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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54d1ae29ed3f8360308b11892fb783f14e86b0a0e49abff502a5ba86b3539a76
- Size of remote file:
- 1.42 GB
- SHA256:
- 08ad20131b72e8c4280f5c2e4c7b9952b935a742a556382366c9c5faba7a8b77
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.