synyyy
/

t5-spotify-features-v2

@@ -1,199 +1,126 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
+language: en
+license: apache-2.0
+tags:
+- t5
+- music
+- spotify
+- text2json
+- audio-features
+base_model: t5-base
+datasets:
+- custom
 library_name: transformers
+pipeline_tag: text2text-generation
 ---
+# T5-Base Fine-tuned for Spotify Features Prediction
+T5-Base fine-tuned to convert natural language prompts into Spotify audio feature JSON
 ## Model Details
+- **Base Model**: t5-base
+- **Model Type**: Text-to-JSON generation
+- **Language**: English
+- **Task**: Convert natural language music preferences into Spotify audio feature JSON objects
+- **Fine-tuning Dataset**: Custom dataset of prompts to Spotify audio features
+## Known Issues
+**IMPORTANT**: This model version may have JSON formatting issues where it doesn't generate proper curly braces. The output might need post-processing to create valid JSON.
+## Usage
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import json
+# Load model and tokenizer
+model = T5ForConditionalGeneration.from_pretrained("synyyy/t5-spotify-features-v2")
+tokenizer = T5Tokenizer.from_pretrained("synyyy/t5-spotify-features-v2")
+def generate_spotify_features(prompt):
+    # Format input
+    input_text = f"prompt: {prompt}"
+    # Tokenize and generate
+    input_ids = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True).input_ids
+    outputs = model.generate(
+        input_ids,
+        max_length=256,
+        num_beams=4,
+        early_stopping=True,
+        do_sample=False
+    )
+    # Decode result
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Post-process if needed (add curly braces if missing)
+    if not result.strip().startswith('{') and not result.strip().endswith('}'):
+        result = "{" + result + "}"
+    try:
+        return json.loads(result)
+    except json.JSONDecodeError as e:
+        print(f"JSON parsing failed: {e}")
+        print(f"Raw output: {result}")
+        return None
+# Example usage
+prompt = "I want energetic dance music for a party"
+features = generate_spotify_features(prompt)
+print(features)
+```
+## Expected Output Format
+```json
+{
+  "danceability": 0.85,
+  "energy": 0.90,
+  "valence": 0.75,
+  "acousticness": 0.15,
+  "instrumentalness": 0.05,
+  "speechiness": 0.08
+}
+```
+## Training Configuration
+- **Epochs**: 7
+- **Learning Rate**: 1e-4
+- **Batch Size**: 8 (per device)
+- **Gradient Accumulation Steps**: 2
+- **Scheduler**: Cosine with warmup
+- **Max Length**: 256 tokens
+## Limitations
+- May generate incomplete JSON that requires post-processing
+- Performance depends on similarity to training data
+- Trained on specific prompt format starting with "prompt: "
+## Model Files
+This repository contains:
+- `config.json`: Model configuration
+- `pytorch_model.bin`: Model weights
+- `tokenizer.json`: Tokenizer vocabulary
+- `tokenizer_config.json`: Tokenizer configuration
+- `special_tokens_map.json`: Special token mappings
+## Citation
+If you use this model, please cite:
+```
+@misc{t5-spotify-features-v1,
+  author = {Your Name},
+  title = {T5-Base Fine-tuned for Spotify Features Prediction},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/synyyy/t5-spotify-features-v2}
+}
+```