2.2 KiB
PyTorch Sentiment Analysis
This repo contains tutorials covering how to do sentiment analysis using PyTorch and TorchText.
The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). Subsequent tutorials will cover different approaches.
Tutorials
-
This tutorial covers the workflow of a PyTorch with TorchText project. We'll learn how to: load data, create train/test/validation splits, build a vocabulary, create data iterators, define a model and implement the train/evaluate/test loop. The model will be simple and achieve poor performance, but this will be improved in the subsequent tutorials.
-
2 - Upgraded Sentiment Analysis
Now we have the basic workflow covered, this tutorial will focus on improving our results. We'll cover: loading and using pre-trained word embeddings, different optimizers, different RNN architectures, bi-directional RNNs, multi-layer (aka deep) RNNs and regularization.
-
After we've covered all the fancy upgrades to RNNs, we'll look at a different approach that does not use RNNs. More specifically, we'll implement the model from Bag of Tricks for Efficient Text Classification. This simple model achieves comparable performance as the Upgraded Sentiment Analysis, but trains much faster.
-
4 - Convolutional Sentiment Analysis (WIP)
Finally, we'll cover convolutional neural networks (CNNs) for sentiment analysis. This model will be an implementation of Convolutional Neural Networks for Sentence Classification.
Appendices
-
A - Using TorchText with your Own Datasets
The tutorials use TorchText's built in datasets. This first appendix notebook covers how to load your own datasets using TorchText.
-
B - A Closer Look at Word Embeddings (WIP)
This appendix notebook covers a brief look at exploring the pre-trained word embeddings provided by TorchText.