Add pipeline tag and link to paper

README.md

@@ -1,21 +1,24 @@
 ---
+library_name: pytorch
 license: apache-2.0
+pipeline_tag: feature-extraction
 tags:
-  - physiowave
-  - physiowave-t
-  - foundation-model
-  - emg
-  - semg
-  - electromyography
-  - biosignal
-  - self-supervised
-  - masked-autoencoder
-  - pretrained
-library_name: pytorch
+- physiowave
+- physiowave-t
+- foundation-model
+- emg
+- semg
+- electromyography
+- biosignal
+- self-supervised
+- masked-autoencoder
+- pretrained
 ---
 
 # PhysioWave-T-sEMG: multi-route pretrained sEMG encoder
 
+Paper: [PhysioWave-T: Token-Efficient Wavelet Foundation Models for Biosignals](https://huggingface.co/papers/2611.08969)
+
 One of the three **PhysioWave-T** encoders ([EEG](https://huggingface.co/ForeverBlue/EEG), [ECG](https://huggingface.co/ForeverBlue/ECG), sEMG); the [RALF fusion model](https://huggingface.co/ForeverBlue/RALF-SeizeIT2) combines them. Code: <https://github.com/ForeverBlue816/PhysioWave>.
 
 Masked dual-reconstruction pretraining on surface EMG from wristbands, forearm arrays and high-density grids. One shared transformer serves three electrode layouts through route-specific wavelet frontends, and electrode identity enters as a learned embedding of the electrode NAME rather than of its index.
@@ -146,8 +149,8 @@ The three routes the shared transformer was pretrained across:
 | `G64_2000` | 64 | 2000 Hz |
 | `W16_2000` | 16 | 2000 Hz |
 
-To fine-tune ON one of them exactly, take that route's frontend and patcher out
-of the complete checkpoint:
+To fine-tune ON one of them exactly, take that route's frontend and patcher
+out of the complete checkpoint:
 
 ```bash
 python scripts/export_eeg_pretrained_encoder.py \
@@ -228,4 +231,4 @@ Code: <https://github.com/ForeverBlue816/PhysioWave>
 
 ---
 Prepared by `scripts/prepare_hf_release.py` from `pretrain_emg_c1_moe`
-on 2026-10-29.
+on 2026-10-29.