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@menshikh-iv menshikh-iv released this 09 Dec 19:19
· 1041 commits to develop since this release
3.2.0
6dd8ae7

3.2.0, 2017-12-09

🌟 New features:

  • New download API for corpora and pre-trained models (@chaitaliSaini & @menshikh-iv, #1705 & #1632 & #1492)

    • Download large NLP datasets in one line of Python, then use with memory-efficient data streaming: 
      import gensim.downloader as api

for article in api.load("wiki-english-20171001"):
    print(article)
    • Don’t waste time searching for good word embeddings, use the curated ones: 
      import gensim.downloader as api

model = api.load("glove-twitter-25")
model.most_similar("engineer")

# [('specialist', 0.957542896270752),
#  ('developer', 0.9548177123069763),
#  ('administrator', 0.9432312846183777),
#  ('consultant', 0.93915855884552),
#  ('technician', 0.9368376135826111),
#  ('analyst', 0.9342101216316223),
#  ('architect', 0.9257484674453735),
#  ('engineering', 0.9159940481185913),
#  ('systems', 0.9123805165290833),
#  ('consulting', 0.9112802147865295)]
    • Blog post introducing the API and design decisions.
    • Jupyter notebook with examples

  • New model: Poincaré embeddings (@jayantj, #1696 & #1700 & #1757 & #1734)

    • Embed a graph (taxonomy) in the same way as word2vec embeds words: 
      from gensim.models.poincare import PoincareRelations, PoincareModel
from gensim.test.utils import datapath

data = PoincareRelations(datapath('poincare_hypernyms.tsv'))
model = PoincareModel(data)
model.kv.most_similar("cat.n.01")

# [('kangaroo.n.01', 0.010581353439700418),
# ('gib.n.02', 0.011171531439892076),
# ('striped_skunk.n.01', 0.012025106076442395),
# ('metatherian.n.01', 0.01246679759214648),
# ('mammal.n.01', 0.013281303506525968),
# ('marsupial.n.01', 0.013941330203709653)]
    • Tutorial on Poincaré embeddings (Jupyter notebook).
    • Model introduction and the journey of its implementation (blog post).
    • Original paper on arXiv.

  • Optimized FastText (@manneshiva, #1742)

    • New fast multithreaded implementation of FastText, natively in Python/Cython. Deprecates the existing wrapper for Facebook’s C++ implementation. 
      import gensim.downloader as api
from gensim.models import FastText

model = FastText(api.load("text8"))
model.most_similar("cat")

# [('catnip', 0.8538144826889038),
#  ('catwalk', 0.8136177062988281),
#  ('catchy', 0.7828493118286133),
#  ('caf', 0.7826495170593262),
#  ('bobcat', 0.7745151519775391),
#  ('tomcat', 0.7732658386230469),
#  ('moat', 0.7728310823440552),
#  ('caye', 0.7666271328926086),
#  ('catv', 0.7651021480560303),
#  ('caveat', 0.7643581628799438)]

  • Binary pre-compiled wheels for Windows, OSX and Linux (@menshikh-iv, MacPython/gensim-wheels/#7)

    • Users no longer need to have a C compiler for using the fast (Cythonized) version of word2vec, doc2vec, fasttext etc.
    • Faster Gensim pip installation

  • Added DeprecationWarnings to deprecated methods and parameters, with a clear schedule for removal.

👍 Improvements:

🔴 Bug fixes:

📚 Tutorial and doc improvements:

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

  • Remove

    • 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

  • 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 26
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