add readme

README.md

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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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/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