Question Answering
Transformers
Safetensors
English
phi
text-generation
legal
supreme-court
indian-law
extractive-qa
fine-tuned
text-generation-inference
Instructions to use shivvamm/phi-1.5-mini-judgements-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shivvamm/phi-1.5-mini-judgements-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="shivvamm/phi-1.5-mini-judgements-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shivvamm/phi-1.5-mini-judgements-finetuned") model = AutoModelForCausalLM.from_pretrained("shivvamm/phi-1.5-mini-judgements-finetuned") - Notebooks
- Google Colab
- Kaggle
🏛️ SC Legal QA Model
This model is a legal question-answering model fine-tuned on Indian Supreme Court judgments. It is designed to answer law-related questions such as doctrines, legal principles, precedents, and statutory interpretation based on input context.
🧠 Model Details
- Architecture: Transformer-based (e.g.,
bert-base-uncasedor your base model) - Fine-tuned on: Curated and parsed Indian Supreme Court case texts
- Task: Extractive Question Answering
- Language: English
- Domain: Indian Law, Constitutional Law, Civil & Criminal Judgments
🚀 How to Use
from transformers import pipeline
qa_pipeline = pipeline("question-answering", model="shivvamm/phi-1.5-mini-judgements-finetuned")
context = """
The basic structure doctrine is a judicial principle that certain fundamental features of the Constitution of India cannot be altered by amendments by the Parliament.
"""
question = "What is the basic structure doctrine?"
result = qa_pipeline(question=question, context=context)
print(result['answer'])
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