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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("csc-unipd/lilybert") model = AutoModelForMaskedLM.from_pretrained("csc-unipd/lilybert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from csc-unipd/lilybert: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/csc-unipd/lilybert/resolve/main/tokenizer.json
- Command line
-
hf download hf://csc-unipd/lilybert/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/csc-unipd/lilybert/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.