Fill-Mask
Transformers
ONNX
Safetensors
English
roberta
music
lilypond
mlm
music-information-retrieval
Eval Results (legacy)
Instructions to use csc-unipd/lilybert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csc-unipd/lilybert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="csc-unipd/lilybert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("csc-unipd/lilybert") model = AutoModelForMaskedLM.from_pretrained("csc-unipd/lilybert") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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language:
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license: apache-2.0
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library_name: transformers
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pipeline_tag: fill-mask
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tags:
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base_model: microsoft/codebert-base
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datasets:
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model-index:
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# lilyBERT
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## License
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Apache-2.0
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: fill-mask
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tags:
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- music
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- lilypond
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- mlm
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- music-information-retrieval
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base_model: microsoft/codebert-base
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datasets:
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- custom
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model-index:
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- name: lilyBERT
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results:
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- task:
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type: text-classification
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name: Composer Classification (Linear Probe)
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dataset:
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type: custom
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name: Mutopia (out-of-domain)
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metrics:
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- type: accuracy
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value: 84.3
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name: Composer Accuracy
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- type: accuracy
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value: 82.9
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name: Style Accuracy
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metrics:
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- accuracy
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---
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# lilyBERT
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## License
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Apache-2.0
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