Text classification with PyTorch and torchtext

This notebook shows how to use torchtext and PyTorch libraries to retrieve a dataset and build a simple RNN model to classify text.

It is based on the TREC-6 dataset, which consists on 5,952 questions written in English, classified in the following categories, depending on their answer:

• HUM: Human
• DESC: Description
• ABBR: Abbreviation
• LOC: Location
• NUM: Number
• ENTY: Entity

Further exercises

Try improving the performance of the model by:

• Adding more complexity (RNN layers, other layers)
• Add regularisation (L1, L2, dropout)
• Make the model a bidirectional RNN
• Use pretrained embeddings such as word2vec or GLOVE. Note that you can use: nn.Embedding.from_pretrained(...)