Instructions to use jmilic/roberta-baseline-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmilic/roberta-baseline-3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jmilic/roberta-baseline-3") model = AutoModel.from_pretrained("jmilic/roberta-baseline-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jmilic/roberta-baseline-3: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/jmilic/roberta-baseline-3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jmilic/roberta-baseline-3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jmilic/roberta-baseline-3/resolve/main/pytorch_model.bin
501 MB
- Xet hash:
- 772da4681179529d5f2f17948f83249484a71dcbaf532207112a03ef3cb5ce5a
- Size of remote file:
- 501 MB
- SHA256:
- 8cd5c83ce2a49a225e635da9d697523fdcd164cb4ebc563a7280221401a7dfd0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.