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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- t5 |
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- music |
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- spotify |
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- text2json |
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- audio-features |
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- fine-tuned |
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base_model: t5-base |
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datasets: |
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- custom |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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--- |
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# T5-Base Fine-tuned for Spotify Features Prediction |
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T5-Base fine-tuned to convert natural language prompts into Spotify audio feature JSON |
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## Model Details |
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- **Base Model**: t5-base |
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- **Model Type**: Text-to-JSON generation |
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- **Language**: English |
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- **Task**: Convert natural language music preferences into Spotify audio feature JSON objects |
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- **Fine-tuning Dataset**: Custom dataset of prompts to Spotify audio features |
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## Training Configuration |
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- **Epochs**: 7 |
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- **Learning Rate**: 3e-4 |
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- **Batch Size**: 8 (per device) |
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- **Gradient Accumulation Steps**: 4 |
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- **Scheduler**: Cosine with warmup |
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- **Optimizer**: AdamW |
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- **Max Length**: 256 tokens |
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- **Precision**: bfloat16 |
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## Usage |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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import json |
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# Load model and tokenizer |
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model = T5ForConditionalGeneration.from_pretrained("afsagag/t5-spotify-features") |
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tokenizer = T5Tokenizer.from_pretrained("afsagag/t5-spotify-features") |
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# Example usage |
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prompt = "I want energetic dance music with high energy and danceability" |
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input_text = f"prompt: {prompt}" |
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# Tokenize and generate |
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input_ids = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True).input_ids |
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outputs = model.generate( |
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input_ids, |
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max_length=256, |
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num_beams=4, |
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early_stopping=True, |
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do_sample=False |
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) |
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# Decode result |
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result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(result) |
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# Parse JSON output |
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try: |
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spotify_features = json.loads(result) |
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print("Generated Spotify Features:", spotify_features) |
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except json.JSONDecodeError: |
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print("Generated text is not valid JSON") |
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``` |
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## Expected Output Format |
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The model generates JSON objects with Spotify audio features: |
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```json |
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{ |
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"danceability": 0.85, |
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"energy": 0.90, |
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"valence": 0.75, |
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"acousticness": 0.15, |
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"instrumentalness": 0.05, |
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"speechiness": 0.08, |
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} |
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``` |
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## Metrics |
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- **Per-set Mean Absolute Error**: Measures average prediction accuracy across feature sets |
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- **Per-set Root Mean Squared Error**: Measures prediction variance |
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- **Per-feature Correlation**: Pearson correlation for individual audio features |
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## Model Files |
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- `config.json`: Model configuration |
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- `pytorch_model.bin`: Model weights |
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- `tokenizer.json`: Tokenizer vocabulary |
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- `tokenizer_config.json`: Tokenizer configuration |
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- `special_tokens_map.json`: Special token mappings |
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## Limitations |
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- Model may occasionally generate invalid JSON that requires post-processing |
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- Trained on specific prompt formats starting with "prompt: " |
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- Performance depends on similarity to training data distribution |
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- May not generalize well to very abstract or unusual music descriptions |
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## Training Data |
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The model was trained on a custom dataset pairing natural language music descriptions with corresponding Spotify audio feature values. |
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## Ethical Considerations |
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This model generates music preference predictions and should not be used as the sole basis for music recommendation systems without human oversight. |
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