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A list of NLP(Natural Language Processing) tutorials

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- TransformerEncoder.py
- - corpus_sample.png
- - train.ipynb
- build_vocab.py
- data_loader.py
- preprocessing.sh
- read_word_pair.py
- README.md
- tokenization_ko.py
- train.py
- - dataLoader.py
  - evaluate.py
  - seq2seq.py
  - train.py
- - dataLoader.py
  - evaluate.py
  - seq2seq.py
  - train.py
- - dataLoader.py
  - evaluate.py
  - seq2seq.py
  - train.py
- - dataLoader.py
  - evaluate.py
  - input_emb_matrix.npy
  - output_emb_matrix.npy
  - seq2seq.py
  - train.py
  - word_embedding.py
- - eng-fra.txt
- - dataLoader.py
  - evaluate.py
  - input_emb_matrix.npy
  - output_emb_matrix.npy
  - seq2seq.py
  - train.py
  - word_embedding.py
- download_fasttext.sh
- README.md
- - cbow.vec
  - cbow.vocab.train.pkl
  - skipgram.vec
  - skipgram.vocab.train.pkl
  - vocab.train.pkl
- - data_loader.py
  - preprocessing.sh
  - read_word_pair.py
  - README.md
  - remove_emoji.py
  - tokenization_en.py
  - train.py
- build_corpus.py
- build_vocab.py
- dataset_utils.py
- models.py
- preprocessing.py
- README.md
- tokenization.py
- trainer.py
- - Posts.xml
- dataLoader.py
- embedding.py
- evaluate.py
- models.py
- preprocessing.py
- README.md
- train.py
- data_utils.py
- inference.py
- main.py
- model.py
- README.md
- tokenization.py
- trainer.py
- vocab.py
- - attention_visualize.ipynb
- - attention_visualize1.png
  - attention_visualize2.png
- - log.txt
- data_utils.py
- inference.py
- main.py
- model.py
- optimization.py
- README.md
- tokenization.py
- trainer.py
- vocab.py
LICENSE
README.md

// repository documentation

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# NLP Tutorial
[![LICENSE](https://img.shields.io/github/license/lyeoni/nlp-tutorial?style=flat-square)](https://github.com/lyeoni/nlp-tutorial/blob/master/LICENSE)
[![GitHub issues](https://img.shields.io/github/issues/lyeoni/nlp-tutorial?style=flat-square)](https://github.com/lyeoni/nlp-tutorial/issues)
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A list of NLP(Natural Language Processing) tutorials built on PyTorch.
 

<img width="350" src="https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.png" align="middle">

## Table of Contents
A step-by-step tutorial on how to implement and adapt to the simple real-word NLP task.

### Text Classification

#### [News Category Classification](https://github.com/lyeoni/nlp-tutorial/tree/master/news-category-classifcation)
This repo provides a simple PyTorch implementation of Text Classification, with simple annotation. Here we use _Huffpost_ news corpus including corresponding category. The classification model trained on this dataset identify the category of news article based on their headlines and descriptions.
 **_Keyword_:** _CBoW, LSTM, fastText, Text cateogrization_

#### [IMDb Movie Review Classification](https://github.com/lyeoni/nlp-tutorial/tree/master/text-classification-transformer)
This text classification tutorial trains a transformer model on the IMDb movie review dataset for sentiment analysis. It provides a simple PyTorch implementation, with simple annotation.
 **_Keyword_:** _Transformer, Sentiment analysis_

#### [Question-Answer Matching](https://github.com/lyeoni/nlp-tutorial/tree/master/question-answer-matching)
This repo provides a simple PyTorch implementation of Question-Answer matching. Here we use the corpus from _Stack Exchange_ to build embeddings for entire questions. Using those embeddings, we find similar questions for a given question, and show the corresponding answers to those I found.
 **_Keyword_:** _CBoW, TF-IDF, LSTM with variable-length seqeucnes_

#### [Movie Review Classification (Korean NLP)](https://github.com/lyeoni/nlp-tutorial/tree/master/movie-rating-classification)
This repo provides a simple Keras implementation of TextCNN for Text Classification. Here we use the _movie review_ corpus written in Korean. The model trained on this dataset identify the sentiment based on review text.
 **_Keyword_:** _TextCNN, Sentiment analysis_

### Neural Machine Translation
#### [English to French Translation - seq2seq](https://github.com/lyeoni/nlp-tutorial/tree/master/neural-machine-translation)
This neural machine translation tutorial trains a seq2seq model on a set of many thousands of English to French translation pairs to translate from English to French. It provides an intrinsic/extrinsic comparison of various sequence-to-sequence (seq2seq) models in translation.
 **_Keyword_:** _sequence to seqeunce network(seq2seq), Attention, Autoregressive, Teacher-forcing_

#### [French to English Translation - Transformer](https://github.com/lyeoni/nlp-tutorial/tree/master/translation-transformer)
This neural machine translation tutorial trains a Transformer model on a set of many thousands of French to English translation pairs to translate from French to English. It provides a simple PyTorch implementation, with simple annotation.
 **_Keyword_:** _Transformer, SentencePiece_

### Natural Language Understanding
#### [Neural Language Model](https://github.com/lyeoni/pretraining-for-language-understanding)
This repo provides a simple PyTorch implementation of Neural Language Model for natural language understanding. Here we implement unidirectional/bidirectional language models, and pre-train language representations from unlabeled text (_Wikipedia_ corpus).
 **_Keyword_:** _Autoregressive language model, Perplexity_

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