Gensim是一个Python库，用于主题建模，文档索引和大型语料库的相似性检索

Gensim是一个Python库，用于主题建模，文档索引和大型语料库的相似性检索。 目标受众是自然语言处理（NLP）和信息检索（IR）社区。
详细内容 问题 354 同类相比654 发布的版本 4.0.0beta
4.0.0beta
4.0.0beta
Pre-release

@mpenkov mpenkov released this Nov 1, 2020 · 229 commits to develop since this release

4.0.0beta, 2020-10-31

⚠️ Gensim 4.0 contains breaking API changes! See the Migration guide to update your existing Gensim 3.x code and models.

Main highlights

  • Massively optimized popular algorithms the community has grown to love: fastText, word2vec, doc2vec, phrases:

    a. Efficiency

    model 3.8.3
    wall time / peak RAM / throughput    		 4.0.0
    wall time / peak RAM / throughput
    fastText 2.9h / 4.11 GB / 822k words/s 2.3h / 1.26 GB / 914k words/s
    word2vec 1.7h / 0.36 GB / 1685k words/s 1.2h / 0.33 GB / 1762k words/s

    In other words, fastText now needs 3x less RAM (and is faster); word2vec has 2x faster init (and needs less RAM, and is faster); detecting collocation phrases is 2x faster. 4.0 benchmarks.

    b. Robustness. We fixed a bunch of long-standing bugs by refactoring the internal code structure (see 🔴 Bug fixes below)

    c. Simplified OOP model for easier model exports and integration with TensorFlow, PyTorch &co.

    These improvements come to you transparently aka "for free", but see Migration guide for some changes that break the old Gensim 3.x API. Update your code accordingly.

  • Dropped a bunch of externally contributed modules: summarization, pivoted TFIDF normalization, FIXME.

    • Code quality was not up to our standards. Also there was no one to maintain them, answer user questions, support these modules.

      So rather than let them rot, we took the hard decision of removing these contributed modules from Gensim. If anyone's interested in maintaining them please fork into your own repo, they can live happily outside of Gensim.

  • Dropped Python 2. Gensim 4.0 is Py3.6+. Read our Python version support policy.

    • If you still need Python 2 for some reason, stay at Gensim 3.8.3.

  • A new Gensim website – finally! 🙃

So, a major clean-up release overall. We're happy with this tighter, leaner and faster Gensim.

This is the direction we'll keep going forward: less kitchen-sink of "latest academic fad", more focus on robust engineering, targetting common NLP & document similarity use-cases.

Why a pre-release?

This 4.0.0beta pre-release is for users who want the cutting edge performance and bug fixes. Plus users who want to help out, by testing and providing feedback: code, documentation, workflows… Please let us know on the mailing list!

Install the pre-release with:

pip install --pre --upgrade gensim

What will change between this pre-release and a "full" 4.0 release?

Check progress here.

👍 Improvements

📚 Tutorials and docs

🔴 Bug fixes

  • #2891: Fix fastText word-vectors with ngrams off, by @gojomo
  • #2907: Fix doc2vec crash for large sets of doc-vectors, by @gojomo
  • #2899: Fix similarity bug in NMSLIB indexer, by @piskvorky
  • #2899: Fix deprecation warnings in Annoy integration, by @piskvorky
  • #2901: Fix inheritance of WikiCorpus from TextCorpus, by @jenishah
  • #2940; Fix deprecations in SoftCosineSimilarity, by @Witiko
  • #2944: Fix save_facebook_model failure after update-vocab & other initialization streamlining, by @gojomo
  • #2846: Fix for Python 3.9/3.10: remove xml.etree.cElementTree, by @hugovk
  • #2973: phrases.export_phrases() doesn't yield all bigrams
  • #2942: Segfault when training doc2vec

⚠️ Removed functionality & deprecations

  • #6: No more binary wheels for x32 platforms, by menshikh-iv
  • #2899: Renamed overly broad similarities.index to the more appropriate similarities.annoy, by @piskvorky
  • #2958: Remove gensim.summarization subpackage, docs and test data, by @mpenkov
  • #2926: Rename num_words to topn in dtm_coherence, by @MeganStodel
  • #2937: Remove Keras dependency, by @piskvorky
  • Removed all code, methods, attributes and functions marked as deprecated in Gensim 3.8.3.
Assets 2
3.8.3
3.8.3
Latest release

@mpenkov mpenkov released this May 4, 2020 · 5 commits to develop since this release

⚠️ 3.8.x will be the last gensim version to support Py2.7. Starting with 4.0.0, gensim will only support Py3.5 and above

3.8.3, 2020-05-03

This is primarily a bugfix release to bring back Py2.7 compatibility to gensim 3.8.

🔴 Bug fixes

  • Bring back Py27 support (PR #2812, @mpenkov)
  • Fix wrong version reported by setup.py (Issue #2796)
  • Fix missing C extensions (Issues #2794 and #2802)

👍 Improvements

📚 Tutorial and doc improvements

⚠️ Deprecations (will be removed in the next major release)

  • Remove

    • gensim.models.FastText.load_fasttext_format: use load_facebook_vectors to load embeddings only (faster, less CPU/memory usage, does not support training continuation) and load_facebook_model to load full model (slower, more CPU/memory intensive, supports training continuation)
    • gensim.models.wrappers.fasttext (obsoleted by the new native gensim.models.fasttext implementation)
    • gensim.examples
    • gensim.nosy
    • gensim.scripts.word2vec_standalone
    • gensim.scripts.make_wiki_lemma
    • gensim.scripts.make_wiki_online
    • gensim.scripts.make_wiki_online_lemma
    • gensim.scripts.make_wiki_online_nodebug
    • gensim.scripts.make_wiki (all of these obsoleted by the new native gensim.scripts.segment_wiki implementation)
    • "deprecated" functions and attributes

  • Move

    • gensim.scripts.make_wikicorpus ➡ gensim.scripts.make_wiki.py
    • gensim.summarization ➡ gensim.models.summarization
    • gensim.topic_coherence ➡ gensim.models._coherence
    • gensim.utils ➡ gensim.utils.utils (old imports will continue to work)
    • gensim.parsing.* ➡ gensim.utils.text_utils
Assets 2
3.8.2
3.8.2
Latest release

@mpenkov mpenkov released this Apr 12, 2020 · 52 commits to develop since this release

3.8.2, 2020-04-10

🔴 Bug fixes

  • Pin smart_open version for compatibility with Py2.7

⚠️ Deprecations (will be removed in the next major release)

  • Remove

    • gensim.models.FastText.load_fasttext_format: use load_facebook_vectors to load embeddings only (faster, less CPU/memory usage, does not support training continuation) and load_facebook_model to load full model (slower, more CPU/memory intensive, supports training continuation)
    • gensim.models.wrappers.fasttext (obsoleted by the new native gensim.models.fasttext implementation)
    • gensim.examples
    • gensim.nosy
    • gensim.scripts.word2vec_standalone
    • gensim.scripts.make_wiki_lemma
    • gensim.scripts.make_wiki_online
    • gensim.scripts.make_wiki_online_lemma
    • gensim.scripts.make_wiki_online_nodebug
    • gensim.scripts.make_wiki (all of these obsoleted by the new native gensim.scripts.segment_wiki implementation)
    • "deprecated" functions and attributes

  • Move

    • gensim.scripts.make_wikicorpus ➡ gensim.scripts.make_wiki.py
    • gensim.summarization ➡ gensim.models.summarization
    • gensim.topic_coherence ➡ gensim.models._coherence
    • gensim.utils ➡ gensim.utils.utils (old imports will continue to work)
    • gensim.parsing.* ➡ gensim.utils.text_utils
Assets 2
3.8.1
3.8.1
Latest release

@mpenkov mpenkov released this Sep 26, 2019 · 1 commit to develop since this release

3.8.1, 2019-09-23

🔴 Bug fixes

  • Fix usage of base_dir instead of BASE_DIR in _load_info in downloader. (movb, #2605)
  • Update the version of smart_open in the setup.py file (AMR-KELEG, #2582)
  • Properly handle unicode_errors arg parameter when loading a vocab file (wmtzk, #2570)
  • Catch loading older TfidfModels without smartirs (bnomis, #2559)
  • Fix bug where a module import set up logging, pin doctools for Py2 (piskvorky, #2552)

📚 Tutorial and doc improvements

👍 Improvements

⚠️ Deprecations (will be removed in the next major release)

  • Remove

    • gensim.models.FastText.load_fasttext_format: use load_facebook_vectors to load embeddings only (faster, less CPU/memory usage, does not support training continuation) and load_facebook_model to load full model (slower, more CPU/memory intensive, supports training continuation)
    • gensim.models.wrappers.fasttext (obsoleted by the new native gensim.models.fasttext implementation)
    • gensim.examples
    • gensim.nosy
    • gensim.scripts.word2vec_standalone
    • gensim.scripts.make_wiki_lemma
    • gensim.scripts.make_wiki_online
    • gensim.scripts.make_wiki_online_lemma
    • gensim.scripts.make_wiki_online_nodebug
    • gensim.scripts.make_wiki (all of these obsoleted by the new native gensim.scripts.segment_wiki implementation)
    • "deprecated" functions and attributes

  • Move

    • gensim.scripts.make_wikicorpus ➡ gensim.scripts.make_wiki.py
    • gensim.summarization ➡ gensim.models.summarization
    • gensim.topic_coherence ➡ gensim.models._coherence
    • gensim.utils ➡ gensim.utils.utils (old imports will continue to work)
    • gensim.parsing.* ➡ gensim.utils.text_utils
Assets 2
3.8.0
3.8.0
Latest release

@mpenkov mpenkov released this Jul 9, 2019 · 1 commit to develop since this release

3.8.0, 2019-07-08

⚠️ 3.8.x will be the last Gensim version to support Py2.7. Starting with 4.0.0, Gensim will only support Py3.5 and above

🌟 New Features

  • Enable online training of Poincare models (koiizukag, #2505)
  • Make BM25 more scalable by adding support for generator inputs (saraswatmks, #2479)
  • Allow the Gensim dataset / pre-trained model downloader gensim.downloader to run offline, by introducing a local file cache (mpenkov, #2545)
  • Make the gensim.downloader target directory configurable (mpenkov, #2456)
  • Support fast kNN document similarity search using NMSLIB (masa3141, #2417)

🔴 Bug fixes

  • Fix smart_open deprecation warning globally (itayB, #2530)
  • Fix AppVeyor issues with Windows and Py2 (mpenkov, #2546)
  • Fix topn=0 versus topn=None bug in most_similar, accept topn of any integer type (Witiko, #2497)
  • Fix Python version check (charsyam, #2547)
  • Fix typo in FastText documentation (Guitaricet, #2518)
  • Fix "Market Matrix" to "Matrix Market" typo. (Shooter23, #2513)
  • Fix auto-generated hyperlinks in CHANGELOG.md (mpenkov, #2482)

📚 Tutorial and doc improvements

  • Generate documentation for the gensim.similarities.termsim module (Witiko, #2485)
  • Simplify the Support section in README (piskvorky, #2542)

👍 Improvements

  • Pin sklearn version for Py2, because sklearn dropped py2 support (mpenkov, #2510)

⚠️ Deprecations (will be removed in the next major release)

  • Remove

    • gensim.models.FastText.load_fasttext_format: use load_facebook_vectors to load embeddings only (faster, less CPU/memory usage, does not support training continuation) and load_facebook_model to load full model (slower, more CPU/memory intensive, supports training continuation)
    • gensim.models.wrappers.fasttext (obsoleted by the new native gensim.models.fasttext implementation)
    • gensim.examples
    • gensim.nosy
    • gensim.scripts.word2vec_standalone
    • gensim.scripts.make_wiki_lemma
    • gensim.scripts.make_wiki_online
    • gensim.scripts.make_wiki_online_lemma
    • gensim.scripts.make_wiki_online_nodebug
    • gensim.scripts.make_wiki (all of these obsoleted by the new native gensim.scripts.segment_wiki implementation)
    • "deprecated" functions and attributes

  • Move

    • gensim.scripts.make_wikicorpus ➡ gensim.scripts.make_wiki.py
    • gensim.summarization ➡ gensim.models.summarization
    • gensim.topic_coherence ➡ gensim.models._coherence
    • gensim.utils ➡ gensim.utils.utils (old imports will continue to work)
    • gensim.parsing.* ➡ gensim.utils.text_utils
Assets 2
3.7.3
3.7.3
Latest release

@mpenkov mpenkov released this May 8, 2019 · 3 commits to develop since this release

3.7.3, 2019-05-06

🔴 Bug fixes

📚 Tutorial and doc improvements

👍 Improvements

⚠️ Deprecations (will be removed in the next major release)

  • Remove

    • gensim.models.FastText.load_fasttext_format: use load_facebook_vectors to load embeddings only (faster, less CPU/memory usage, does not support training continuation) and load_facebook_model to load full model (slower, more CPU/memory intensive, supports training continuation)
    • gensim.models.wrappers.fasttext (obsoleted by the new native gensim.models.fasttext implementation)
    • gensim.examples
    • gensim.nosy
    • gensim.scripts.word2vec_standalone
    • gensim.scripts.make_wiki_lemma
    • gensim.scripts.make_wiki_online
    • gensim.scripts.make_wiki_online_lemma
    • gensim.scripts.make_wiki_online_nodebug
    • gensim.scripts.make_wiki (all of these obsoleted by the new native gensim.scripts.segment_wiki implementation)
    • "deprecated" functions and attributes

  • Move

    • gensim.scripts.make_wikicorpus ➡ gensim.scripts.make_wiki.py
    • gensim.summarization ➡ gensim.models.summarization
    • gensim.topic_coherence ➡ gensim.models._coherence
    • gensim.utils ➡ gensim.utils.utils (old imports will continue to work)
    • gensim.parsing.* ➡ gensim.utils.text_utils
Assets 2
3.7.2
3.7.2
Latest release

@mpenkov mpenkov released this Apr 10, 2019 · 2 commits to develop since this release

3.7.2, 2019-04-06

🌟 New Features

  • gensim.models.fasttext.load_facebook_model function: load full model (slower, more CPU/memory intensive, supports training continuation)

    >>> from gensim.test.utils import datapath
>>>
>>> cap_path = datapath("crime-and-punishment.bin")
>>> fb_model = load_facebook_model(cap_path)
>>>
>>> 'landlord' in fb_model.wv.vocab  # Word is out of vocabulary
False
>>> oov_term = fb_model.wv['landlord']
>>>
>>> 'landlady' in fb_model.wv.vocab  # Word is in the vocabulary
True
>>> iv_term = fb_model.wv['landlady']
>>>
>>> new_sent = [['lord', 'of', 'the', 'rings'], ['lord', 'of', 'the', 'flies']]
>>> fb_model.build_vocab(new_sent, update=True)
>>> fb_model.train(sentences=new_sent, total_examples=len(new_sent), epochs=5)

  • gensim.models.fasttext.load_facebook_vectors function: load embeddings only (faster, less CPU/memory usage, does not support training continuation)

    >>> fbkv = load_facebook_vectors(cap_path)
>>>
>>> 'landlord' in fbkv.vocab  # Word is out of vocabulary
False
>>> oov_vector = fbkv['landlord']
>>>
>>> 'landlady' in fbkv.vocab  # Word is in the vocabulary
True
>>> iv_vector = fbkv['landlady']

🔴 Bug fixes

  • Fix unicode error when loading FastText vocabulary (@mpenkov, #2390)
  • Avoid division by zero in fasttext_inner.pyx (@mpenkov, #2404)
  • Avoid incorrect filename inference when loading model (@mpenkov, #2408)
  • Handle invalid unicode when loading native FastText models (@mpenkov, #2411)
  • Avoid divide by zero when calculating vectors for terms with no ngrams (@mpenkov, #2411)

📚 Tutorial and doc improvements

👍 Improvements

⚠️ Changes in FastText behavior

Out-of-vocab word handling

To achieve consistency with the reference implementation from Facebook,
a FastText model will now always report any word, out-of-vocabulary or
not, as being in the model, and always return some vector for any word
looked-up. Specifically:

  1. 'any_word' in ft_model will always return True. Previously, it
    returned True only if the full word was in the vocabulary. (To test if a
    full word is in the known vocabulary, you can consult the wv.vocab
    property: 'any_word' in ft_model.wv.vocab will return False if the full
    word wasn't learned during model training.)
  2. ft_model['any_word'] will always return a vector. Previously, it
    raised KeyError for OOV words when the model had no vectors
    for any ngrams of the word.
  3. If no ngrams from the term are present in the model,
    or when no ngrams could be extracted from the term, a vector pointing
    to the origin will be returned. Previously, a vector of NaN (not a number)
    was returned as a consequence of a divide-by-zero problem.
  4. Models may use more more memory, or take longer for word-vector
    lookup, especially after training on smaller corpuses where the previous
    non-compliant behavior discarded some ngrams from consideration.

Loading models in Facebook .bin format

The gensim.models.FastText.load_fasttext_format function (deprecated) now loads the entire model contained in the .bin file, including the shallow neural network that enables training continuation.
Loading this NN requires more CPU and RAM than previously required.

Since this function is deprecated, consider using one of its alternatives (see below).

Furthermore, you must now pass the full path to the file to load, including the file extension.
Previously, if you specified a model path that ends with anything other than .bin, the code automatically appended .bin to the path before loading the model.
This behavior was confusing, so we removed it.

⚠️ Deprecations (will be removed in the next major release)

  • Remove

    • gensim.models.FastText.load_fasttext_format: use load_facebook_vectors to load embeddings only (faster, less CPU/memory usage, does not support training continuation) and load_facebook_model to load full model (slower, more CPU/memory intensive, supports training continuation)
    • gensim.models.wrappers.fasttext (obsoleted by the new native gensim.models.fasttext implementation)
    • gensim.examples
    • gensim.nosy
    • gensim.scripts.word2vec_standalone
    • gensim.scripts.make_wiki_lemma
    • gensim.scripts.make_wiki_online
    • gensim.scripts.make_wiki_online_lemma
    • gensim.scripts.make_wiki_online_nodebug
    • gensim.scripts.make_wiki (all of these obsoleted by the new native gensim.scripts.segment_wiki implementation)
    • "deprecated" functions and attributes

  • Move

    • gensim.scripts.make_wikicorpus ➡ gensim.scripts.make_wiki.py
    • gensim.summarization ➡ gensim.models.summarization
    • gensim.topic_coherence ➡ gensim.models._coherence
    • gensim.utils ➡ gensim.utils.utils (old imports will continue to work)
    • gensim.parsing.* ➡ gensim.utils.text_utils
Assets 2
3.7.1
3.7.1
Latest release

@menshikh-iv menshikh-iv released this Jan 31, 2019 · 2 commits to develop since this release

Assets 33

3.7.1, 2019-01-31

👍 Improvements

🔴 Bug fixes

  • Fix infinite diff in LdaModel.do_mstep (@horpto, #2344)
  • Fix backward compatibility issue: loading FastTextKeyedVectors using KeyedVectors (missing attribute compatible_hash) (@menshikh-iv, #2349)
  • Fix logging issue (conda-forge related) (@menshikh-iv, #2339)
  • Fix WordEmbeddingsKeyedVectors.most_similar (@Witiko, #2356)
  • Fix issues of flake8==3.7.1 (@horpto, #2365)

📚 Tutorial and doc improvements

⚠️ Deprecations (will be removed in the next major release)

  • Remove

    • gensim.models.wrappers.fasttext (obsoleted by the new native gensim.models.fasttext implementation)
    • gensim.examples
    • gensim.nosy
    • gensim.scripts.word2vec_standalone
    • gensim.scripts.make_wiki_lemma
    • gensim.scripts.make_wiki_online
    • gensim.scripts.make_wiki_online_lemma
    • gensim.scripts.make_wiki_online_nodebug
    • gensim.scripts.make_wiki (all of these obsoleted by the new native gensim.scripts.segment_wiki implementation)
    • "deprecated" functions and attributes

  • Move

    • gensim.scripts.make_wikicorpus ➡ gensim.scripts.make_wiki.py
    • gensim.summarization ➡ gensim.models.summarization
    • gensim.topic_coherence ➡ gensim.models._coherence
    • gensim.utils ➡ gensim.utils.utils (old imports will continue to work)
    • gensim.parsing.* ➡ gensim.utils.text_utils
3.7.0
3.7.0
Latest release

@menshikh-iv menshikh-iv released this Jan 18, 2019 · 1 commit to develop since this release

Assets 2

3.7.0, 2019-01-18

🌟 New features

  • Fast Online NMF (@anotherbugmaster, #2007)

    • Benchmark wiki-english-20171001

      Model Perplexity Coherence L2 norm Train time (minutes)
      LDA 4727.07 -2.514 7.372 138
      NMF 975.74 -2.814 7.265 73
      NMF (with regularization) 985.57 -2.436 7.269 441

    • Simple to use (same interface as LdaModel)

      from gensim.models.nmf import Nmf
from gensim.corpora import Dictionary
import gensim.downloader as api

text8 = api.load('text8')

dictionary = Dictionary(text8)
dictionary.filter_extremes()

corpus = [
    dictionary.doc2bow(doc) for doc in text8
]

nmf = Nmf(
    corpus=corpus,
    num_topics=5,
    id2word=dictionary,
    chunksize=2000,
    passes=5,
    random_state=42,
)

nmf.show_topics()
"""
[(0, '0.007*"km" + 0.006*"est" + 0.006*"islands" + 0.004*"league" + 0.004*"rate" + 0.004*"female" + 0.004*"economy" + 0.003*"male" + 0.003*"team" + 0.003*"elections"'),
 (1, '0.006*"actor" + 0.006*"player" + 0.004*"bwv" + 0.004*"writer" + 0.004*"actress" + 0.004*"singer" + 0.003*"emperor" + 0.003*"jewish" + 0.003*"italian" + 0.003*"prize"'),
 (2, '0.036*"college" + 0.007*"institute" + 0.004*"jewish" + 0.004*"universidad" + 0.003*"engineering" + 0.003*"colleges" + 0.003*"connecticut" + 0.003*"technical" + 0.003*"jews" + 0.003*"universities"'),
 (3, '0.016*"import" + 0.008*"insubstantial" + 0.007*"y" + 0.006*"soviet" + 0.004*"energy" + 0.004*"info" + 0.003*"duplicate" + 0.003*"function" + 0.003*"z" + 0.003*"jargon"'),
 (4, '0.005*"software" + 0.004*"games" + 0.004*"windows" + 0.003*"microsoft" + 0.003*"films" + 0.003*"apple" + 0.003*"video" + 0.002*"album" + 0.002*"fiction" + 0.002*"characters"')]
"""

    • See also:

  • Massive improvement of FastText compatibilities (@mpenkov, #2313)

    from gensim.models import FastText

# 'cc.ru.300.bin' - Russian Facebook FT model trained on Common Crawl
# Can be downloaded from https://s3-us-west-1.amazonaws.com/fasttext-vectors/word-vectors-v2/cc.ru.300.bin.gz

model = FastText.load_fasttext_format("cc.ru.300.bin")

# Fixed hash-function allow to produce same output as FB FastText & works correctly for non-latin languages (for example, Russian)
assert "мяу" in m.wv.vocab  # 'мяу' - vocab word
model.wv.most_similar("мяу")
"""
[('Мяу', 0.6820122003555298),
 ('МЯУ', 0.6373013257980347),
 ('мяу-мяу', 0.593108594417572),
 ('кис-кис', 0.5899622440338135),
 ('гав', 0.5866007804870605),
 ('Кис-кис', 0.5798211097717285),
 ('Кис-кис-кис', 0.5742273330688477),
 ('Мяу-мяу', 0.5699705481529236),
 ('хрю-хрю', 0.5508339405059814),
 ('ав-ав', 0.5479759573936462)]
"""

assert "котогород" not in m.wv.vocab  # 'котогород' - out-of-vocab word
model.wv.most_similar("котогород", topn=3)
"""
[('автогород', 0.5463314652442932),
 ('ТагилНовокузнецкНовомосковскНовороссийскНовосибирскНовотроицкНовочеркасскНовошахтинскНовый',
  0.5423436164855957),
 ('областьНовосибирскБарабинскБердскБолотноеИскитимКарасукКаргатКуйбышевКупиноОбьТатарскТогучинЧерепаново',
  0.5377570390701294)]
"""

# Now we load full model, for this reason, we can continue an training

from gensim.test.utils import datapath
from smart_open import smart_open

with smart_open(datapath("crime-and-punishment.txt"), encoding="utf-8") as infile:  # russian text
    corpus = [line.strip().split() for line in infile]

model.train(corpus, total_examples=len(corpus), epochs=5)

  • Similarity search improvements (@Witiko, #2016)

    • Add similarity search using the Levenshtein distance in gensim.similarities.LevenshteinSimilarityIndex

    • Performance optimizations to gensim.similarities.SoftCosineSimilarity (full benchmark)

      dictionary size corpus size speed
      1000 100 1.0×
      1000 1000 53.4×
      1000 100000 156784.8×
      100000 100 3.8×
      100000 1000 405.8×
      100000 100000 66262.0×

    • See updated soft-cosine tutorial for more information and usage examples

  • Add python3.7 support (@menshikh-iv, #2211)

👍 Improvements

Optimizations
  • Reduce Phraser memory usage (drop frequencies) (@jenishah, #2208)
  • Reduce memory consumption of summarizer (@horpto, #2298)
  • Replace inline slow equivalent of mean_absolute_difference with fast (@horpto, #2284)
  • Reuse precalculated updated prior in ldamodel.update_dir_prior (@horpto, #2274)
  • Improve KeyedVector.wmdistance (@horpto, #2326)
  • Optimize remove_unreachable_nodes in gensim.summarization (@horpto, #2263)
  • Optimize mz_entropy from gensim.summarization (@horpto, #2267)
  • Improve filter_extremes methods in Dictionary and HashDictionary (@horpto, #2303)
Additions
Cleanup

🔴 Bug fixes

📚 Tutorial and doc improvements

⚠️ Deprecations (will be removed in the next major release)

  • Remove

    • gensim.models.wrappers.fasttext (obsoleted by the new native gensim.models.fasttext implementation)
    • gensim.examples
    • gensim.nosy
    • gensim.scripts.word2vec_standalone
    • gensim.scripts.make_wiki_lemma
    • gensim.scripts.make_wiki_online
    • gensim.scripts.make_wiki_online_lemma
    • gensim.scripts.make_wiki_online_nodebug
    • gensim.scripts.make_wiki (all of these obsoleted by the new native gensim.scripts.segment_wiki implementation)
    • "deprecated" functions and attributes

  • Move

    • gensim.scripts.make_wikicorpus ➡ gensim.scripts.make_wiki.py
    • gensim.summarization ➡ gensim.models.summarization
    • gensim.topic_coherence ➡ gensim.models._coherence
    • gensim.utils ➡ gensim.utils.utils (old imports will continue to work)
    • gensim.parsing.* ➡ gensim.utils.text_utils
3.6.0
3.6.0
Latest release

@menshikh-iv menshikh-iv released this Sep 20, 2018 · 19 commits to develop since this release

Assets 26

3.6.0, 2018-09-20

🌟 New features

  • File-based training for *2Vec models (@persiyanov, #2127 & #2078 & #2048)

    Blog post / Jupyter tutorial.

    New training mode for *2Vec models (word2vec, doc2vec, fasttext) that allows model training to scale linearly with the number of cores (full GIL elimination). The result of our Google Summer of Code 2018 project by Dmitry Persiyanov.

    Benchmark on the full English Wikipedia, Intel(R) Xeon(R) CPU @ 2.30GHz 32 cores (GCE cloud), MKL BLAS:

    Model Queue-based version [sec] File-based version [sec] speed up Accuracy (queue-based) Accuracy (file-based)
    Word2Vec 9230 2437 3.79x 0.754 (± 0.003) 0.750 (± 0.001)
    Doc2Vec 18264 2889 6.32x 0.721 (± 0.002) 0.683 (± 0.003)
    FastText 16361 10625 1.54x 0.642 (± 0.002) 0.660 (± 0.001)

    Usage:

    import gensim.downloader as api
from multiprocessing import cpu_count
from gensim.utils import save_as_line_sentence
from gensim.test.utils import get_tmpfile
from gensim.models import Word2Vec, Doc2Vec, FastText


# Convert any corpus to the needed format: 1 document per line, words delimited by " "
corpus = api.load("text8")
corpus_fname = get_tmpfile("text8-file-sentence.txt")
save_as_line_sentence(corpus, corpus_fname)

# Choose num of cores that you want to use (let's use all, models scale linearly now!)
num_cores = cpu_count()

# Train models using all cores
w2v_model = Word2Vec(corpus_file=corpus_fname, workers=num_cores)
d2v_model = Doc2Vec(corpus_file=corpus_fname, workers=num_cores)
ft_model = FastText(corpus_file=corpus_fname, workers=num_cores)

    Read notebook tutorial with full description.

👍 Improvements

🔴 Bug fixes

📚 Tutorial and doc improvements

  • Update docstring with new analogy evaluation method (@akutuzov, #2130)
  • Improve prune_at parameter description for gensim.corpora.Dictionary (@yxonic, #2128)
  • Fix default -> auto prior parameter in documentation for lda-related models (@Laubeee, #2156)
  • Use heading instead of bold style in gensim.models.translation_matrix (@nzw0301, #2164)
  • Fix quote of vocabulary from gensim.models.Word2Vec (@nzw0301, #2161)
  • Replace deprecated parameters with new in docstring of gensim.models.Doc2Vec (@xuhdev, #2165)
  • Fix formula in Mallet documentation (@Laubeee, #2186)
  • Fix minor semantic issue in docs for Phrases (@RunHorst, #2148)
  • Fix typo in documentation (@KenjiOhtsuka, #2157)
  • Additional documentation fixes (@piskvorky, #2121)

⚠️ Deprecations (will be removed in the next major release)

  • Remove

    • gensim.models.wrappers.fasttext (obsoleted by the new native gensim.models.fasttext implementation)
    • gensim.examples
    • gensim.nosy
    • gensim.scripts.word2vec_standalone
    • gensim.scripts.make_wiki_lemma
    • gensim.scripts.make_wiki_online
    • gensim.scripts.make_wiki_online_lemma
    • gensim.scripts.make_wiki_online_nodebug
    • gensim.scripts.make_wiki (all of these obsoleted by the new native gensim.scripts.segment_wiki implementation)
    • "deprecated" functions and attributes

  • Move

    • gensim.scripts.make_wikicorpus ➡ gensim.scripts.make_wiki.py
    • gensim.summarization ➡ gensim.models.summarization
    • gensim.topic_coherence ➡ gensim.models._coherence
    • gensim.utils ➡ gensim.utils.utils (old imports will continue to work)
    • gensim.parsing.* ➡ gensim.utils.text_utils
热门度与活跃度
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创建时间： 2011-02-10 15:43:04
最后Commits： 2月前
RaRe-Technologies
RARE Technologies
Commercial Machine Learning & NLP
GitHub作者资料
基本信息
分类：自然语言处理 / Python开发
收录时间：2018-10-30 08:43:56