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--- |
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license: apache-2.0 |
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tags: |
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- text-to-json |
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- music-preferences |
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- fine-tuned |
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- bart-large |
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--- |
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# Model Card for afsagag/bart-spotify-preferences |
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This is a fine-tuned BART-large model for converting music prompts to Spotify feature preferences (e.g., Energy, Valence, Release Year). Fine-tuned on a ~1k sample dataset in Kaggle. |
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## Training Details |
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- **Dataset**: PromptsToSpotifyFeatures-v2 (~1k samples) |
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- **Framework**: Hugging Face Transformers |
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- **Hyperparameters**: |
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- Learning rate: 5e-05 |
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- Epochs: 7 |
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- Batch size: 4 (with gradient accumulation steps=4) |
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- FP16: True |
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- **Metrics**: MAE, RMSE, per-feature correlation |
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## Usage |
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```python |
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from transformers import BartForConditionalGeneration, BartTokenizer |
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model = BartForConditionalGeneration.from_pretrained("afsagag/bart-spotify-preferences") |
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tokenizer = BartTokenizer.from_pretrained("afsagag/bart-spotify-preferences") |
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prompt = "music for a supervillain" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=256, num_beams=1, length_penalty=0.6, no_repeat_ngram_size=2, early_stopping=True) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Notes |
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- Fine-tuned without LoRA for full model weights. |
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- Outputs JSON-like dictionaries; may require post-processing for malformed JSON. |
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- Trained on Kaggle T4 GPU with ~16GB VRAM. |
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