Package: compress-fasttext
Compress-fastTextThis Python 3 package allows to compress fastText word embedding models
(from thegensimpackage) by orders of magnitude,
without significantly affecting their quality.Hereare some links to the models that have already been compressed.Thisblogpost in Russianandthis one in Englishgive more details about the motivation and
methods for compressing fastText models.Note: gensim==4.0.0 has introduced some backward-incompatible changes:With gensim<4.0.0, please use compress-fasttext<=0.0.7
(and optionally Russian models fromour first release).With gensim>=4.0.0, please use compress-fasttext>=0.1.0
(and optionally Russian or English models fromour 0.1.0 release).Some models are no longer supported in the new version of gensim+compress-fasttext
(for example, multiple models fromRusVectoresthat usecompatible_hash=False).For any particular model, compatibility should be determined experimentally.
If you notice any strange behaviour, please report in the Github issues.The package can be installed withpip:pip install compress-fasttext[full]If you are not going to perform matrix decomposition or quantization,
you can install a variety with less dependencies:pip install compress-fasttextModel compressionYou can use this package to compress your own fastText model (or one downloaded e.g. fromRusVectores):Compress a model in Gensim format:importgensimimportcompress_fasttextbig_model=gensim.models.fasttext.FastTextKeyedVectors.load('path-to-original-model')small_model=compress_fasttext.prune_ft_freq(big_model,pq=True)small_model.save('path-to-new-model')Import a model in Facebook original format and compress it:fromgensim.models.fasttextimportload_facebook_modelimportcompress_fasttextbig_model=load_facebook_model('path-to-original-model').wvsmall_model=compress_fasttext.prune_ft_freq(big_model,pq=True)small_model.save('path-to-new-model')To perform this compression, you will need topip install gensim==3.8.3 sklearnbeforehand.Different compression methods include:matrix decomposition (svd_ft)product quantization (quantize_ft)optimization of feature hashing (prune_ft)feature selection (prune_ft_freq)The recommended approach is combination of feature selection and quantization (prune_ft_freqwithpq=True).Model usageIf you just need a tiny fastText model for Russian, you can downloadthis21-megabyte model. It's a compressed version ofgeowac_tokens_none_fasttextskipgram_300_5_2020model
fromRusVectores.Ifcompress-fasttextis already installed, you can download and use this tiny modelimportcompress_fasttextsmall_model=compress_fasttext.models.CompressedFastTextKeyedVectors.load('https://github.com/avidale/compress-fasttext/releases/download/gensim-4-draft/geowac_tokens_sg_300_5_2020-100K-20K-100.bin')print(small_model['спасибо'])# [ 0.26762889  0.35489027 ...  -0.06149674] # a 300-dimensional vectorprint(small_model.most_similar('котенок'))# [('кот', 0.7391024827957153), ('пес', 0.7388300895690918), ('малыш', 0.7280327081680298), ... ]The classCompressedFastTextKeyedVectorsinherits fromgensim.models.fasttext.FastTextKeyedVectors,
but makes a few additional optimizations.For English, you can usethistiny model,
obtained by compressingthe model by Facebook.importcompress_fasttextsmall_model=compress_fasttext.models.CompressedFastTextKeyedVectors.load('https://github.com/avidale/compress-fasttext/releases/download/v0.0.4/cc.en.300.compressed.bin')print(small_model['hello'])# [ 1.84736611e-01  6.32683930e-03  4.43901886e-03 ... -2.88431027e-02]  # a 300-dimensional vectorprint(small_model.most_similar('Python'))# [('PHP', 0.5252903699874878), ('.NET', 0.5027452707290649), ('Java', 0.4897131323814392),  ... ]More compressed models for 101 various languages can be found athttps://zenodo.org/record/4905385.Example of applicationIn practical applications, you usually feed fastText embeddings to some other model.
The classFastTextTransformerusesthe scikit-learn interfaceand represents a text as the average of the embedding of its words.
With it you can, for example, train a classifier on top of fastText
to tell edible things from inedible ones:importcompress_fasttextfromsklearn.pipelineimportmake_pipelinefromsklearn.linear_modelimportLogisticRegressionfromcompress_fasttext.feature_extractionimportFastTextTransformersmall_model=compress_fasttext.models.CompressedFastTextKeyedVectors.load('https://github.com/avidale/compress-fasttext/releases/download/v0.0.4/cc.en.300.compressed.bin')classifier=make_pipeline(FastTextTransformer(model=small_model),LogisticRegression()).fit(['banana','soup','burger','car','tree','city'],[1,1,1,0,0,0])classifier.predict(['jet','train','cake','apple'])# array([0, 0, 1, 1])NotesThis code is heavily based on thenavecpackage by Alexander Kukushkin andthe blogpostby Andrey Vasnetsov about shrinking fastText embeddings.