Update README.md

README.md

@@ -2,140 +2,31 @@
 license: mit
 library_name: braindecode
 tags:
-  - eeg
-  - foundation-model
-  - self-supervised
-  - signal-jepa
-pipeline_tag: feature-extraction
+  - deprecated
 ---
 
-![sjepa](https://cdn-uploads.huggingface.co/production/uploads/646e0135174cc96d509582a6/DS-cXrFyxZ78hK48ft0iU.png)
+# ⚠️ This repository is deprecated
 
-# Signal-JEPA
+The weights in this repository are kept online for archival purposes only. They
+are **not** compatible with current releases of
+[braindecode](https://braindecode.org/) and will not receive updates.
 
-Self-supervised pre-trained weights for the Signal-JEPA foundation model from
-[Guetschel et al. (2024)](https://arxiv.org/abs/2403.11772), packaged for use
-with [braindecode](https://braindecode.org/).
+## Please use the new repositories
 
-The model was pre-trained on the Lee2019 dataset (62 EEG channels in the
-10-10 layout, sampled at 128 Hz). The repo ships the weights together with a
-`config.json` so they can be loaded in one line with
-`YourModelClass.from_pretrained(repo_id, ...)`.
+The Signal-JEPA weights have been republished:
 
-## Available checkpoints
-
-Two variants are published:
-
-| repo ID | channel embedding included | when to use |
-| --- | --- | --- |
-| [`braindecode/signal-jepa`](https://huggingface.co/braindecode/signal-jepa) | ✓ 62-row `_ChannelEmbedding` aligned with the pre-training layout | your recording channels are a **subset** (by name, case-insensitive) of the 62 pre-training channels — you want to reuse the learned spatial embeddings |
-| [`braindecode/signal-jepa_without-chans`](https://huggingface.co/braindecode/signal-jepa_without-chans) | ✗ only the SSL backbone (feature encoder + transformer) | your channels are **not** a subset of the pre-training set, or you prefer to train channel embeddings from scratch |
-
-If you are unsure, start with `braindecode/signal-jepa_without-chans`: it
-always works, regardless of your electrode layout.
-
-## Quick start
-
-### Base model (pre-training architecture)
-
-The base model outputs contextual features, not class predictions. Use it
-for downstream feature extraction or further SSL.
+- [`braindecode/signal-jepa`](https://huggingface.co/braindecode/signal-jepa)
+  — full checkpoint including the 62-channel embedding table (for users whose
+  recording channels are a subset of the Lee2019 pre-training layout).
+- [`braindecode/signal-jepa_without-chans`](https://huggingface.co/braindecode/signal-jepa_without-chans)
+  — SSL backbone only (channel embeddings trained from scratch on your data).
 
 ```python
 from braindecode.models import SignalJEPA
 
-# With the pre-trained channel embeddings (recording channels ⊂ pre-train set):
-model = SignalJEPA.from_pretrained("braindecode/signal-jepa")
-
-# Or: with your own channels, kept aligned to the pre-training embedding table
-model = SignalJEPA.from_pretrained(
-    "braindecode/signal-jepa",
-    chs_info=raw.info["chs"],           # subset of the 62 pre-training channels
-    channel_embedding="pretrain_aligned",
-)
-
-# Or: without pre-trained channel embeddings (any electrode layout):
-model = SignalJEPA.from_pretrained(
-    "braindecode/signal-jepa_without-chans",
-    chs_info=raw.info["chs"],
-    strict=False,  # the channel-embedding weight is intentionally missing
-)
+model = SignalJEPA.from_pretrained("braindecode/signal-jepa_without-chans")
 ```
 
-### Downstream architectures
-
-Three classification architectures are introduced in the paper:
-
-- **a) Contextual** — uses the full transformer encoder
-- **b) Post-local** — discards the transformer; spatial convolution after local features
-- **c) Pre-local** — discards the transformer; spatial convolution before local features
-
-All three add a freshly-initialized classification head on top of the SSL
-backbone. The head is **not** part of the checkpoint and will be trained from
-scratch during fine-tuning; pass `strict=False` so `from_pretrained` does not
-complain about those missing keys.
-
-```python
-from braindecode.models import (
-    SignalJEPA_Contextual,
-    SignalJEPA_PreLocal,
-    SignalJEPA_PostLocal,
-)
-
-# a) Contextual — keeps the transformer
-model = SignalJEPA_Contextual.from_pretrained(
-    "braindecode/signal-jepa",          # or "signal-jepa_without-chans"
-    n_times=256,                         # e.g. 2 s at 128 Hz
-    n_outputs=4,
-    strict=False,                        # ignore un-trained classification head
-)
-
-# b) Post-local — transformer discarded
-model = SignalJEPA_PostLocal.from_pretrained(
-    "braindecode/signal-jepa_without-chans",
-    n_chans=19,
-    n_times=256,
-    n_outputs=4,
-    strict=False,
-)
-
-# c) Pre-local — transformer discarded
-model = SignalJEPA_PreLocal.from_pretrained(
-    "braindecode/signal-jepa_without-chans",
-    n_chans=19,
-    n_times=256,
-    n_outputs=4,
-    strict=False,
-)
-```
-
-See the braindecode tutorial
-[Fine-tuning a Foundation Model (Signal-JEPA)](https://braindecode.org/stable/auto_examples/advanced_training/plot_finetune_foundation_model.html)
-for a complete example including layer freezing and training with
-`skorch.EEGClassifier`.
-
-## Channel embedding modes
-
-`SignalJEPA` and `SignalJEPA_Contextual` accept a `channel_embedding` kwarg:
-
-- `"scratch"` (default): the `_ChannelEmbedding` table has one row per user
-  channel, initialized from `chs_info`. Compatible with the
-  `without-chans` checkpoint.
-- `"pretrain_aligned"`: the table has 62 rows in the pre-training order,
-  `forward` indexes into the subset matching your `chs_info` (matched by
-  channel name, case-insensitive). Compatible with the full checkpoint.
-
-`from_pretrained` picks the right mode automatically based on the checkpoint's
-`config.json`; override with the `channel_embedding=` kwarg if needed.
-
-## Citation
-
-```bibtex
-@article{guetschel2024sjepa,
-  title   = {S-JEPA: towards seamless cross-dataset transfer
-             through dynamic spatial attention},
-  author  = {Guetschel, Pierre and Moreau, Thomas and Tangermann, Michael},
-  journal = {arXiv preprint arXiv:2403.11772},
-  year    = {2024},
-}
-```
+See the docs for [`braindecode.models.SignalJEPA`](https://braindecode.org/stable/generated/braindecode.models.SignalJEPA.html)
+and the fine-tuning tutorial
+[Fine-tuning a Foundation Model (Signal-JEPA)](https://braindecode.org/stable/auto_examples/advanced_training/plot_finetune_foundation_model.html).