.2]: https://spacy.io/usage/v2-1
|
||||
[new in v2.1]: https://spacy.io/usage/v2-1
|
||||
[usage guides]: https://spacy.io/usage/
|
||||
[api reference]: https://spacy.io/api/
|
||||
[models]: https://spacy.io/models
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# fmt: off
|
||||
__title__ = "spacy"
|
||||
__version__ = "2.2.0.dev8"
|
||||
__version__ = "2.2.0.dev10"
|
||||
__summary__ = "Industrial-strength Natural Language Processing (NLP) in Python"
|
||||
__uri__ = "https://spacy.io"
|
||||
__author__ = "Explosion"
|
||||
|
|
|
|||
|
|
@ -35,6 +35,13 @@ msg = Printer()
|
|||
clusters_loc=("Optional location of brown clusters data", "option", "c", str),
|
||||
vectors_loc=("Optional vectors file in Word2Vec format", "option", "v", str),
|
||||
prune_vectors=("Optional number of vectors to prune to", "option", "V", int),
|
||||
vectors_name=(
|
||||
"Optional name for the word vectors, e.g. en_core_web_lg.vectors",
|
||||
"option",
|
||||
"vn",
|
||||
str,
|
||||
),
|
||||
model_name=("Optional name for the model meta", "option", "mn", str),
|
||||
)
|
||||
def init_model(
|
||||
lang,
|
||||
|
|
@ -44,6 +51,8 @@ def init_model(
|
|||
jsonl_loc=None,
|
||||
vectors_loc=None,
|
||||
prune_vectors=-1,
|
||||
vectors_name=None,
|
||||
model_name=None,
|
||||
):
|
||||
"""
|
||||
Create a new model from raw data, like word frequencies, Brown clusters
|
||||
|
|
@ -75,10 +84,10 @@ def init_model(
|
|||
lex_attrs = read_attrs_from_deprecated(freqs_loc, clusters_loc)
|
||||
|
||||
with msg.loading("Creating model..."):
|
||||
nlp = create_model(lang, lex_attrs)
|
||||
nlp = create_model(lang, lex_attrs, name=model_name)
|
||||
msg.good("Successfully created model")
|
||||
if vectors_loc is not None:
|
||||
add_vectors(nlp, vectors_loc, prune_vectors)
|
||||
add_vectors(nlp, vectors_loc, prune_vectors, vectors_name)
|
||||
vec_added = len(nlp.vocab.vectors)
|
||||
lex_added = len(nlp.vocab)
|
||||
msg.good(
|
||||
|
|
@ -138,7 +147,7 @@ def read_attrs_from_deprecated(freqs_loc, clusters_loc):
|
|||
return lex_attrs
|
||||
|
||||
|
||||
def create_model(lang, lex_attrs):
|
||||
def create_model(lang, lex_attrs, name=None):
|
||||
lang_class = get_lang_class(lang)
|
||||
nlp = lang_class()
|
||||
for lexeme in nlp.vocab:
|
||||
|
|
@ -157,10 +166,12 @@ def create_model(lang, lex_attrs):
|
|||
else:
|
||||
oov_prob = DEFAULT_OOV_PROB
|
||||
nlp.vocab.cfg.update({"oov_prob": oov_prob})
|
||||
if name:
|
||||
nlp.meta["name"] = name
|
||||
return nlp
|
||||
|
||||
|
||||
def add_vectors(nlp, vectors_loc, prune_vectors):
|
||||
def add_vectors(nlp, vectors_loc, prune_vectors, name=None):
|
||||
vectors_loc = ensure_path(vectors_loc)
|
||||
if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
|
||||
nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open("rb")))
|
||||
|
|
@ -181,7 +192,10 @@ def add_vectors(nlp, vectors_loc, prune_vectors):
|
|||
lexeme.is_oov = False
|
||||
if vectors_data is not None:
|
||||
nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
|
||||
nlp.vocab.vectors.name = "%s_model.vectors" % nlp.meta["lang"]
|
||||
if name is None:
|
||||
nlp.vocab.vectors.name = "%s_model.vectors" % nlp.meta["lang"]
|
||||
else:
|
||||
nlp.vocab.vectors.name = name
|
||||
nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
|
||||
if prune_vectors >= 1:
|
||||
nlp.vocab.prune_vectors(prune_vectors)
|
||||
|
|
|
|||
|
|
@ -471,7 +471,16 @@ class Errors(object):
|
|||
"that case.")
|
||||
E166 = ("Can only merge DocBins with the same pre-defined attributes.\n"
|
||||
"Current DocBin: {current}\nOther DocBin: {other}")
|
||||
E167 = ("Tokenizer special cases are not allowed to modify the text. "
|
||||
E167 = ("Unknown morphological feature: '{feat}' ({feat_id}). This can "
|
||||
"happen if the tagger was trained with a different set of "
|
||||
"morphological features. If you're using a pre-trained model, make "
|
||||
"sure that your models are up to date:\npython -m spacy validate")
|
||||
E168 = ("Unknown field: {field}")
|
||||
E169 = ("Can't find module: {module}")
|
||||
E170 = ("Cannot apply transition {name}: invalid for the current state.")
|
||||
E171 = ("Matcher.add received invalid on_match callback argument: expected "
|
||||
"callable or None, but got: {arg_type}")
|
||||
E172 = ("Tokenizer special cases are not allowed to modify the text. "
|
||||
"This would map '{chunk}' to '{orth}' given token attributes "
|
||||
"'{token_attrs}'.")
|
||||
|
||||
|
|
|
|||
|
|
@ -103,6 +103,8 @@ cdef class Matcher:
|
|||
*patterns (list): List of token descriptions.
|
||||
"""
|
||||
errors = {}
|
||||
if on_match is not None and not hasattr(on_match, "__call__"):
|
||||
raise ValueError(Errors.E171.format(arg_type=type(on_match)))
|
||||
for i, pattern in enumerate(patterns):
|
||||
if len(pattern) == 0:
|
||||
raise ValueError(Errors.E012.format(key=key))
|
||||
|
|
|
|||
|
|
@ -197,7 +197,7 @@ cdef class Morphology:
|
|||
cdef attr_t feature
|
||||
for feature in features:
|
||||
if feature != 0 and feature not in self._feat_map.id2feat:
|
||||
raise KeyError("Unknown feature: %s" % self.strings[feature])
|
||||
raise ValueError(Errors.E167.format(feat=self.strings[feature], feat_id=feature))
|
||||
cdef MorphAnalysisC tag
|
||||
tag = create_rich_tag(features)
|
||||
cdef hash_t key = self.insert(tag)
|
||||
|
|
@ -531,7 +531,7 @@ cdef attr_t get_field(const MorphAnalysisC* tag, int field_id) nogil:
|
|||
elif field == Field_VerbType:
|
||||
return tag.verb_type
|
||||
else:
|
||||
raise ValueError("Unknown field: (%d)" % field_id)
|
||||
raise ValueError(Errors.E168.format(field=field_id))
|
||||
|
||||
|
||||
cdef int check_feature(const MorphAnalysisC* tag, attr_t feature) nogil:
|
||||
|
|
@ -726,7 +726,7 @@ cdef int set_feature(MorphAnalysisC* tag,
|
|||
elif field == Field_VerbType:
|
||||
tag.verb_type = value_
|
||||
else:
|
||||
raise ValueError("Unknown feature: %s (%d)" % (FEATURE_NAMES.get(feature), feature))
|
||||
raise ValueError(Errors.E167.format(field=FEATURE_NAMES.get(feature), field_id=feature))
|
||||
|
||||
|
||||
FIELDS = {
|
||||
|
|
|
|||
|
|
@ -96,8 +96,7 @@ cdef class TransitionSystem:
|
|||
|
||||
def apply_transition(self, StateClass state, name):
|
||||
if not self.is_valid(state, name):
|
||||
raise ValueError(
|
||||
"Cannot apply transition {name}: invalid for the current state.".format(name=name))
|
||||
raise ValueError(Errors.E170.format(name=name))
|
||||
action = self.lookup_transition(name)
|
||||
action.do(state.c, action.label)
|
||||
|
||||
|
|
|
|||
|
|
@ -410,3 +410,11 @@ def test_matcher_schema_token_attributes(en_vocab, pattern, text):
|
|||
assert len(matcher) == 1
|
||||
matches = matcher(doc)
|
||||
assert len(matches) == 1
|
||||
|
||||
|
||||
def test_matcher_valid_callback(en_vocab):
|
||||
"""Test that on_match can only be None or callable."""
|
||||
matcher = Matcher(en_vocab)
|
||||
with pytest.raises(ValueError):
|
||||
matcher.add("TEST", [], [{"TEXT": "test"}])
|
||||
matcher(Doc(en_vocab, words=["test"]))
|
||||
|
|
|
|||
|
|
@ -558,7 +558,7 @@ cdef class Tokenizer:
|
|||
attrs = [intify_attrs(spec, _do_deprecated=True) for spec in substrings]
|
||||
orth = "".join([spec[ORTH] for spec in attrs])
|
||||
if chunk != orth:
|
||||
raise ValueError(Errors.E167.format(chunk=chunk, orth=orth, token_attrs=substrings))
|
||||
raise ValueError(Errors.E172.format(chunk=chunk, orth=orth, token_attrs=substrings))
|
||||
|
||||
def add_special_case(self, unicode string, substrings):
|
||||
"""Add a special-case tokenization rule.
|
||||
|
|
|
|||
|
|
@ -136,7 +136,7 @@ def load_language_data(path):
|
|||
|
||||
def get_module_path(module):
|
||||
if not hasattr(module, "__module__"):
|
||||
raise ValueError("Can't find module {}".format(repr(module)))
|
||||
raise ValueError(Errors.E169.format(module=repr(module)))
|
||||
return Path(sys.modules[module.__module__].__file__).parent
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -63,7 +63,7 @@ cdef class Vectors:
|
|||
shape (tuple): Size of the table, as (# entries, # columns)
|
||||
data (numpy.ndarray): The vector data.
|
||||
keys (iterable): A sequence of keys, aligned with the data.
|
||||
name (string): A name to identify the vectors table.
|
||||
name (unicode): A name to identify the vectors table.
|
||||
RETURNS (Vectors): The newly created object.
|
||||
|
||||
DOCS: https://spacy.io/api/vectors#init
|
||||
|
|
|
|||
|
|
@ -45,6 +45,7 @@ cdef class Vocab:
|
|||
strings (StringStore): StringStore that maps strings to integers, and
|
||||
vice versa.
|
||||
lookups (Lookups): Container for large lookup tables and dictionaries.
|
||||
name (unicode): Optional name to identify the vectors table.
|
||||
RETURNS (Vocab): The newly constructed object.
|
||||
"""
|
||||
lex_attr_getters = lex_attr_getters if lex_attr_getters is not None else {}
|
||||
|
|
|
|||
|
|
@ -538,6 +538,7 @@ $ python -m spacy init-model [lang] [output_dir] [--jsonl-loc] [--vectors-loc]
|
|||
| `--jsonl-loc`, `-j` | option | Optional location of JSONL-formatted [vocabulary file](/api/annotation#vocab-jsonl) with lexical attributes. |
|
||||
| `--vectors-loc`, `-v` | option | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. |
|
||||
| `--prune-vectors`, `-V` | flag | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. |
|
||||
| `--vectors-name`, `-vn` | option | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. |
|
||||
| **CREATES** | model | A spaCy model containing the vocab and vectors. |
|
||||
|
||||
## Evaluate {#evaluate new="2"}
|
||||
|
|
|
|||
|
|
@ -35,6 +35,7 @@ you can add vectors to later.
|
|||
| `data` | `ndarray[ndim=1, dtype='float32']` | The vector data. |
|
||||
| `keys` | iterable | A sequence of keys aligned with the data. |
|
||||
| `shape` | tuple | Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. |
|
||||
| `name` | unicode | A name to identify the vectors table. |
|
||||
| **RETURNS** | `Vectors` | The newly created object. |
|
||||
|
||||
## Vectors.\_\_getitem\_\_ {#getitem tag="method"}
|
||||
|
|
@ -211,7 +212,7 @@ Iterate over `(key, vector)` pairs, in order.
|
|||
| ---------- | ----- | -------------------------------- |
|
||||
| **YIELDS** | tuple | `(key, vector)` pairs, in order. |
|
||||
|
||||
## Vectors.find (#find tag="method")
|
||||
## Vectors.find {#find tag="method"}
|
||||
|
||||
Look up one or more keys by row, or vice versa.
|
||||
|
||||
|
|
|
|||
|
|
@ -21,13 +21,14 @@ Create the vocabulary.
|
|||
> vocab = Vocab(strings=["hello", "world"])
|
||||
> ```
|
||||
|
||||
| Name | Type | Description |
|
||||
| ------------------ | -------------------- | ------------------------------------------------------------------------------------------------------------------ |
|
||||
| `lex_attr_getters` | dict | A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. |
|
||||
| `tag_map` | dict | A dictionary mapping fine-grained tags to coarse-grained parts-of-speech, and optionally morphological attributes. |
|
||||
| `lemmatizer` | object | A lemmatizer. Defaults to `None`. |
|
||||
| `strings` | `StringStore` / list | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings. |
|
||||
| **RETURNS** | `Vocab` | The newly constructed object. |
|
||||
| Name | Type | Description |
|
||||
| ------------------------------------------- | -------------------- | ------------------------------------------------------------------------------------------------------------------ |
|
||||
| `lex_attr_getters` | dict | A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. |
|
||||
| `tag_map` | dict | A dictionary mapping fine-grained tags to coarse-grained parts-of-speech, and optionally morphological attributes. |
|
||||
| `lemmatizer` | object | A lemmatizer. Defaults to `None`. |
|
||||
| `strings` | `StringStore` / list | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings. |
|
||||
| `vectors_name` <Tag variant="new">2.2</Tag> | unicode | A name to identify the vectors table. |
|
||||
| **RETURNS** | `Vocab` | The newly constructed object. |
|
||||
|
||||
## Vocab.\_\_len\_\_ {#len tag="method"}
|
||||
|
||||
|
|
|
|||
|
|
@ -45,7 +45,7 @@ for token in doc:
|
|||
| for | for | `ADP` | `IN` | `prep` | `xxx` | `True` | `True` |
|
||||
| \$ | \$ | `SYM` | `$` | `quantmod` | `$` | `False` | `False` |
|
||||
| 1 | 1 | `NUM` | `CD` | `compound` | `d` | `False` | `False` |
|
||||
| billion | billion | `NUM` | `CD` | `probj` | `xxxx` | `True` | `False` |
|
||||
| billion | billion | `NUM` | `CD` | `pobj` | `xxxx` | `True` | `False` |
|
||||
|
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> #### Tip: Understanding tags and labels
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>
|
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|
|
|
|||
|
|
@ -432,17 +432,21 @@
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{
|
||||
"id": "neuralcoref",
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"slogan": "State-of-the-art coreference resolution based on neural nets and spaCy",
|
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"description": "This coreference resolution module is based on the super fast [spaCy](https://spacy.io/) parser and uses the neural net scoring model described in [Deep Reinforcement Learning for Mention-Ranking Coreference Models](http://cs.stanford.edu/people/kevclark/resources/clark-manning-emnlp2016-deep.pdf) by Kevin Clark and Christopher D. Manning, EMNLP 2016. With ✨Neuralcoref v2.0, you should now be able to train the coreference resolution system on your own dataset — e.g., another language than English! — **provided you have an annotated dataset**.",
|
||||
"description": "This coreference resolution module is based on the super fast [spaCy](https://spacy.io/) parser and uses the neural net scoring model described in [Deep Reinforcement Learning for Mention-Ranking Coreference Models](http://cs.stanford.edu/people/kevclark/resources/clark-manning-emnlp2016-deep.pdf) by Kevin Clark and Christopher D. Manning, EMNLP 2016. Since ✨Neuralcoref v2.0, you can train the coreference resolution system on your own dataset — e.g., another language than English! — **provided you have an annotated dataset**. Note that to use neuralcoref with spaCy > 2.1.0, you'll have to install neuralcoref from source.",
|
||||
"github": "huggingface/neuralcoref",
|
||||
"thumb": "https://i.imgur.com/j6FO9O6.jpg",
|
||||
"code_example": [
|
||||
"from neuralcoref import Coref",
|
||||
"import spacy",
|
||||
"import neuralcoref",
|
||||
"",
|
||||
"coref = Coref()",
|
||||
"clusters = coref.one_shot_coref(utterances=u\"She loves him.\", context=u\"My sister has a dog.\")",
|
||||
"mentions = coref.get_mentions()",
|
||||
"utterances = coref.get_utterances()",
|
||||
"resolved_utterance_text = coref.get_resolved_utterances()"
|
||||
"nlp = spacy.load('en')",
|
||||
"neuralcoref.add_to_pipe(nlp)",
|
||||
"doc1 = nlp('My sister has a dog. She loves him.')",
|
||||
"print(doc1._.coref_clusters)",
|
||||
"",
|
||||
"doc2 = nlp('Angela lives in Boston. She is quite happy in that city.')",
|
||||
"for ent in doc2.ents:",
|
||||
" print(ent._.coref_cluster)"
|
||||
],
|
||||
"author": "Hugging Face",
|
||||
"author_links": {
|
||||
|
|
@ -735,7 +739,7 @@
|
|||
"slogan": "Use NLP to go beyond vanilla word2vec",
|
||||
"description": "sense2vec ([Trask et. al](https://arxiv.org/abs/1511.06388), 2015) is a nice twist on [word2vec](https://en.wikipedia.org/wiki/Word2vec) that lets you learn more interesting, detailed and context-sensitive word vectors. For an interactive example of the technology, see our [sense2vec demo](https://explosion.ai/demos/sense2vec) that lets you explore semantic similarities across all Reddit comments of 2015.",
|
||||
"github": "explosion/sense2vec",
|
||||
"pip": "sense2vec==1.0.0a0",
|
||||
"pip": "sense2vec==1.0.0a1",
|
||||
"thumb": "https://i.imgur.com/awfdhX6.jpg",
|
||||
"image": "https://explosion.ai/assets/img/demos/sense2vec.png",
|
||||
"url": "https://explosion.ai/demos/sense2vec",
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user