Instructions to use distilbert/distilroberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distilbert/distilroberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="distilbert/distilroberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("distilbert/distilroberta-base") model = AutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from distilbert/distilroberta-base: direct link, hf CLI and curl.
- Browser
- Download file 331 MB
-
https://huggingface.co/distilbert/distilroberta-base/resolve/fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b/pytorch_model.bin
- Command line
-
hf download hf://distilbert/distilroberta-base@fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/distilbert/distilroberta-base/resolve/fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b/pytorch_model.bin
331 MB
- Xet hash:
- bbb3bf704bda2b6c081234b58d9567ab8afd60a360138e26eb2550c625f5ebf4
- Size of remote file:
- 331 MB
- SHA256:
- ec1ed6ff4c9753d2fc41ddf4a63cc099697b6cfc308f3df668659e8d16f84c57
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