# Retroactive Reasoning Network (RRN) for Question Answering

## Model Description

This model implements an Retroactive Reasoning Network (RRN) for Question Answering tasks. The RRN architecture enables multi-step reasoning through an iterative refinement process that retroactively updates hidden states.

### Key Features

- **Multi-step Reasoning**: The model performs 3 reasoning steps to iteratively refine its predictions.
- **Dynamic Reasoning Steps**: Enabled - Uses a learned approach to determine the number of steps (min: 1, max: 5)
- **Gating Mechanism**: Selectively applies updates to hidden states.
- **Delta Magnitude Constraint**: Prevents destabilizing updates with a target ratio of 0.2.
- **Active Memory**: Stores and retrieves examples to enhance reasoning.

## Usage

```python
from transformers import AutoTokenizer
from model import EnhancedRRN_QA_Model

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("will4381/rrn-qa")
model = EnhancedRRN_QA_Model("will4381/rrn-qa")

# Load custom components
import torch
import os

model.qa_head.load_state_dict(torch.load(os.path.join("will4381/rrn-qa", "qa_head.pth")))
model.retroactive_update_layer.load_state_dict(torch.load(os.path.join("will4381/rrn-qa", "retroactive_layer.pth")))
model.gating_mechanism.load_state_dict(torch.load(os.path.join("will4381/rrn-qa]", "gating_mechanism.pth")))

# If using learned dynamic steps
if os.path.exists(os.path.join("will4381/rrn-qa", "step_controller.pth")) and hasattr(model, "step_controller"):
    model.step_controller.load_state_dict(torch.load(os.path.join("will4381/rrn-qa", "step_controller.pth")))

# Example usage
inputs = tokenizer("What is the capital of France?", "Paris is the capital of France.", return_tensors="pt")
outputs = model(**inputs)
```

## Training

This model was trained on the SQuAD dataset using a multi-step reasoning approach. The training code is included in the `code` directory of this repository.

To train your own model:

```bash
python code/train.py
```

To evaluate the model:

```bash
python code/test_model.py
```

## Model Architecture

The RRN architecture consists of:

1. A base language model (BERT)
2. A retroactive update layer that computes delta updates
3. A gating mechanism for selective updates
4. An enhanced QA head for answer prediction
5. A step controller for dynamic reasoning steps (if enabled)

## Evaluation Results
{'exact_match': 78.79848628193, 'f1': 86.94253357952118}