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---
base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit
library_name: peft
license: mit
datasets:
- axondendriteplus/context-relevance-classifier-dataset
language:
- en
pipeline_tag: text-classification
tags:
- legal
---

# llama-3.2-1b-context-relevance-classifier

This is a supervised fine-tuned version of `unsloth/llama-3.2-1b-instruct-bnb-4bit`, trained to classify whether an answer is derived from a given context based on a legal question.

- **Task**: Binary classification
- **Input**: question, answer, context  
- **Output**: YES (answer is from context) or NO (answer is not from context)
- **Domain**: Legal Q&A context relevance detection

## Dataset

Fine-tuned on: [axondendriteplus/context-relevance-classifier-dataset](https://huggingface.co/datasets/axondendriteplus/context-relevance-classifier-dataset)

Each sample contains:
```json
{
 "question": "What is the legal definition of contract?",
 "answer": "A contract is a legally binding agreement between two parties.",
 "context": "Contract law defines a contract as an agreement between two or more parties that creates legally enforceable obligations.",
 "label": 1
}
```

## Training Configuration

- **Base model**: `unsloth/llama-3.2-1b-instruct-bnb-4bit`  
- **Training method**: **LoRA fine-tuning using Unsloth**  
- **Sequence length**: **2048**  
- **Epochs**: **3**  
- **Batch size**: **2** (gradient accumulation: **4**)  
- **Optimizer**: **AdamW (8-bit)**  
- **Learning rate**: **2e-4**  
- **Weight decay**: **0.01**  
- **Warmup steps**: **50**  
- **LoRA config**: **r=16**, **alpha=16**, **dropout=0**  
- **Target modules**: `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]`

## Prompt Format
``` json
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a context relevance classifier. Given a question, answer, and context, determine if the answer was generated from the given context. Respond with either "YES" if the answer is derived from the context, or "NO" if it is not.

<|eot_id|><|start_header_id|>user<|end_header_id|>
Question: {question}

Answer: {answer}

Context: {context}

Was this answer generated from the given context? Respond with YES or NO only.
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{YES/NO}
```

### Installation
```json
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes
```

### Usage
Basic Inference - You can also use inference_SFT.py in files!

```python
from unsloth import FastLanguageModel
import torch

# Load the fine-tuned model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="axondendriteplus/llama-3.2-1b-context-relevance-classifier",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

def classify_answer(question, answer, context):
    """Classify if answer is generated from context"""
    prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a context relevance classifier. Given a question, answer, and context, determine if the answer was generated from the given context. Respond with either "YES" if the answer is derived from the context, or "NO" if it is not.

<|eot_id|><|start_header_id|>user<|end_header_id|>
Question: {question}

Answer: {answer}

Context: {context}

Was this answer generated from the given context? Respond with YES or NO only.
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""
    
    inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=5,
            use_cache=True,
            do_sample=False,
            repetition_penalty=1.1,
            eos_token_id=tokenizer.eos_token_id,
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    prediction = response.split("assistant")[-1].strip()
    
    return "YES" in prediction.upper()

# Example usage
question = "What is the legal definition of contract?"
answer = "A contract is a legally binding agreement between two parties."
context = "Contract law defines a contract as an agreement between two or more parties that creates legally enforceable obligations."

result = classify_answer(question, answer, context)
print(f"Classification result: {'Relevant' if result else 'Not Relevant'}")
```

### Usage Examples
Example 1: Relevant Answer
```python
question = "What are the elements of a valid contract?"
answer = "A valid contract requires offer, acceptance, and consideration."
context = "For a contract to be legally binding, it must contain three essential elements: an offer, acceptance of that offer, and consideration."
# Expected output: YES (Relevant)
```
Example 2: Irrelevant Answer
```python
question = "What is negligence in tort law?"
answer = "A contract is a legally binding agreement."
context = "Negligence is the failure to exercise reasonable care that results in harm to another person."
# Expected output: NO (Not Relevant)
```
Example 3: Partially Relevant
```python
question = "What is the statute of limitations?"
answer = "It's a time limit for filing lawsuits, typically 2-3 years."
context = "The statute of limitations is a law that sets the maximum time after an event within which legal proceedings may be initiated."
# Expected output: YES (Relevant)
```

The model is trained to distinguish between:

Positive examples: Answers that are derived from or supported by the given context
Negative examples: Answers that are not supported by the context (generated using GPT-4o-mini or context mismatching)

### Training dataset composition:

- **Total examples**: ~3.2K
- **Positive examples**: ~1.6K (answers generated from context)
- **Negative examples**: ~1.6K (wrong answers or mismatched contexts)