Instructions to use distilbert/distilbert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distilbert/distilbert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="distilbert/distilbert-base-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-cased") model = AutoModelForMaskedLM.from_pretrained("distilbert/distilbert-base-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.onnx from distilbert/distilbert-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 261 MB
-
https://huggingface.co/distilbert/distilbert-base-cased/resolve/refs%2Fpr%2F5/model.onnx
- Command line
-
hf download hf://distilbert/distilbert-base-cased@refs/pr/5/model.onnx
-
curl -L -o model.onnx https://huggingface.co/distilbert/distilbert-base-cased/resolve/refs%2Fpr%2F5/model.onnx
261 MB
- Xet hash:
- e8d10e2d9272301faf153e881b0b3ccd7c3e4a5676348895286d23cd56c977e9
- Size of remote file:
- 261 MB
- SHA256:
- cc5b0665fec58476b83b262450de920d57c7e0aaf66126731b10834c52d5aa1c
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