Update README.md

README.md

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 # Epsilon-Transformers Belief Analysis Dataset
 
 This dataset contains trained neural network models and their corresponding belief state regression analysis from the Epsilon-Transformers project. The models were trained on four different stochastic processes and analyzed for their ability to learn and represent belief states.
+See https://github.com/adamimos/epsilon-transformers/tree/quantum-public for codebase which generated this data.
 
 ## Dataset Structure
 
@@ -61,92 +62,13 @@ epsilon-transformers-belief-analysis/
 | 20250421221507 | 0 | Transformer | Moon Process | Transformer trained on Moon Process |
 | 20250422023003 | 1 | Transformer | FRDN | Transformer trained on FRDN |
 
-## Process Descriptions
-
-### Mess3 (Classical Process)
-A classical stochastic process used as a baseline for comparison with quantum processes.
-
-### FRDN (Finite Random Dynamics Networks)
-A quantum process representing finite random dynamics networks, modeling quantum systems with specific structural properties.
-
-### Bloch Walk
-A quantum random walk process on the Bloch sphere, representing quantum state evolution in a geometric framework.
-
-### Moon Process
-A post-quantum stochastic process that explores computational mechanics beyond standard quantum frameworks.
-
-## Model Architectures
-
-### RNN Models (LSTM, GRU, RNN)
-- **Layers**: 4
-- **Hidden Units**: 64
-- **Direction**: Unidirectional
-- **Configuration**: L4_H64_uni
-
-### Transformer Models
-- **Layers**: 4
-- **Attention Heads**: 4
-- **Head Dimension**: 16
-- **Model Dimension**: 64
-- **Configuration**: L4_H4_DH16_DM64
-
 ## File Formats
 
 ### Model Files (.pt)
-PyTorch model checkpoints containing trained model weights and optimizer states.
+Transformerlens (for transformers) or Pytorch (for RNNs) model checkpoints containing trained model weights and optimizer states.
 
 ### Analysis Files (.joblib)
 Joblib-serialized files containing:
 - **checkpoint_*.joblib**: Regression analysis results mapping activations to belief states
 - **ground_truth_data.joblib**: True belief states and probabilities for the neural network data
-- **markov3_*.joblib**: Classical Markov model comparisons and baselines
-
-## Usage
-
-### Loading Models
-
-```python
-import torch
-from pathlib import Path
-
-# Load a model checkpoint
-model_path = Path("models/20241121152808_57/4075724800.pt")
-checkpoint = torch.load(model_path, map_location='cpu')
-```
-
-### Loading Analysis Data
-
-```python
-import joblib
-from pathlib import Path
-
-# Load regression analysis results
-analysis_path = Path("analysis/20241121152808_57/checkpoint_4075724800.joblib")
-analysis_data = joblib.load(analysis_path)
-
-# Access layer-wise regression metrics
-for layer, metrics in analysis_data.items():
-    print(f"Layer {layer} RMSE: {metrics['rmse']}")
-```
-
-## Citation
-
-If you use this dataset in your research, please cite:
-
-```bibtex
-@misc{epsilon-transformers-belief-analysis,
-  title={Epsilon-Transformers Belief Analysis Dataset},
-  author={[Your Name]},
-  year={2024},
-  howpublished={Hugging Face Datasets},
-  url={https://huggingface.co/datasets/[your-username]/epsilon-transformers-belief-analysis}
-}
-```
-
-## License
-
-[Specify your license here]
-
-## Contact
-
-[Your contact information]
+- **markov3_*.joblib**: Classical Markov model comparisons and baselines