InLegalBERT EU AI Act Compliance Profile

InLegalBERT

Model and tokenizer files for the InLegalBERT model from the paper Pre-training Transformers on Indian Legal Text.

Training Data

For building the pre-training corpus of Indian legal text, we collected a large corpus of case documents from the Indian Supreme Court and many High Courts of India. The court cases in our dataset range from 1950 to 2019, and belong to all legal domains, such as Civil, Criminal, Constitutional, and so on. In total, our dataset contains around 5.4 million Indian legal documents (all in the English language). The raw text corpus size is around 27 GB.

Training Setup

This model is initialized with the LEGAL-BERT-SC model from the paper LEGAL-BERT: The Muppets straight out of Law School. In our work, we refer to this model as LegalBERT, and our re-trained model as InLegalBERT. We further train this model on our data for 300K steps on the Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) tasks.

Model Overview

This model uses the same tokenizer as LegalBERT. This model has the same configuration as the bert-base-uncased model: 12 hidden layers, 768 hidden dimensionality, 12 attention heads, ~110M parameters.

Usage

Using the model to get embeddings/representations for a piece of text

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("law-ai/InLegalBERT")
text = "Replace this string with yours"
encoded_input = tokenizer(text, return_tensors="pt")
model = AutoModel.from_pretrained("law-ai/InLegalBERT")
output = model(**encoded_input)
last_hidden_state = output.last_hidden_state

Fine-tuning Results

We have fine-tuned all pre-trained models on 3 legal tasks with Indian datasets:

• Legal Statute

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