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README.md

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+---
+pipeline_tag: symbolic-music-retrieval
+language: en
+library_name: pytorch
+license: apache-2.0
+tags:
+  - music
+  - midi
+  - mir
+  - deduplication
+  - caugbert
+model-index:
+  - name: LMD Deduplication - CAugBERT
+    results: 
+    - task:
+        type: representation-learning
+        name: symbolic music representation learning
+      dataset:
+        type: midi
+        name: Lakh MIDI Dataset 
+      metrics:
+        - type: F1
+          value: 0.493
+---
+
+# LMD Deduplication Supplements
+This repository provides the pre-trained CAugBERT model checkpoint used in: 
+**"On the De-duplication of the Lakh MIDI Dataset" (ISMIR 2025)**  
+[[Paper]](https://ismir2025program.ismir.net/poster_188.html) | [[GitHub Code]](https://github.com/jech2/LMD_Deduplication)
+
+---
+
+# Usage
+You can either integrate this checkpoint into the main repository for inference, or load it directly:
+```bash
+# Option 1: Run inference in the main repo
+poetry run python inference.py  # make sure yamls/inference.yaml paths are correct
+```
+```python
+# Option 2: Load checkpoint manually
+import torch
+from contrastive_musicbert.model.BERT import BERT_Lightning
+
+model = BERT_Lightning(...).to(device)  # see .hydra/config.yaml for arguments
+checkpoint = torch.load(checkpoint_path, map_location="cpu")
+model.load_state_dict(checkpoint['state_dict'])
+```
+
+# Note
+If you have any questions regarding the checkpoint, please contact:
+Eunjin Choi (jech@kaist.ac.kr)