Instructions to use tharindu/roberta-25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tharindu/roberta-25 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tharindu/roberta-25")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tharindu/roberta-25") model = AutoModelForMaskedLM.from_pretrained("tharindu/roberta-25", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eff2b960ad98633340924870bb7f9b369649989c2630fdeb439d3e5a3ba95f2a
- Size of remote file:
- 2.84 GB
- SHA256:
- 51b4956da3d9648cd44b7ed558909ca6c42370b7b63df2efb7731501e01a33b9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.