Instructions to use hz53/roberta-base-cb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hz53/roberta-base-cb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hz53/roberta-base-cb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hz53/roberta-base-cb") model = AutoModelForSequenceClassification.from_pretrained("hz53/roberta-base-cb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hz53/roberta-base-cb: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/hz53/roberta-base-cb/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hz53/roberta-base-cb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hz53/roberta-base-cb/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 696d02cf028ae0bff64ed12a21c82285c77a65c0dc50ea63ee82e6cfca29ba59
- Size of remote file:
- 499 MB
- SHA256:
- af0f8e2b7be13e87089bf92342997892776196860a8e86e5096241aa4b756ae0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.