A repo for the final shared task CSCI 5832, Fall 2022. This repo attempts to solve the task of Named Entity recognition (NER) on Indian legal documents.

Sub-task B: Legal Named Entities Extraction (L-NER) More details for this task can be found here and the dataset and baseline model developed can be found here.

This repo presents 4 model architectures used for the task. These include the following:

• CRF with hand-crafted features
• BiLSTM CRF with GloVe and Flair embeddings
• BiLSTM CRF with BERT embeddings
• BERT CRF

The code for CRF with hand-crafter features is present in CRF.ipynb and the code for the rest of the models is present in legal_ner.ipynb.

The F1-scores we got for NER on judgement data are presented in the table below.

The F1-scores for preamble data are presented in the table below.

A detailed report of our findings can be found here.