Replace with clean markdown card

README.md

@@ -9,19 +9,17 @@ tags:
   - neuroscience
   - braindecode
   - convolutional
-  - transformer
   - sleep-staging
 ---
 
 # ContraWR
 
-Contrast with the World Representation ContraWR from Yang et al (2021) .
+Contrast with the World Representation ContraWR from Yang et al (2021) [Yang2021].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.ContraWR` class. **No pretrained weights are
-> distributed here** — instantiate the model and train it on your own
-> data, or fine-tune from a published foundation-model checkpoint
-> separately.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -40,134 +38,38 @@ model = ContraWR(
 )
 ```
 
-The signal-shape arguments above are example defaults — adjust them
-to match your recording.
+The signal-shape arguments above are illustrative defaults — adjust to
+match your recording.
 
 ## Documentation
-
-- Full API reference (parameters, references, architecture figure):
-  <https://braindecode.org/stable/generated/braindecode.models.ContraWR.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.ContraWR.html>
+- Interactive browser (live instantiation, parameter counts):
   <https://huggingface.co/spaces/braindecode/model-explorer>
 - Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/contrawr.py#L10>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>Contrast with the World Representation ContraWR from Yang et al (2021) [Yang2021]_.</p>
-<span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#5cb85c;color:white;font-size:11px;font-weight:600;margin-right:4px;">Convolution</span>
-
-
-
- This model is a convolutional neural network that uses a spectral
- representation with a series of convolutional layers and residual blocks.
- The model is designed to learn a representation of the EEG signal that can
- be used for sleep staging.
-
- Parameters
- ----------
- steps : int, optional
-     Number of steps to take the frequency decomposition `hop_length`
-     parameters by default 20.
- emb_size : int, optional
-     Embedding size for the final layer, by default 256.
- res_channels : list[int], optional
-     Number of channels for each residual block, by default [32, 64, 128].
- activation: nn.Module, default=nn.ELU
-     Activation function class to apply. Should be a PyTorch activation
-     module class like ``nn.ReLU`` or ``nn.ELU``. Default is ``nn.ELU``.
- drop_prob : float, default=0.5
-     The dropout rate for regularization. Values should be between 0 and 1.
-
- .. versionadded:: 0.9
-
- Notes
- -----
- This implementation is not guaranteed to be correct, has not been checked
- by original authors. The modifications are minimal and the model is expected
- to work as intended. the original code from [Code2023]_.
-
- References
- ----------
- .. [Yang2021] Yang, C., Xiao, C., Westover, M. B., & Sun, J. (2023).
-    Self-supervised electroencephalogram representation learning for automatic
-    sleep staging: model development and evaluation study. JMIR AI, 2(1), e46769.
- .. [Code2023] Yang, C., Westover, M.B. and Sun, J., 2023. BIOT
-    Biosignal Transformer for Cross-data Learning in the Wild.
-    GitHub https://github.com/ycq091044/BIOT (accessed 2024-02-13)
-
- .. rubric:: Hugging Face Hub integration
-
- When the optional ``huggingface_hub`` package is installed, all models
- automatically gain the ability to be pushed to and loaded from the
- Hugging Face Hub. Install with::
-
-     pip install braindecode[hub]
-
- **Pushing a model to the Hub:**
-
- .. code::
-     from braindecode.models import ContraWR
-
-     # Train your model
-     model = ContraWR(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-contrawr-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import ContraWR
-
-     # Load pretrained model
-     model = ContraWR.from_pretrained("username/my-contrawr-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = ContraWR.from_pretrained("username/my-contrawr-model", n_outputs=4)
-
- **Extracting features and replacing the head:**
-
- .. code::
-     import torch
-
-     x = torch.randn(1, model.n_chans, model.n_times)
-     # Extract encoder features (consistent dict across all models)
-     out = model(x, return_features=True)
-     features = out["features"]
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `steps` | int, optional | Number of steps to take the frequency decomposition `hop_length` parameters by default 20. |
+| `emb_size` | int, optional | Embedding size for the final layer, by default 256. |
+| `res_channels` | list[int], optional | Number of channels for each residual block, by default [32, 64, 128]. |
+| `activation: nn.Module, default=nn.ELU` | — | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.ELU`. |
+| `drop_prob` | float, default=0.5 | The dropout rate for regularization. Values should be between 0 and 1. |
+| `.. versionadded:: 0.9` | — | — |
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
 
-     model2 = ContraWR.from_config(config)    # reconstruct (no weights)
+## References
 
- All model parameters (both EEG-specific and model-specific such as
- dropout rates, activation functions, number of filters) are automatically
- saved to the Hub and restored when loading.
+1. Yang, C., Xiao, C., Westover, M. B., & Sun, J. (2023). Self-supervised electroencephalogram representation learning for automatic sleep staging: model development and evaluation study. JMIR AI, 2(1), e46769.
+2. Yang, C., Westover, M.B. and Sun, J., 2023. BIOT Biosignal Transformer for Cross-data Learning in the Wild. GitHub https://github.com/ycq091044/BIOT (accessed 2024-02-13)
 
- See :ref:`load-pretrained-models` for a complete tutorial.</main>
-</div>
 
 ## Citation
 
-Please cite both the original paper for this architecture (see the
-*References* section above) and braindecode:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,