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Fix FastText doc-comment example for build_vocab and train to use correct argument names #3227

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merged 4 commits into from
Oct 24, 2021
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Fix FastText doc-comment example for build_vocab and train to use correct argument names #3227

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44d879b
fix docs to match argument names
HLasse Sep 2, 2021
8c7fd7d
dummy commit to trigger CI
mpenkov Sep 28, 2021
103cbaa
Merge remote-tracking branch 'upstream/develop' into develop
mpenkov Oct 24, 2021
db31c57
Update CHANGELOG.md
mpenkov Oct 24, 2021
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Expand Up @@ -4,6 +4,7 @@ Changes
## Unreleased

* [#3194](https://github.com/RaRe-Technologies/gensim/pull/3194): Added random_seed parameter to make LsiModel reproducible, by [@parashardhapola](https://github.com/parashardhapola)
* [#3227](https://github.com/RaRe-Technologies/gensim/pull/3227): Fix FastText doc-comment example for `build_vocab` and `train` to use correct argument names, by [@HLasse](https://github.com/HLasse)

## 4.1.2, 2021-09-17

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1 change: 0 additions & 1 deletion README.md
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Expand Up @@ -176,4 +176,3 @@ BibTeX entry:
[OpenBLAS]: http://xianyi.github.io/OpenBLAS/
[source tar.gz]: http://pypi.python.org/pypi/gensim
[documentation]: http://radimrehurek.com/gensim/install.html

8 changes: 4 additions & 4 deletions gensim/models/fasttext.py
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Expand Up @@ -38,8 +38,8 @@
>>> print(len(common_texts))
9
>>> model = FastText(vector_size=4, window=3, min_count=1) # instantiate
>>> model.build_vocab(sentences=common_texts)
>>> model.train(sentences=common_texts, total_examples=len(common_texts), epochs=10) # train
>>> model.build_vocab(corpus_iterable=common_texts)
>>> model.train(corpus_iterable=common_texts, total_examples=len(common_texts), epochs=10) # train

Once you have a model, you can access its keyed vectors via the `model.wv` attributes.
The keyed vectors instance is quite powerful: it can perform a wide range of NLP tasks.
Expand Down Expand Up @@ -108,9 +108,9 @@
>>>
>>>
>>> model4 = FastText(vector_size=4, window=3, min_count=1)
>>> model4.build_vocab(sentences=MyIter())
>>> model4.build_vocab(corpus_iterable=MyIter())
>>> total_examples = model4.corpus_count
>>> model4.train(sentences=MyIter(), total_examples=total_examples, epochs=5)
>>> model4.train(corpus_iterable=MyIter(), total_examples=total_examples, epochs=5)

Persist a model to disk with:

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