Model Name: IBPS – Indian Bail Prediction System v0.2
This repository provides fine-tuned models developed as part of the Indian Bail Prediction System (IBPS), a research project focused on analyzing Indian High Court bail judgments.
The released models support two tasks:
Bail Outcome Classification: Predicts the bail outcome (Grant/Deny) for Regular and Anticipatory Bail applications from structured legal case attributes. Judicial Reasoning Generation: Generates concise judicial reasoning based on structured case facts, following the style of Indian High Court bail orders. Intended Use
These models are intended for:
Legal AI research Benchmarking legal language models Explainable AI for judicial decision support Academic research on legal NLP
They are not intended for real-world judicial decision-making or legal advice.
Training Data
The models were trained on a curated dataset of Indian High Court bail judgments collected from multiple High Courts. The dataset contains structured legal attributes extracted from judgments, including:
Application type Statutory provisions Criminal history Health conditions Incident details Judicial reasoning Bail outcome Tasks
- Bail Outcome Classification
Input: Structured legal case attributes
Output: Predicted bail outcome
Regular Bail Granted Regular Bail Denied Anticipatory Bail Granted Anticipatory Bail Denied 2. Judicial Reasoning Generation
Input: Structured legal case attributes
Output: A concise judicial reasoning explaining the predicted bail decision.
Limitations Trained only on Indian High Court bail cases. Performance may not generalize to other legal domains or jurisdictions. Generated reasoning reflects learned patterns from historical judgments and should not be interpreted as legal advice. Predictions should be used only for research and educational purposes. Citation
If you use these models, please cite the corresponding IBPS publication (or thesis, if the paper is not yet available).
License
For research and academic use only. Please comply with the licensing terms of the underlying base model and the dataset used for fine-tuning.
This model card follows Hugging Face conventions while clearly communicating the model's purpose, scope, and limitations.