https://github.com/bentrevett/pytorch-sentiment-analysis

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
https://github.com/bentrevett/pytorch-sentiment-analysis

bert cnn cnn-text-classification fasttext lstm lstm-sentiment-analysis natural-language-processing nlp pytorch pytorch-nlp pytorch-tutorial pytorch-tutorials recurrent-neural-networks rnn sentiment-analysis sentiment-classification torchtext transformers tutorial word-embeddings

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Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

• Host: GitHub
• URL: https://github.com/bentrevett/pytorch-sentiment-analysis
• Owner: bentrevett
• License: mit
• Created: 2017-12-13T13:36:40.000Z (about 7 years ago)
• Default Branch: main
• Last Pushed: 2024-03-27T17:27:27.000Z (10 months ago)
• Last Synced: 2024-04-10T00:48:10.065Z (10 months ago)
• Topics: bert, cnn, cnn-text-classification, fasttext, lstm, lstm-sentiment-analysis, natural-language-processing, nlp, pytorch, pytorch-nlp, pytorch-tutorial, pytorch-tutorials, recurrent-neural-networks, rnn, sentiment-analysis, sentiment-classification, torchtext, transformers, tutorial, word-embeddings
• Language: Jupyter Notebook
• Homepage:
• Size: 1.64 MB
• Stars: 4,207
• Watchers: 83
• Forks: 1,155
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# PyTorch Sentiment Analysis

This repo contains tutorials covering understanding and implementing sequence classification models using [PyTorch](https://github.com/pytorch/pytorch), with Python 3.9. Specifically, we'll train models to predict sentiment from movie reviews.

**If you find any mistakes or disagree with any of the explanations, please do not hesitate to [submit an issue](https://github.com/bentrevett/pytorch-sentiment-analysis/issues/new). I welcome any feedback, positive or negative!**

## Getting Started

Install the required dependencies with: `pip install -r requirements.txt --upgrade`.

## Tutorials

-   1 - [Neural Bag of Words](https://github.com/bentrevett/pytorch-sentiment-analysis/blob/main/1%20-%20Neural%20Bag%20of%20Words.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bentrevett/pytorch-sentiment-analysis/blob/main/1%20-%20Neural%20Bag%20of%20Words.ipynb)

    This tutorial covers the workflow of a sequence classification project with PyTorch. We'll cover the basics of sequence classification using a simple, but effective, neural bag-of-words model, and how to use the datasets/torchtext libaries to simplify data loading/preprocessing.

-   2 - [Recurrent Neural Networks](https://github.com/bentrevett/pytorch-sentiment-analysis/blob/main/2%20-%20Recurrent%20Neural%20Networks.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bentrevett/pytorch-sentiment-analysis/blob/main/2%20-%20Recurrent%20Neural%20Networks.ipynb)

    Now we have the basic sequence classification workflow covered, this tutorial will focus on improving our results by switching to a recurrent neural network (RNN) model. We'll cover the theory behind RNNs, and look at an implementation of the long short-term memory (LSTM) RNN, one of the most common variants of RNN.

-   3 - [Convolutional Neural Networks](https://github.com/bentrevett/pytorch-sentiment-analysis/blob/main/3%20-%20Convolutional%20Neural%20Networks.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bentrevett/pytorch-sentiment-analysis/blob/main/3%20-%20Convolutional%20Neural%20Networks.ipynb)

    Next, we'll cover convolutional neural networks (CNNs) for sentiment analysis. This model will be an implementation of [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882).

-   4 - [Transformers](https://github.com/bentrevett/pytorch-sentiment-analysis/blob/main/4%20-%20Transformers.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bentrevett/pytorch-sentiment-analysis/blob/main/4%20-%20Transformers.ipynb)

    Finally, we'll show how to use the transformers library to load a pre-trained transformer model, specifically the BERT model from [this](https://arxiv.org/abs/1810.04805) paper, and use it for sequence classification.

## Legacy Tutorials

Previous versions of these tutorials used features from the torchtext library which are no longer available. These are stored in the [legacy](https://github.com/bentrevett/pytorch-sentiment-analysis/tree/main/legacy) directory.

## References

Here are some things I looked at while making these tutorials. Some of it may be out of date.

-   http://anie.me/On-Torchtext/

-   http://mlexplained.com/2018/02/08/a-comprehensive-tutorial-to-torchtext/

-   https://github.com/spro/practical-pytorch

-   https://gist.github.com/Tushar-N/dfca335e370a2bc3bc79876e6270099e

-   https://gist.github.com/HarshTrivedi/f4e7293e941b17d19058f6fb90ab0fec

-   https://github.com/keras-team/keras/blob/master/examples/imdb_fasttext.py

-   https://github.com/Shawn1993/cnn-text-classification-pytorch