https://github.com/morvanzhou/nlp-tutorials

Simple implementations of NLP models. Tutorials are written in Chinese on my website https://mofanpy.com
https://github.com/morvanzhou/nlp-tutorials

attention bert elmo gpt nlp seq2seq transformer tutorial w2v

Last synced: 7 days ago
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Simple implementations of NLP models. Tutorials are written in Chinese on my website https://mofanpy.com

• Host: GitHub
• URL: https://github.com/morvanzhou/nlp-tutorials
• Owner: MorvanZhou
• License: mit
• Created: 2018-11-30T09:11:39.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-05-22T23:31:37.000Z (over 1 year ago)
• Last Synced: 2024-12-08T11:04:37.642Z (14 days ago)
• Topics: attention, bert, elmo, gpt, nlp, seq2seq, transformer, tutorial, w2v
• Language: Python
• Homepage: https://mofanpy.com
• Size: 888 KB
• Stars: 914
• Watchers: 17
• Forks: 313
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Natural Language Processing Tutorial

Tutorial in Chinese can be found in [mofanpy.com](https://mofanpy.com/tutorials/machine-learning/nlp/).

This repo includes many simple implementations of models in Neural Language Processing (NLP).

All code implementations in this tutorial are organized as following:

1. Search Engine

  - [TF-IDF numpy / TF-IDF skearn](#TF-IDF)

2. Understand Word (W2V)

  - [Continuous Bag of Words (CBOW)](#Word2Vec)

  - [Skip-Gram](#Word2Vec)

3. Understand Sentence (Seq2Seq)

  - [seq2seq](#Seq2Seq)

  - [CNN language model](#CNNLanguageModel)

4. All about Attention

  - [seq2seq with attention](#Seq2SeqAttention)

  - [Transformer](#Transformer)

5. Pretrained Models

  - [ELMo](#ELMO)

  - [GPT](#GPT)

  - [BERT](#BERT)

Thanks for the contribution made by [@W1Fl](https://github.com/W1Fl) with a simplified keras codes in [simple_realize](simple_realize).

And the a [pytorch version of this NLP](/pytorch) tutorial made by [@ruifanxu](https://github.com/ruifan831).

## Installation

```shell script

$ git clone https://github.com/MorvanZhou/NLP-Tutorials

$ cd NLP-Tutorials/

$ sudo pip3 install -r requirements.txt

```

## TF-IDF

TF-IDF numpy [code](tf_idf.py)

TF-IDF short sklearn [code](tf_idf_sklearn.py)







## Word2Vec

[Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/pdf/1301.3781.pdf)

Skip-Gram [code](skip-gram.py)

CBOW [code](CBOW.py)



















## Seq2Seq

[Sequence to Sequence Learning with Neural Networks](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)

Seq2Seq [code](seq2seq.py)







## CNNLanguageModel

[Convolutional Neural Networks for Sentence Classification](https://arxiv.org/pdf/1408.5882.pdf)

CNN language model [code](cnn-lm.py)







## Seq2SeqAttention

[Effective Approaches to Attention-based Neural Machine Translation](https://arxiv.org/pdf/1508.04025.pdf)

Seq2Seq Attention [code](seq2seq_attention.py)













## Transformer

[Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf)

Transformer [code](transformer.py)



















## ELMO

[Deep contextualized word representations](https://arxiv.org/pdf/1802.05365.pdf)

ELMO [code](ELMo.py)













## GPT

[Improving Language Understanding by Generative Pre-Training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)

GPT [code](GPT.py)













## BERT

[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/pdf/1810.04805.pdf)

BERT [code](BERT.py)

My new attempt [Bert with window mask](BERT_window_mask.py)