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All Projects → DSXiangLi → Embedding

DSXiangLi / Embedding

Licence: other
Embedding模型代码和学习笔记总结

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python
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Labels

word2vectransformerseq2seqfasttextembeddingdoc2vecskip-thoughtsencoder-decodercnn-lstmhierarchical-softmaxquick-thoughtgenismnegative-samplingtf-estimatorbookcorpus

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NLP Embedding

Requirement: Tensorflow 1.14, Tensorboard 1.14

The code is not rigorously tested, if you find a bug, welcome PR ^_^ ~

Model List

  1. Word2Vec: sogou新闻数据
  2. Fasttext: quora kaggle 分类数据
  3. Doc2Vec[PV-DBOW/PV-DM]: sogou新闻数据
  4. skip-thought: bookcorpus爬虫数据
  5. quick-thought: bookcorpus爬虫数据
  6. CNN-LSTM: bookcorpus爬虫数据
  7. transformer: WMT英翻中任务

Paper List

词向量

  • [Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013)
  • [Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013)
  • [Word2Vec] word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method (2014)
  • [Word2Vec] word2vec Parameter Learning Explained (2016)
  • [Fasttext] Enriching Word Vectors with Subword Information (Facebook 2017)
  • [Fasttext] Bag of Tricks for Efficient Text Classification (Facebook 2016)
  • [Glove] Global Vectors for Word Representation (2014)
  • [ELMo] Deep contextualized word representations (2018)

文本向量

  • [Doc2vec] Distributed Representations of Sentences and Documents (Google 2014)
  • [Doc2vec] A SIMPLE BUT TOUGH-TO-BEAT BASELINE FOR SEN- TENCE EMBEDDINGS (2017)
  • [Encoder-Decoder: Skip-Thought] Skip-Thought Vectors (2015)
  • [Encoder-Decoder: Skip-Thought] Rethinking Skip-thought- A Neighborhood based Approach (2017)
  • [Encoder-Decoder: CNN-LSTM]Learning Generic Sentence Representations Using Convolutional Neural Networks (2017)
  • [Encoder-Decoder: Quick-Thought] Quick-Thought: AN EFFICIENT FRAMEWORK FOR LEARNING SENTENCE REPRESENTATIONS (Google 2018)
  • [Transformer] Attention is all you need (2017)
  • [FastSent|DVAE]Learning Distributed Representations of Sentences from Unlabelled Data (2016)
  • [Siamese] Learning Text Similarity with Siamese Recurrent Networks (2016)
  • [InferSent] Supervised Learning of Universal Sentence Representations from Natural Language Inference Data (2018)
  • [GenSen] LEARNING GENERAL PURPOSE DISTRIBUTED SENTENCE REPRESENTATIONS VIA LARGE SCALE MULTITASK LEARNING (2018)
  • [USE] Universal Sentence Encoder (Google 2018)
  • [ULMFit] Universal Language Model Fine-tuning for Text Classification (fastai 2018)
  • [GPT] Improving Language Understanding by Generative Pre-Training (openai 2018)
  • [Bert] Pre-training of Deep Bidirectional Transformers for Language Understanding（Google 2019)
  • [Sentence-BERT] Sentence Embeddings using Siamese BERT-Networks (2019)
  • [Bert-flow] On the Sentence Embedding from Pre-trained Language Model(2020)
  • [Representation] Fine-Grained Analysis of Sentence Embedding Using Auxiliary Prediction Tasks (2017)
  • [Representation] What you can cram into a single vector: Probing Sentence Embedding for linguistic properties(2018)
  • [Representation] Assessing Composition in Sentence Vector Representations (2018)

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