Update README.md

README.md

@@ -1,79 +1,69 @@
-# Retroactive Reasoning Network (RRN) for Question Answering
-
-## Model Description
-
-This model implements an Enhanced 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("[MODEL_REPO_ID]")
-model = EnhancedRRN_QA_Model("[MODEL_REPO_ID]/base_model")
-
-# Load custom components
-import torch
-import os
-
-model.qa_head.load_state_dict(torch.load(os.path.join("[MODEL_REPO_ID]", "qa_head.pth")))
-model.retroactive_update_layer.load_state_dict(torch.load(os.path.join("[MODEL_REPO_ID]", "retroactive_layer.pth")))
-model.gating_mechanism.load_state_dict(torch.load(os.path.join("[MODEL_REPO_ID]", "gating_mechanism.pth")))
-
-# If using learned dynamic steps
-if os.path.exists(os.path.join("[MODEL_REPO_ID]", "step_controller.pth")) and hasattr(model, "step_controller"):
-    model.step_controller.load_state_dict(torch.load(os.path.join("[MODEL_REPO_ID]", "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)
-
-## Citation
-
-If you use this model in your research, please cite:
-
-```
-@article{rrn_qa_model,
-  title={Retroactive Reasoning Networks for Question Answering},
-  author={[Authors]},
-  journal={[Journal]},
-  year={2025}
-}
-```
+# 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}