Instructions to use sagteam/xlm-roberta-large-sag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagteam/xlm-roberta-large-sag with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sagteam/xlm-roberta-large-sag")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sagteam/xlm-roberta-large-sag") model = AutoModelForMaskedLM.from_pretrained("sagteam/xlm-roberta-large-sag", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sagteam/xlm-roberta-large-sag: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/sagteam/xlm-roberta-large-sag/resolve/main/training_args.bin
- Command line
-
hf download hf://sagteam/xlm-roberta-large-sag/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sagteam/xlm-roberta-large-sag/resolve/main/training_args.bin
1.78 kB
- Xet hash:
- 40c03fca6c5d9612deb9face244cea23ba8e65ebdd58ce8059234b215d36a621
- Size of remote file:
- 1.78 kB
- SHA256:
- 46d0fe7b6029a23d11a05f09b9ee5c9935b722ab48af3f80d75e19b66d71a45f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.