Feature Extraction
Transformers
PyTorch
English
apex
music
audio
popularity-prediction
aesthetic-quality
multi-task-learning
mert
ai-generated-music
suno
udio
custom_code
Instructions to use amaai-lab/apex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amaai-lab/apex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="amaai-lab/apex", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("amaai-lab/apex", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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@@ -84,4 +84,19 @@ print(f"Musicality : {results['musicality']:.2f}")
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print(f"Memorability : {results['memorability']:.2f}")
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print(f"Clarity : {results['clarity']:.2f}")
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print(f"Naturalness : {results['naturalness']:.2f}")
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```
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print(f"Memorability : {results['memorability']:.2f}")
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print(f"Clarity : {results['clarity']:.2f}")
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print(f"Naturalness : {results['naturalness']:.2f}")
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```
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---
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## Citation
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```bash
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@misc{husain2026apexlargescalemultitaskaestheticinformed,
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title={APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music},
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author={Jaavid Aktar Husain and Dorien Herremans},
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year={2026},
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eprint={2605.03395},
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archivePrefix={arXiv},
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primaryClass={cs.SD},
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url={https://arxiv.org/abs/2605.03395},
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}
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```
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