Posts with #Doc2vec

#Doc2vec

Public posts on X that use #Doc2vec.

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  1. Gensim@gensim_py
    See for yourself how #Doc2vec DM is different from #Doc2vec DBOW! Original paper's Wikipedia result in a py notebook
    0411
  2. Andres Karjus | also on 🟦bsky@AndresKarjus
    I know contextual models like #BERT are supposed to be all the rage (since a while), but I just ran #doc2vec with a pretrained #fasttext model on a 10m word corpus of docs in a morphl complex lang *without lemmatizing it* and it took a minute & the results are like... really good
    Download
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  3. Gensim@gensim_py
    To achieve best-fit in #doc2vec: re-infer the training docs using the final model | tip from @gojomo
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  4. Gensim@gensim_py
    A serious bug in #doc2vec fixed, after 3 years :O New release will have faster convergence & better vectors. Huge thanks to Umang!
    01889
  5. Gensim@gensim_py
    #doc2vec on Japanese text. Tutorial with code.
    DeepAge @deepage_
    Word2Vecの応用!文書をベクトル表現化するDoc2Vecについて紹介しました! Doc2Vecの仕組みとgensimを使った文書類似度算出チュートリアル | DeepAge
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  6. DeepAge@deepage_
    Word2Vecの応用!文書をベクトル表現化するDoc2Vecについて紹介しました! Doc2Vecの仕組みとgensimを使った文書類似度算出チュートリアル | DeepAge
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  7. Gensim@gensim_py
    In the next major release 4.0.0, we’ll drop the pure-Python implementations of #word2vec, #doc2vec etc. Fast Cython versions only. Don’t worry, we also provide binary pre-compiled wheels for all platforms to make this transition painless /im
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  8. Tryolabs@tryolabs
    At Tryolabs, our ultimate 'lab rat' is #doc2vec (Paragraph Vector). It all started with this @Google publishing:
    0111
  9. Gensim@gensim_py
    #Doc2vec works well on Japanese text after applying right tokenisation. Code and analysis.
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  10. TruSTAR@trustartech
    Exciting news! We've published new research on how make sense of unstructured threat intelligence data using #NLP. Read our summary on how we created a model using #Doc2Vec trained on @MITREattack and @usnistgov's NVD datasets
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