Instructions to use tharindu/roberta-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tharindu/roberta-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tharindu/roberta-50")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tharindu/roberta-50") model = AutoModelForMaskedLM.from_pretrained("tharindu/roberta-50") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a40da0a6aed4c26522ee4254704f60038f169044b50187cca6ae97d193159cbd
- Size of remote file:
- 3.44 kB
- SHA256:
- 73d660b98d53c66088f03e99170cb447498e593b6a8729f70ab949c9fad32c6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.