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README.md

@@ -14,13 +14,12 @@ tags:
 
 # SleepStagerBlanco2020
 
-Sleep staging architecture from Blanco et al (2020) from
+Sleep staging architecture from Blanco et al (2020) from [Blanco2020]
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.SleepStagerBlanco2020` 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
 
@@ -39,139 +38,44 @@ model = SleepStagerBlanco2020(
 )
 ```
 
-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.SleepStagerBlanco2020.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SleepStagerBlanco2020.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/sleep_stager_blanco_2020.py#L11>
 
-## 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 Blanco et al (2020) from [Blanco2020]_</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>
-
-
-
- .. figure:: https://media.springernature.com/full/springer-static/image/art%3A10.1007%2Fs00500-019-04174-1/MediaObjects/500_2019_4174_Fig2_HTML.png
-     :align: center
-     :alt: SleepStagerBlanco2020 Architecture
-
- Convolutional neural network for sleep staging described in [Blanco2020]_.
- A series of seven convolutional layers with kernel sizes running down from 7 to 3,
- in an attempt to extract more general features at the beginning, while more specific
- and complex features were extracted in the final stages.
-
- Parameters
- ----------
- n_conv_chans : int
-     Number of convolutional channels. Set to 20 in [Blanco2020]_.
- n_groups : int
-     Number of groups for the convolution. Set to 2 in [Blanco2020]_ for 2 Channel EEG.
-     controls the connections between inputs and outputs. n_channels and n_conv_chans must be
-     divisible by n_groups.
- drop_prob : float
-     Dropout rate before the output dense layer.
- apply_batch_norm : bool
-     If True, apply batch normalization after both temporal convolutional
-     layers.
- 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_channels : int
-     Alias for `n_chans`.
- 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``.
-
- References
- ----------
- .. [Blanco2020] Fernandez-Blanco, E., Rivero, D. & Pazos, A. Convolutional
-     neural networks for sleep stage scoring on a two-channel EEG signal.
-     Soft Comput 24, 4067–4079 (2020). https://doi.org/10.1007/s00500-019-04174-1
-
- .. 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 SleepStagerBlanco2020
-
-     # Train your model
-     model = SleepStagerBlanco2020(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-sleepstagerblanco2020-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import SleepStagerBlanco2020
-
-     # Load pretrained model
-     model = SleepStagerBlanco2020.from_pretrained("username/my-sleepstagerblanco2020-model")
 
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = SleepStagerBlanco2020.from_pretrained("username/my-sleepstagerblanco2020-model", n_outputs=4)
+## Architecture
 
- **Extracting features and replacing the head:**
+![SleepStagerBlanco2020 architecture](https://media.springernature.com/full/springer-static/image/art%3A10.1007%2Fs00500-019-04174-1/MediaObjects/500_2019_4174_Fig2_HTML.png)
 
- .. 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"]
+## Parameters
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
+| Parameter | Type | Description |
+|---|---|---|
+| `n_conv_chans` | int | Number of convolutional channels. Set to 20 in [Blanco2020]. |
+| `n_groups` | int | Number of groups for the convolution. Set to 2 in [Blanco2020] for 2 Channel EEG. controls the connections between inputs and outputs. n_channels and n_conv_chans must be divisible by n_groups. |
+| `drop_prob` | float | Dropout rate before the output dense layer. |
+| `apply_batch_norm` | bool | If True, apply batch normalization after both temporal convolutional layers. |
+| `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_channels` | int | Alias for `n_chans`. |
+| `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`. |
 
- **Saving and restoring full configuration:**
 
- .. code::
-     import json
+## References
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
+1. Fernandez-Blanco, E., Rivero, D. & Pazos, A. Convolutional neural networks for sleep stage scoring on a two-channel EEG signal. Soft Comput 24, 4067–4079 (2020). https://doi.org/10.1007/s00500-019-04174-1
 
-     model2 = SleepStagerBlanco2020.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:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,