---
library_name: peft
tags:
- legal
- nlp
- classification
- inlegalbert
- lora
- pytorch
datasets:
- L-NLProc/PredEx_Instruction-Tuning_Pred-Exp
base_model: law-ai/InLegalBERT
---

# Model Card for InLegalBERT-Appeal-Predictor-LoRA

## Model Details

### Model Description

This model is a fine-tuned version of `law-ai/InLegalBERT` designed to predict the outcome of legal appeals (Accepted vs. Rejected) based on the text of the legal judgment. It utilizes Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning (PEFT), making it highly efficient while retaining strong predictive capabilities on complex, domain-specific Indian legal texts. 

To handle extremely long legal documents, this model utilizes a specialized "Head+Tail" truncation strategy, analyzing the first 128 tokens and the last 383 tokens of a document to capture both the introductory context and the final verdict rationale.

- **Developed by:** Swastik Sharma (swastik7805 / Beelzi)
- **Model type:** Sequence Classification (PEFT/LoRA)
- **Language(s) (NLP):** English (Indian Legal Context)
- **Finetuned from model:** `law-ai/InLegalBERT`

## Uses

### Direct Use

The model is intended for legal researchers, practitioners, and automated legal analysis pipelines to predict the likelihood of a legal appeal being accepted (Class 1) or rejected (Class 0) based on the textual content of the case. 

### Out-of-Scope Use

This model should not be used as a substitute for professional legal counsel. The predictions are statistically generated based on historical data and do not guarantee actual future court outcomes. 

## How to Get Started with the Model

Use the code below to get started with the model. Because the model was trained using a specific Head+Tail truncation strategy for texts exceeding 512 tokens, it is highly recommended to apply the same truncation during inference:

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel

base_model_name = "law-ai/InLegalBERT"
peft_model_id = "Beelzi/InLegalBERT-Appeal-Predictor-LoRA"

tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
base_model = AutoModelForSequenceClassification.from_pretrained(base_model_name, num_labels=2)
model = PeftModel.from_pretrained(base_model, peft_model_id)

def predict_long_text(text):
    tokens = tokenizer(text, truncation=False, return_tensors="pt")
    input_ids = tokens["input_ids"]
    attention_mask = tokens["attention_mask"]

    if input_ids.shape[1] > 512:
        head_ids = input_ids[:, :128]
        head_mask = attention_mask[:, :128]
        tail_ids = input_ids[:, -383:]
        tail_mask = attention_mask[:, -383:]
        
        sep_id = torch.tensor([[tokenizer.sep_token_id]], device=input_ids.device)
        sep_mask = torch.tensor([[1]], device=attention_mask.device)
        
        input_ids = torch.cat([head_ids, tail_ids, sep_id], dim=1)
        attention_mask = torch.cat([head_mask, tail_mask, sep_mask], dim=1)

    input_ids = input_ids.to(model.device)
    attention_mask = attention_mask.to(model.device)

    model.eval()
    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
        logits = outputs.logits
        final_pred = torch.argmax(logits, dim=1).item()

    return "Accepted" if final_pred == 1 else "Rejected"