TheGod-2003/legal_QA_model

🏛️ Legal QA Model (T5-based, Indian Law)

This is a fine-tuned version of the T5 model for Question Answering in the Indian Legal domain. It was trained on curated QA samples based on Indian laws, including statutes such as IPC, CrPC, and constitutional provisions.

The model is designed to provide accurate and context-aware answers for questions grounded in Indian legal texts.

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🔍 Model Details

• Architecture: T5
• Base model: t5-base
• Task: Question Answering (QA)
• Domain: Indian Legal System
• Input format:
  question: context:

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📦 Files Included

File Name	Description
model.safetensors	Fine-tuned model weights (Git LFS)
config.json	Model configuration
tokenizer_config.json	Tokenizer configuration
spiece.model	SentencePiece tokenizer model
added_tokens.json	Additional token definitions
special_tokens_map.json	Special token mapping
generation_config.json	Generation hyperparameters

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📊 Intended Use:

🔎 Question Answering over Indian Legal texts

📜 Legal research tools and assistants

🎓 Educational tools for law students

Not Recommended For: General-purpose QA beyond the legal domain

Use as a substitute for professional legal advice

🧠 Training Details Dataset: Indian legal QA samples (IPC, CrPC, Constitution, etc.)

Model: Fine-tuned t5-base

Input length: 512 tokens

Output length: 128 tokens

Hardware: Google Colab (T4 GPU)

✅ License This model is released under the Apache-2.0 License. You are free to use, modify, and distribute it with attribution.

🤝 Citation / Credit If you use this model in your research or application, please consider citing:

@model{legal_qa_indian_t5, author = {Harsh Upadhyay}, title = {Legal QA Model using T5 (Indian Law)}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/TheGod-2003/legal_QA_model}} }

🧾 How to Use

You can load and use this model directly using the Hugging Face transformers library:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("TheGod-2003/legal_QA_model")
tokenizer = AutoTokenizer.from_pretrained("TheGod-2003/legal_QA_model")

input_text = "question: What is the punishment for theft? context: Section 378 of IPC defines theft as..."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))