Instructions to use jfernandez/cebfil-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfernandez/cebfil-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jfernandez/cebfil-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jfernandez/cebfil-roberta") model = AutoModelForMaskedLM.from_pretrained("jfernandez/cebfil-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jfernandez/cebfil-roberta: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/jfernandez/cebfil-roberta/resolve/main/training_args.bin
- Command line
-
hf download hf://jfernandez/cebfil-roberta/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jfernandez/cebfil-roberta/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- ed588a94f7c045f2bae8c3409fcc2a0a622fab460819470dcb087b7a20fe2f19
- Size of remote file:
- 3.39 kB
- SHA256:
- 7f844e21af175bed928e71c83ee891be16e0b6e41b78430fc855c12a47223b03
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