Replace with clean markdown card

README.md

@@ -9,19 +9,17 @@ tags:
   - neuroscience
   - braindecode
   - foundation-model
-  - transformer
   - sleep-staging
 ---
 
 # BIOT
 
-BIOT from Yang et al (2023)
+BIOT from Yang et al (2023) [Yang2023]
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.BIOT` 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
 
@@ -40,184 +38,43 @@ model = BIOT(
 )
 ```
 
-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.BIOT.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.BIOT.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/biot.py#L56>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>BIOT from Yang et al (2023) [Yang2023]_</p>
-<span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#d9534f;color:white;font-size:11px;font-weight:600;margin-right:4px;">Foundation Model</span>
-
-
-
- .. figure:: https://braindecode.org/dev/_static/model/biot.jpg
-    :align: center
-    :alt: BioT
-
- BIOT: Cross-data Biosignal Learning in the Wild.
-
- BIOT is a foundation model for biosignal classification. It is
- a wrapper around the `BIOTEncoder` and `ClassificationHead` modules.
-
- It is designed for N-dimensional biosignal data such as EEG, ECG, etc.
- The method was proposed by Yang et al. [Yang2023]_ and the code is
- available at [Code2023]_
-
- The model is trained with a contrastive loss on large EEG datasets
- TUH Abnormal EEG Corpus with 400K samples and Sleep Heart Health Study
- 5M. Here, we only provide the model architecture, not the pre-trained
- weights or contrastive loss training.
-
- The architecture is based on the `LinearAttentionTransformer` and
- `PatchFrequencyEmbedding` modules.
- The `BIOTEncoder` is a transformer that takes the input data and outputs
- a fixed-size representation of the input data. More details are
- present in the `BIOTEncoder` class.
-
- The `ClassificationHead` is an ELU activation layer, followed by a simple
- linear layer that takes the output of the `BIOTEncoder` and outputs
- the classification probabilities.
-
- .. important::
-    **Pre-trained Weights Available**
-
-    This model has pre-trained weights available on the Hugging Face Hub.
-    You can load them using:
-
-    .. code:: python
-        from braindecode.models import BIOT
-
-        # Load the original pre-trained model from Hugging Face Hub
-        # For 16-channel models:
-        model = BIOT.from_pretrained("braindecode/biot-pretrained-prest-16chs")
-
-        # For 18-channel models:
-        model = BIOT.from_pretrained("braindecode/biot-pretrained-shhs-prest-18chs")
-        model = BIOT.from_pretrained("braindecode/biot-pretrained-six-datasets-18chs")
-
-    To push your own trained model to the Hub:
-
-    .. code:: python
-        # After training your model
-        model.push_to_hub(
-            repo_id="username/my-biot-model", commit_message="Upload trained BIOT model"
-        )
-
-    Requires installing ``braindecode[hug]`` for Hub integration.
-
- .. versionadded:: 0.9
-
- Parameters
- ----------
- embed_dim : int, optional
-     The size of the embedding layer, by default 256
- num_heads : int, optional
-     The number of attention heads, by default 8
- num_layers : int, optional
-     The number of transformer layers, by default 4
- 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``.
- return_feature: bool, optional
-     Changing the output for the neural network. Default is single tensor
-     when return_feature is True, return embedding space too.
-     Default is False.
- hop_length: int, optional
-     The hop length for the torch.stft transformation in the
-     encoder. The default is 100.
- sfreq: int, optional
-     The sfreq parameter for the encoder. The default is 200
-
- References
- ----------
- .. [Yang2023] Yang, C., Westover, M.B. and Sun, J., 2023, November. BIOT:
-    Biosignal Transformer for Cross-data Learning in the Wild. In Thirty-seventh
-    Conference on Neural Information Processing Systems, NeurIPS.
- .. [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 BIOT
-
-     # Train your model
-     model = BIOT(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-biot-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import BIOT
-
-     # Load pretrained model
-     model = BIOT.from_pretrained("username/my-biot-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = BIOT.from_pretrained("username/my-biot-model", n_outputs=4)
-
- **Extracting features and replacing the head:**
 
- .. code::
-     import torch
+## Architecture
 
-     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"]
+![BIOT architecture](https://braindecode.org/dev/_static/model/biot.jpg)
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `embed_dim` | int, optional | The size of the embedding layer, by default 256 |
+| `num_heads` | int, optional | The number of attention heads, by default 8 |
+| `num_layers` | int, optional | The number of transformer layers, by default 4 |
+| `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`. |
+| `return_feature: bool, optional` | — | Changing the output for the neural network. Default is single tensor when return_feature is True, return embedding space too. Default is False. |
+| `hop_length: int, optional` | — | The hop length for the torch.stft transformation in the encoder. The default is 100. |
+| `sfreq: int, optional` | — | The sfreq parameter for the encoder. The default is 200 |
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
 
-     model2 = BIOT.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., Westover, M.B. and Sun, J., 2023, November. BIOT: Biosignal Transformer for Cross-data Learning in the Wild. In Thirty-seventh Conference on Neural Information Processing Systems, NeurIPS.
+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,