Update README.md

README.md

@@ -1,20 +1,34 @@
 ---
 license: mit
 tags:
-  - midi
-  - music-generation
-  - tokenization
+- music-generation
+- midi
+- transformer
+- symbolic-music
+datasets:
+- maestro
+- asap
 ---
-# MIDI tokenization models (Lakh+HSMT pretrain → ASAP finetune)
-Six composer/genre-conditional MIDI Transformers, one per tokenization mode.
-| subfolder | tokenization_mode |
-|---|---|
-| `note` | note |
-| `note_pedal` | note_pedal |
-| `note_velocity` | note_velocity |
-| `note_velocity_beat` | note_velocity_beat |
-| `note_velocity_pedal` | note_velocity_pedal |
-| `full` | full |
-Each folder: `config.json` + `pytorch_model.bin`.
-- Pretrain: Lakh MIDI + HSMT (`note_velocity_pedal`)
-- Finetune: ASAP v2, mode as above
+
+# Evaluating Tokenization Strategies for Expressive Classical Piano Performance Generation
+
+This repository contains official artifacts for the paper **"Evaluating Tokenization Strategies for Expressive Classical Piano Performance Generation"**, submitted to the *29th International Conference on Digital Audio Effects (DAFx26)*.
+
+We provide six composer/genre-conditional MIDI Transformers, each trained on a distinct tokenization scheme to systematically evaluate expressive piano performance generation.
+
+## Model Variants & Tokenization Modes
+
+| Subfolder | Tokenization Mode | Target Features |
+| :--- | :--- | :--- |
+| `note` | `note` | Onset, Pitch & duration |
+| `note_pedal` | `note_pedal` | Onset, Pitch, duration, sustain pedal |
+| `note_velocity` | `note_velocity` | Onset, Pitch, duration, velocity |
+| `note_velocity_beat` | `note_velocity_beat` | Onset, Pitch, duration, velocity, beat markers |
+| `note_velocity_pedal` | `note_velocity_pedal` | Onset, Pitch, duration, velocity, sustain pedal |
+| `full` | `full` | Complete expressive performance attributes |
+
+## Training Strategy
+
+* **Pre-training:** **Lakh MIDI** + **MAESTRO** dataset  
+  *(Note: Lakh MIDI was incorporated during pre-training to improve generation diversity, expanding upon the baseline setup described in the paper).*
+* **Fine-tuning:** **ASAP** (Aligned Scores and Performances) dataset.