bentrevett / pytorch-pos-tagging

A tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.

PyTorch PoS Tagging

Note: This repo only works with torchtext 0.9 or above which requires PyTorch 1.8 or above. If you are using torchtext 0.8 then please use this branch

This repo contains tutorials covering how to perform part-of-speech (PoS) tagging using PyTorch 1.8, torchtext 0.9, and and spaCy 3.0, using Python 3.8.

These tutorials will cover getting started with the most common approach to PoS tagging: recurrent neural networks (RNNs). The first notebook introduces a bi-directional LSTM (BiLSTM) network. The second covers how to fine-tune a pretrained Transformer model.

If you find any mistakes or disagree with any of the explanations, please do not hesitate to submit an issue. I welcome any feedback, positive or negative!

Getting Started

To install PyTorch, see installation instructions on the PyTorch website.

To install TorchText:

pip install torchtext

To install the transformers library:

pip install transformers

We'll also make use of spaCy to tokenize our data. To install spaCy, follow the instructions here making sure to install the English models:

python -m spacy download en_core_web_sm

Tutorials

• 1 - BiLSTM for PoS Tagging

  This tutorial covers the workflow of a PoS tagging project with PyTorch and TorchText. We'll introduce the basic TorchText concepts such as: defining how data is processed; using TorchText's datasets and how to use pre-trained embeddings. Using PyTorch we built a strong baseline model: a multi-layer bi-directional LSTM. We also show how the model can be used for inference to tag any input text.

• 2 - Fine-tuning Pretrained Transformers for PoS Tagging

  This tutorial covers how to fine-tune a pretrained Transformer model, provided by the transformers library, by integrating it with TorchText. We use a pretrained BERT model to provide the embeddings for our input text and input these embeddings to a linear layer that will predict tags based on these embeddings.

References

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 Some of it may be out of date.

• https://github.com/pytorch/text/blob/master/torchtext/datasets/sequence_tagging.py
• https://github.com/pytorch/text/blob/master/test/sequence_tagging.py