metadata

title: Legal Entity NER - Transformers
emoji: 🏆
colorFrom: green
colorTo: gray
sdk: docker
pinned: true
app_file: gradio_ner.py
models:
  - aimlnerd/bert-finetuned-legalentity-ner-accelerate
tags:
  - bert
  - tokenclassification
  - ner
  - transfomers
python_version: 3.11.5

Extract Legal Entities from Insurance Documents using BERT transfomers

This space use fine tuned BERT transfomers for NER of legal entities in Life Insurance demand letters.

Dataset is publicly available here https://github.com/aws-samples/aws-legal-entity-extraction.git

The model extracts the following entities:

Law Firm
Law Office Address
Insurance Company
Insurance Company Address
Policy Holder Name
Beneficiary Name
Policy Number
Payout
Required Action
Sender

Dataset consists of legal requisition/demand letters for Life Insurance, however this approach can be used across any industry & document which may benefit from spatial data in NER training.

Data preprocessing

The OCRed data is present as JSON here data/raw_data/annotations. I wrote this code to convert the JSON data in format suitable for HF TokenClassification source/services/ner/preprocess/awscomprehend_2_ner_format.py

Finetuning BERT Transformers model

source/services/ner/train/train.py This code fine tune the BERT model and uploads to huggingface