Instructions to use Rak-shit/Naya-Model-Summeriser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rak-shit/Naya-Model-Summeriser with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Rak-shit/Naya-Model-Summeriser")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Rak-shit/Naya-Model-Summeriser", dtype="auto") - Notebooks
- Google Colab
- Kaggle
NayaLLM – Legal Document Summarization Model
This repository contains a Legal-BERT–based model fine-tuned for abstractive summarization of Indian legal case documents.
Model Details
- Base Model: Legal-BERT
- Task: Legal text summarization
- Domain: Indian legal judgments and case documents
Pipeline Overview
- Legal document chunking
- Transformer-based embeddings
- Abstractive summarization
- Retrieval-ready outputs for RAG systems
Datasets
- NayaAnumana Dataset
Intended Use
- Legal document summarization
- Retrieval-Augmented Generation (RAG) pipelines
- Legal research assistance
Limitations
- Trained on domain-specific legal text
- Not suitable for general-purpose summarization
Author
Rakshit Gupta and Mayank Shukla