| # 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. | |
| ## Dataset Structure | |
| ``` | |
| epsilon-transformers-belief-analysis/ | |
| ├── README.md | |
| ├── models/ # Model checkpoints and configurations from S3 | |
| │ ├── {sweep_id}_{run_id}/ | |
| │ │ ├── 0.pt # Initial checkpoint | |
| │ │ ├── {final}.pt # Final checkpoint | |
| │ │ ├── run_config.yaml # Training configuration | |
| │ │ └── loss.csv # Training loss data | |
| │ └── ... | |
| └── analysis/ # Belief state regression analysis results | |
| ├── {sweep_id}_{run_id}/ | |
| │ ├── checkpoint_0.joblib # Initial checkpoint analysis | |
| │ ├── checkpoint_{final}.joblib # Final checkpoint analysis | |
| │ ├── ground_truth_data.joblib # Neural network ground truth | |
| │ ├── markov3_checkpoint_*.joblib # Classical Markov comparisons | |
| │ └── markov3_ground_truth_data.joblib # Classical ground truth | |
| └── ... | |
| ``` | |
| ## Model Mappings | |
| | Sweep ID | Run ID | Architecture | Process | Description | | |
| |----------|--------|--------------|---------|-------------| | |
| ### Mess3 | |
| | 20241121152808 | 55 | LSTM | Mess3 | LSTM trained on Mess3 | | |
| | 20241121152808 | 63 | GRU | Mess3 | GRU trained on Mess3 | | |
| | 20241121152808 | 71 | RNN | Mess3 | RNN trained on Mess3 | | |
| | 20241205175736 | 23 | Transformer | Mess3 | Transformer trained on Mess3 | | |
| ### FRDN | |
| | 20241121152808 | 53 | LSTM | FRDN | LSTM trained on FRDN | | |
| | 20241121152808 | 61 | GRU | FRDN | GRU trained on FRDN | | |
| | 20241121152808 | 69 | RNN | FRDN | RNN trained on FRDN | | |
| | 20250422023003 | 1 | Transformer | FRDN | Transformer trained on FRDN | | |
| ### Bloch Walk | |
| | 20241121152808 | 49 | LSTM | Bloch Walk | LSTM trained on Bloch Walk | | |
| | 20241121152808 | 57 | GRU | Bloch Walk | GRU trained on Bloch Walk | | |
| | 20241121152808 | 65 | RNN | Bloch Walk | RNN trained on Bloch Walk | | |
| | 20241205175736 | 17 | Transformer | Bloch Walk | Transformer trained on Bloch Walk | | |
| ### Moon Process | |
| | 20241121152808 | 48 | LSTM | Moon Process | LSTM trained on Moon Process | | |
| | 20241121152808 | 56 | GRU | Moon Process | GRU trained on Moon Process | | |
| | 20241121152808 | 64 | RNN | Moon Process | RNN trained on Moon Process | | |
| | 20250421221507 | 0 | Transformer | Moon Process | Transformer trained on Moon Process | | |
| ## 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. | |
| ### 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] | |