README.md

BERT Language Model (LM) with TensorFlow

This project demonstrates fine-tuning a BERT model for language modeling using TensorFlow. Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture.

Prerequisites

• TensorFlow 2.x
• Hugging Face Transformers library
• Datasets library from Hugging Face

Setup

1. Install the required dependencies:

pip install -r requirements.txt

2. OPTIONAL: Train model on your data you want

   • First: edit models/dataset.json to your liking.
   • Second: train the model, run:
     

      python scripts/train.py

3. You can run tests of your trained BERT model via the tests/ directory.

4. The model will be saved to the trained_model/ directory.

Notes

This is a simple setup for fine-tuning BERT. You can modify it for larger datasets or more advanced features such as early stopping, distributed training, etc.