Instructions to use XinXuNLPer/MuseCoco_text2attribute with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XinXuNLPer/MuseCoco_text2attribute with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("XinXuNLPer/MuseCoco_text2attribute") model = AutoModel.from_pretrained("XinXuNLPer/MuseCoco_text2attribute") - Notebooks
- Google Colab
- Kaggle
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# Text-to-Attribute Understanding
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This is the text-to-attribute model to extract musical attributes from text, introduced in the paper [*MuseCoco: Generating Symbolic Music from Text*](https://arxiv.org/abs/2306.00110) and [first released in this repository](https://github.com/microsoft/muzic/tree/main/musecoco).
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It is based on BERT-large and has multiple classification heads for diverse musical attributes:
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```json
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# Text-to-Attribute Understanding
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This is the text-to-attribute model to extract musical attributes from text, introduced in the paper [*MuseCoco: Generating Symbolic Music from Text*](https://arxiv.org/abs/2306.00110) and [first released in this repository](https://github.com/microsoft/muzic/tree/main/musecoco).
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It is based on BERT-large and has multiple classification heads for diverse musical attributes:
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```json
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