Instructions to use ramonzaca/roberto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ramonzaca/roberto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ramonzaca/roberto")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ramonzaca/roberto") model = AutoModelForMaskedLM.from_pretrained("ramonzaca/roberto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc1e283acd87f37177dc8b154177710aaa2ae31ff42cf72883b5cb3c2a530809
- Size of remote file:
- 1.33 GB
- SHA256:
- e35819d9a7530159214dc58d6a2cce042e1308bad4254c115ff23ea8aa4cee00
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.