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pytorch-pos-tagging

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

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# 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](https://github.com/bentrevett/pytorch-pos-tagging/tree/torchtext08) branch

This repo contains tutorials covering how to perform part-of-speech (PoS) tagging using [PyTorch](https://github.com/pytorch/pytorch) 1.8, [torchtext](https://github.com/pytorch/text) 0.9, and and [spaCy](https://spacy.io/) 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](https://github.com/bentrevett/pytorch-pos-tagging/issues/new). I welcome any feedback, positive or negative!**

## Getting Started

To install PyTorch, see installation instructions on the [PyTorch website](pytorch.org).

To install TorchText:

``` bash
pip install torchtext
```

To install the transformers library:

```bash
pip install transformers
```

We'll also make use of spaCy to tokenize our data. To install spaCy, follow the instructions [here](https://spacy.io/usage/) making sure to install the English models:

``` bash
python -m spacy download en_core_web_sm
```

## Tutorials

* 1 - [BiLSTM for PoS Tagging](https://github.com/bentrevett/pytorch-pos-tagging/blob/master/1_bilstm.ipynb)[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bentrevett/pytorch-pos-tagging/blob/master/1_bilstm.ipynb)

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](https://github.com/bentrevett/pytorch-pos-tagging/blob/master/2_transformer.ipynb)[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bentrevett/pytorch-pos-tagging/blob/master/2_transformer.ipynb)

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

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