BERT Fine-Tuning

This repository contains Jupyter notebooks for fine-tuning BERT models for specific NLP tasks:

1. Medical Transcripts Question Answering (MTQA)
2. Cybersecurity Named Entity Recognition (CyberNER)

The notebooks provide step-by-step guidance on loading pre-trained BERT models, preparing datasets, and fine-tuning for these specific tasks.

Notebooks

1. Medical Transcripts QA (MTQA)

• MTQA_FineTuning.ipynb: Fine-tunes a BERT model for Medical Transcripts QA and demonstrates how to use the fine-tuned model for inference.

2. Cybersecurity NER (CyberNER)

• CyberNER_FineTuning.ipynb: Fine-tunes a BERT model for Cybersecurity NER and demonstrates how to use the fine-tuned model for inference.

Getting Started

Click on Open in Colab within the notebook in GitHub to get started.

Datasets

The data directory contains datasets for MTQA and CyberNER.

Issues

If you encounter any issues or have suggestions, please open a GitHub issue. Your feedback is valuable.