Feature Extraction
Transformers
Safetensors
mert2
audio
music
music-understanding
representation-learning
custom_code
Instructions to use m-a-p/MERT-v2-FullSong with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m-a-p/MERT-v2-FullSong with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="m-a-p/MERT-v2-FullSong", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("m-a-p/MERT-v2-FullSong", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/mert2-architecture.png from m-a-p/MERT-v2-FullSong: direct link, hf CLI and curl.
- Browser
- Download file 488 kB
-
https://huggingface.co/m-a-p/MERT-v2-FullSong/resolve/main/assets/mert2-architecture.png
- Command line
-
hf download hf://m-a-p/MERT-v2-FullSong/assets/mert2-architecture.png
-
curl -L -o mert2-architecture.png https://huggingface.co/m-a-p/MERT-v2-FullSong/resolve/main/assets/mert2-architecture.png
488 kB

- Xet hash:
- c203b4dc207d7950ffd5d002d997818c0e76191868b2ffa60e8e5471a6aca645
- Size of remote file:
- 488 kB
- SHA256:
- 98bfa98a643a9078f85bb4b4e14989ed0d2f0d3740ff94c5b64ee1d72e32579f
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