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
Update modeling_apex.py
Browse files- modeling_apex.py +5 -5
modeling_apex.py
CHANGED
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@@ -205,18 +205,18 @@ class APEXModel(PreTrainedModel):
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for task in preds
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}
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print(f"\n{'
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print(f" APEX Predictions")
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print(f"{'
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print(f"\n Popularity:")
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print(f" {'
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print(f" {'Streams Score':<20} {results['score_streams']:>8.2f} / 100")
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print(f" {'Likes Score':<20} {results['score_likes']:>8.2f} / 100")
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print(f"\n Aesthetic Quality:")
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-
print(f" {'
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for dim in ["coherence", "musicality", "memorability", "clarity", "naturalness"]:
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print(f" {dim.capitalize():<20} {results[dim]:>8.2f} / 5.00")
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-
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if save_json:
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with open(save_json, "w") as f:
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for task in preds
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}
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+
print(f"\n{'─'*50}")
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print(f" APEX Predictions")
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print(f"{'─'*50}")
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print(f"\n Popularity:")
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+
print(f" {'-'*40}")
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print(f" {'Streams Score':<20} {results['score_streams']:>8.2f} / 100")
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print(f" {'Likes Score':<20} {results['score_likes']:>8.2f} / 100")
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print(f"\n Aesthetic Quality:")
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+
print(f" {'-'*40}")
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for dim in ["coherence", "musicality", "memorability", "clarity", "naturalness"]:
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print(f" {dim.capitalize():<20} {results[dim]:>8.2f} / 5.00")
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+
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if save_json:
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with open(save_json, "w") as f:
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