readme: ---
  language: en
  pipeline_tag: fill-mask
  tags: 
  - legal
  license: mit
  ---
  
  ###  InLegalBERT
  Model and tokenizer files for the InLegalBERT model from the paper [Pre-training Transformers on Indian Legal Text](https://arxiv.org/abs/2209.06049).
  
  ### 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](https://huggingface.co/nlpaueb/legal-bert-base-uncased) from the paper [LEGAL-BERT: The Muppets straight out of Law School](https://aclanthology.org/2020.findings-emnlp.261/). 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](https://huggingface.co/nlpaueb/legal-bert-base-uncased).
  This model has the same configuration as the [bert-base-uncased model](https://huggingface.co/bert-base-uncased):
  12 hidden layers, 768 hidden dimensionality, 12 attention heads, ~110M parameters.
  
  ### Usage
  Using the model to get embeddings/representations for a piece of text
  ```python
  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 Identification ([ILSI Dataset](https://arxiv.org/abs/2112.14731))[Multi-label Text Classification]: Identifying relevant statutes (law articles) based on the facts of a court case
  * Semantic Segmentation ([ISS Dataset](https://arxiv.org/abs/1911.05405))[Sentence Tagging]: Segmenting the document into 7 functional parts (semantic segments) such as Facts, Arguments, etc.
  * Court Judgment Prediction ([ILDC Dataset](https://arxiv.org/abs/2105.13562))[Binary Text Classification]: Predicting whether the claims/petitions of a court case will be accepted/rejected
  
  InLegalBERT beats LegalBERT as well as all other baselines/variants we have used, across all three tasks. For details, see our [paper](https://arxiv.org/abs/2209.06049).
  
  ### Citation
  ```
  @article{paul-2022-pretraining,
    doi = {10.48550/ARXIV.2209.06049},
    url = {https://arxiv.org/abs/2209.06049},
    author = {Paul, Shounak and Mandal, Arpan and Goyal, Pawan and Ghosh, Saptarshi},
    title = {Pre-training Transformers on Indian Legal Text},
    publisher = {arXiv}, 
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
  }
  ```
  
  ### About Us
  We are a group of researchers from the Department of Computer Science and Technology, Indian Insitute of Technology, Kharagpur. 
  Our research interests are primarily ML and NLP applications for the legal domain, with a special focus on the challenges and oppurtunites for the Indian legal scenario. 
  We have, and are currently working on several legal tasks such as:
  * named entity recognition, summarization of legal documents
  * semantic segmentation of legal documents
  * legal statute identification from facts, court judgment prediction
  * legal document matching
  
  You can find our publicly available codes and datasets [here](https://github.com/Law-AI). 