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aimlnerd/legal-entity-ner-transformers

sourceHugging Faceupdated 3y agoView on Hugging Face

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App README

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

This code fine tune the BERT model and uploads to huggingface