Add architecture-only model card

README.md

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+---
+license: bsd-3-clause
+library_name: braindecode
+pipeline_tag: feature-extraction
+tags:
+  - eeg
+  - biosignal
+  - pytorch
+  - neuroscience
+  - braindecode
+  - convolutional
+  - transformer
+  - sleep-staging
+---
+
+# AttnSleep
+
+Sleep Staging Architecture from Eldele et al  (2021) .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.AttnSleep` 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.
+
+## Quick start
+
+```bash
+pip install braindecode
+```
+
+```python
+from braindecode.models import AttnSleep
+
+model = AttnSleep(
+    n_chans=2,
+    sfreq=100,
+    input_window_seconds=30.0,
+    n_outputs=5,
+)
+```
+
+The signal-shape arguments above are example defaults — adjust them
+to match your recording.
+
+## Documentation
+
+- Full API reference (parameters, references, architecture figure):
+  <https://braindecode.org/stable/generated/braindecode.models.AttnSleep.html>
+- Interactive browser with live instantiation:
+  <https://huggingface.co/spaces/braindecode/model-explorer>
+- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/attn_sleep.py#L18>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>Sleep Staging Architecture from Eldele et al  (2021) [Eldele2021]_.</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><span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#56B4E9;color:white;font-size:11px;font-weight:600;margin-right:4px;">Attention/Transformer</span>
+
+
+
+ .. figure:: https://raw.githubusercontent.com/emadeldeen24/AttnSleep/refs/heads/main/imgs/AttnSleep.png
+     :align: center
+     :alt: AttnSleep Architecture
+
+ Attention based Neural Net for sleep staging as described in [Eldele2021]_.
+ The code for the paper and this model is also available at [1]_.
+ Takes single channel EEG as input.
+ Feature extraction module based on multi-resolution convolutional neural network (MRCNN)
+ and adaptive feature recalibration (AFR).
+ The second module is the temporal context encoder (TCE) that leverages a multi-head attention
+ mechanism to capture the temporal dependencies among the extracted features.
+
+ Warning - This model was designed for signals of 30 seconds at 100Hz or 125Hz (in which case
+ the reference architecture from [1]_ which was validated on SHHS dataset [2]_ will be used)
+ to use any other input is likely to make the model perform in unintended ways.
+
+ Parameters
+ ----------
+ n_tce : int
+     Number of TCE clones.
+ d_model : int
+     Input dimension for the TCE.
+     Also the input dimension of the first FC layer in the feed forward
+     and the output of the second FC layer in the same.
+     Increase for higher sampling rate/signal length.
+     It should be divisible by n_attn_heads
+ d_ff : int
+     Output dimension of the first FC layer in the feed forward and the
+     input dimension of the second FC layer in the same.
+ n_attn_heads : int
+     Number of attention heads. It should be a factor of d_model
+ drop_prob : float
+     Dropout rate in the PositionWiseFeedforward layer and the TCE layers.
+ after_reduced_cnn_size : int
+     Number of output channels produced by the convolution in the AFR module.
+ return_feats : bool
+     If True, return the features, i.e. the output of the feature extractor
+     (before the final linear layer). If False, pass the features through
+     the final linear layer.
+ n_classes : int
+     Alias for `n_outputs`.
+ input_size_s : float
+     Alias for `input_window_seconds`.
+ activation : nn.Module, default=nn.ReLU
+     Activation function class to apply. Should be a PyTorch activation
+     module class like ``nn.ReLU`` or ``nn.ELU``. Default is ``nn.ReLU``.
+ activation_mrcnn : nn.Module, default=nn.ReLU
+     Activation function class to apply in the Mask R-CNN layer.
+     Should be a PyTorch activation module class like ``nn.ReLU`` or
+     ``nn.GELU``. Default is ``nn.GELU``.
+
+ References
+ ----------
+ .. [Eldele2021] E. Eldele et al., "An Attention-Based Deep Learning Approach for Sleep Stage
+     Classification With Single-Channel EEG," in IEEE Transactions on Neural Systems and
+     Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.
+
+ .. [1] https://github.com/emadeldeen24/AttnSleep
+
+ .. [2] https://sleepdata.org/datasets/shhs
+
+ .. 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 AttnSleep
+
+     # Train your model
+     model = AttnSleep(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-attnsleep-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import AttnSleep
+
+     # Load pretrained model
+     model = AttnSleep.from_pretrained("username/my-attnsleep-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = AttnSleep.from_pretrained("username/my-attnsleep-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:**
+
+ .. code::
+     import json
+
+     config = model.get_config()            # all __init__ params
+     with open("config.json", "w") as f:
+         json.dump(config, f)
+
+     model2 = AttnSleep.from_config(config)    # reconstruct (no weights)
+
+ 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.
+
+ 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:
+
+```bibtex
+@article{aristimunha2025braindecode,
+  title   = {Braindecode: a deep learning library for raw electrophysiological data},
+  author  = {Aristimunha, Bruno and others},
+  journal = {Zenodo},
+  year    = {2025},
+  doi     = {10.5281/zenodo.17699192},
+}
+```
+
+## License
+
+BSD-3-Clause for the model code (matching braindecode).
+Pretraining-derived weights, if you fine-tune from a checkpoint,
+inherit the licence of that checkpoint and its training corpus.