Instructions to use binwang/roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="binwang/roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("binwang/roberta-base") model = AutoModelForMaskedLM.from_pretrained("binwang/roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from binwang/roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/binwang/roberta-base/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://binwang/roberta-base@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/binwang/roberta-base/resolve/refs%2Fpr%2F2/pytorch_model.bin
501 MB
- Xet hash:
- fc38e877aa1e2fce62c60d68c168872ce5cdf94302a2a148a2b7e34202c98bb3
- Size of remote file:
- 501 MB
- SHA256:
- 2cd836975ef20fc85f639583487429f226bc3091f09805261d8c66e69fde33a0
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