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AistrighNLP - A collection of NLP tools for Irish

Project description



AistrighNLP is a collection of tools and models used for Aistrigh, the BT Young Scientist 2021 project. Our aim is to bring Irish into the modern era with NLP tools to give it parity with English. The tools included are based around the work in Neural Models for Predicting Celtic Mutations (Scannell, 2020). Included is all the tools needed to create a demutated Irish corpus, which can be used in all sorts of NLP tasks, and a model to reinsert them. For the Python API docs visit AistrighNLP Python API

Installing the Package

AistrighNLP can be downloaded using pip

pip install aistrigh-nlp


When lowercasing either Irish or Scots Gaelic for prediciting mutations, you must be aware of special cases outlined in the paper above. Our lowercaser handles that

aistrigh-nlp lowercase -i input.txt -o output.txt

Removing mutations

To remove mutations from an entire dataset for use for NLP tasks (like Machine Translation) use demutate-corpus

aistrigh-nlp demutate-corpus -i input.txt -o output.txt

To remove mutations with a 'window' on either side to train a neural network, use demutate-window, with -w set to your desired window length on each side

aistrigh-nlp demutate-window -i input.txt -o output.csv -w 15

Predicting the mutations

To predict mutations on each word, use predict-mutations. As of right now, it's only compatible with PyTorch+Torchtext/PyTorch models but we are working on expanding to TensorFlow and Keras. You'll need your vocab, labels and model checkpoint. We provide a default model to be used.

aistrigh-nlp predict-mutations -i input.txt -o output.txt -w 15 -v my_vocab.pth -l my_labels.pth -m

Applying the predicted mutations

To apply the mutations predicted by predict-mutations, use apply-mutations.

aistrigh-nlp apply-mutations -i input.txt -o output.txt -skip

-skip here means to skip mutating any tokens containing symbols. It's unlikely these tokens need remutating, and will likely hurt performance without this set.


AistrighNLP uses PyTorch Traces to save the full computational graphs as checkpoints. This way, the model architecture need not be declared into hard-coded scripts. See this StackOverflow Thread for instructions to save a traced checkpoint.

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