Tensorflow实现的深度NLP模型集锦(附资源)

栏目: 数据库 · 发布时间: 5年前

Tensorflow实现的深度NLP模型集锦(附资源)

基于Tensorflow的自然语言处理模型,为自然语言处理问题收集机器学习和Tensorflow深度学习模型,100%Jupeyter NoteBooks且内部代码极为简洁。

资源整理自网络,源地址:

https://github.com/huseinzol05

目录

  • Text classification

  • Chatbot

  • Neural Machine Translation

  • Embedded

  • Entity-Tagging

  • POS-Tagging

  • Dependency-Parser

  • Question-Answers

  • Supervised Summarization

  • Unsupervised Summarization

  • Stemming

  • Generator

  • Language detection

  • OCR (optical character recognition)

  • Speech to Text

  • Text to Speech

  • Text Similarity

  • Miscellaneous

  • Attention

目标

原始的实现稍微有点复杂,对于初学者来说有点难。所以我尝试将其中大部分内容简化,同时,还有很多论文的内容亟待实现,一步一步来。

内容

文本分类:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/text-classification

1. Basic cell RNN

2. Bidirectional RNN

3. LSTM cell RNN

4. GRU cell RNN

5. LSTM RNN + Conv2D

6. K-max Conv1d

7. LSTM RNN + Conv1D + Highway

8. LSTM RNN with Attention

9. Neural Turing Machine

10. Seq2Seq

11. Bidirectional Transformers

12. Dynamic Memory Network

13. Residual Network using Atrous CNN + Bahdanau Attention

14.Transformer-XL

完整列表包含(66 notebooks)

聊天机器人:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/chatbot

1. Seq2Seq-manual

2. Seq2Seq-API Greedy

3. Bidirectional Seq2Seq-manual

4. Bidirectional Seq2Seq-API Greedy

5. Bidirectional Seq2Seq-manual + backward Bahdanau + forward Luong

6. Bidirectional Seq2Seq-API + backward Bahdanau + forward Luong + Stack Bahdanau Luong Attention + Beam Decoder

7. Bytenet

8. Capsule layers + LSTM Seq2Seq-API + Luong Attention + Beam Decoder

9. End-to-End Memory Network

10. Attention is All you need

11.Transformer-XL+ LSTM

12.GPT-2+ LSTM

完整列表包含(51 notebooks)

机器翻译(英语到越南语):

链接:

https://github.com/huseinzol05/NLP-ModelsTensorflow/tree/master/neural-machine-translation

1. Seq2Seq-manual

2. Seq2Seq-API Greedy

3. Bidirectional Seq2Seq-manual

4. Bidirectional Seq2Seq-API Greedy

5. Bidirectional Seq2Seq-manual + backward Bahdanau + forward Luong

6. Bidirectional Seq2Seq-API + backward Bahdanau + forward Luong + Stack Bahdanau Luong Attention + Beam Decoder

7. Bytenet

8. Capsule layers + LSTM Seq2Seq-API + Luong Attention + Beam Decoder

9. End-to-End Memory Network

10. Attention is All you need

完整列表包含(49 notebooks)

词向量:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/embedded

1. Word Vector using CBOW sample softmax

2. Word Vector using CBOW noise contrastive estimation

3. Word Vector using skipgram sample softmax

4. Word Vector using skipgram noise contrastive estimation

5. Lda2Vec Tensorflow

6. Supervised Embedded

7. Triplet-loss + LSTM

8. LSTM Auto-Encoder

9. Batch-All Triplet-loss LSTM

10. Fast-text

11. ELMO (biLM)

词性标注:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/pos-tagging

1. Bidirectional RNN + Bahdanau Attention + CRF

2. Bidirectional RNN + Luong Attention + CRF

3. Bidirectional RNN + CRF

实体识别:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/entity-tagging

1. Bidirectional RNN + Bahdanau Attention + CRF

2. Bidirectional RNN + Luong Attention + CRF

3. Bidirectional RNN + CRF

4. Char Ngrams + Bidirectional RNN + Bahdanau Attention + CRF

5. Char Ngrams + Residual Network + Bahdanau Attention + CRF

依存分析:

链接:

https://github.com/huseinzol05/NLP-ModelsTensorflow/tree/master/dependency-parser

1. Bidirectional RNN + Bahdanau Attention + CRF

2. Bidirectional RNN + Luong Attention + CRF

3. Residual Network + Bahdanau Attention + CRF

4. Residual Network + Bahdanau Attention + Char Embedded + CRF

问答:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/question-answer

1. End-to-End Memory Network + Basic cell

2. End-to-End Memory Network + GRU cell

3. End-to-End Memory Network + LSTM cell

词干抽取:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/stemming

1. LSTM + Seq2Seq + Beam

2. GRU + Seq2Seq + Beam

3. LSTM + BiRNN + Seq2Seq + Beam

4. GRU + BiRNN + Seq2Seq + Beam

5. DNC + Seq2Seq + Greedy

有监督摘要抽取:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/summarization

1. LSTM Seq2Seq using topic modelling

2. LSTM Seq2Seq + Luong Attention using topic modelling

3. LSTM Seq2Seq + Beam Decoder using topic modelling

4. LSTM Bidirectional + Luong Attention + Beam Decoder using topic modelling

5. LSTM Seq2Seq + Luong Attention + Pointer Generator

6. Bytenet

无监督摘要抽取:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/unsupervised-summarization

1. Skip-thought Vector (unsupervised)

2. Residual Network using Atrous CNN (unsupervised)

3. Residual Network using Atrous CNN + Bahdanau Attention (unsupervised)

OCR (字符识别):

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/ocr

1. CNN + LSTM RNN

语音识别:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/speech-to-text

1. Tacotron

2. Bidirectional RNN + Greedy CTC

3. Bidirectional RNN + Beam CTC

4. Seq2Seq + Bahdanau Attention + Beam CTC

5. Seq2Seq + Luong Attention + Beam CTC

6. Bidirectional RNN + Attention + Beam CTC

7. Wavenet

语音合成:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/text-to-speech

1. Tacotron

2. Wavenet

3. Seq2Seq + Luong Attention

4. Seq2Seq + Bahdanau Attention

生成器:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/generator

1. Character-wise RNN + LSTM

2. Character-wise RNN + Beam search

3. Character-wise RNN + LSTM + Embedding

4. Word-wise RNN + LSTM

5. Word-wise RNN + LSTM + Embedding

6. Character-wise + Seq2Seq + GRU

7. Word-wise + Seq2Seq + GRU

8. Character-wise RNN + LSTM + Bahdanau Attention

9. Character-wise RNN + LSTM + Luong Attention

语言检测:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/language-detection

1. Fast-text Char N-Grams

文本相似性:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/text-similarity

1. Character wise similarity + LSTM + Bidirectional

2. Word wise similarity + LSTM + Bidirectional

3. Character wise similarity Triplet loss + LSTM

4. Word wise similarity Triplet loss + LSTM

注意力机制:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/attention

1. Bahdanau

2. Luong

3. Hierarchical

4. Additive

5. Soft

6. Attention-over-Attention

7. Bahdanau API

8. Luong API

其他:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/misc

1. Attention heatmap on Bahdanau Attention

2. Attention heatmap on Luong Attention

非深度学习:

链接:

https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/not-deep-learning

1. Markov chatbot

2. Decomposition summarization (3 notebooks)


以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持 码农网

查看所有标签

猜你喜欢:

本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们

数据结构与算法

数据结构与算法

Michael McMillan / 吕秀峰、崔睿 / 人民邮电出版社 / 2009-5 / 49.00元

《数据结构与算法C#语言描述》是在.NET框架下用C#语言实现数据结构和算法的第一本全面的参考书。《数据结构与算法C#语言描述》介绍的方法非常实用,采用了时间测试而非大O表示法来分析算法性能。内容涵盖了数据结构和算法的基本原理,涉及数组、广义表、链表、散列表、树、图、排序搜索算法以及更多概率算法和动态规则等高级算法。此外,书中还提供了.NET框架类库中的C#语言实现的数据结构和算法。 《数据......一起来看看 《数据结构与算法》 这本书的介绍吧!

RGB转16进制工具
RGB转16进制工具

RGB HEX 互转工具

SHA 加密
SHA 加密

SHA 加密工具

HEX CMYK 转换工具
HEX CMYK 转换工具

HEX CMYK 互转工具