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 flax_model.msgpack from distilbert/distilroberta-base: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/distilbert/distilroberta-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://distilbert/distilroberta-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/distilbert/distilroberta-base/resolve/main/flax_model.msgpack
329 MB
- Xet hash:
- 7eadfe55c416a077c05b25e3e23804984590fe4f4924793d126b2f0da5623f4b
- Size of remote file:
- 329 MB
- SHA256:
- 4da82a2cd90eea052a0f4fe91e4edd3cebd337ab2439d147e6e68f712a2517d0
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