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Word2Vec (5):Pytorch 實作 CBOW with Hierarchical Softmax

 2021-01-31 2021-02-10NLP11 minutes read (About 1637 words)

CBOW with Hierarchical Softmax

CBOW 的思想是用兩側 context words 去預測中間的 center word


 NLP, word embedding, pytorch
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Word2Vec (6):Pytorch 實作 Skipgram with Negative Sampling

 2021-01-31 2021-02-10NLP10 minutes read (About 1516 words)

Skipgram with Negative Sampling

skipgram 的思想是用中心詞 center word 去預測兩側的 context words


 NLP, word embedding, pytorch
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Word2Vec (4):Pytorch 實作 Word2Vec with Softmax

 2021-01-31 2021-02-10NLP9 minutes read (About 1404 words)

用 pytorch 實現最簡單版本的 CBOW 與 skipgram,objective function 採用 minimize negative log likelihood with softmax


 NLP, word embedding, pytorch
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Word2Vec (3):Negative Sampling 背後的數學

 2021-01-24 2021-02-10NLP13 minutes read (About 1993 words)

以下用 Skip-gram 為例


 NLP, word embedding
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Word2Vec (2):Hierarchical Softmax 背後的數學

 2021-01-24 2021-02-10NLP6 minutes read (About 890 words)

以 CBOW 為例


 NLP, word embedding
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Word2Vec (0):從原理到實現

 2021-01-24 2021-02-10NLP8 minutes read (About 1226 words)

這篇是在 notion 整理的筆記大綱,只提供綱要性的說明

預備知識

  • language model: NLP 語言模型

    參閱 Word2Vec (1):NLP Language Model

  • huffman tree

簡介

兩種網路結構

CBOW and skipgram


 NLP, word embedding
  Read more
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