This toolkit improves the performance of HuggingFace transformer models on downstream NLP tasks, by domain-adapting models to the target domain of said NLP tasks (e.g. BERT -> LawBERT).

The overall Domain Adaptation framework can be broken down into three phases:

1. Data Selection

   Select a relevant subset of documents from the in-domain corpus that is likely to be beneficial for domain pre-training (see below)

2. Vocabulary Augmentation

   Extending the vocabulary of the transformer model with domain specific-terminology

3. Domain Pre-Training

   Continued pre-training of transformer model on the in-domain corpus to learn linguistic nuances of the target domain

After a model is domain-adapted, it can be fine-tuned on the downstream NLP task of choice, like any pre-trained transformer model.

This toolkit provides two classes, DataSelector and VocabAugmentor, to simplify the Data Selection and Vocabulary Augmentation steps respectively.

This package was developed on Python 3.6+ and can be downloaded using pip:

pip install transformers-domain-adaptation

• Compatible with the HuggingFace ecosystem:
  • transformers 4.x
  • tokenizers
  • datasets

Please refer to our Colab guide!

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