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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tharindu/roberta-50") model = AutoModelForMaskedLM.from_pretrained("tharindu/roberta-50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tharindu/roberta-50: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/tharindu/roberta-50/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tharindu/roberta-50/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tharindu/roberta-50/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 2c8cd71ffb1adb76c73ecd033b24935987d8e6a63f215353c967c1a41740b88b
- Size of remote file:
- 1.42 GB
- SHA256:
- f05081bfd6256210061d60e02a4c0a2ef598aa41be50eaf7ed335e06af26f6c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.