Instructions to use distilbert/distilbert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distilbert/distilbert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="distilbert/distilbert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("distilbert/distilbert-base-uncased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from distilbert/distilbert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/distilbert/distilbert-base-uncased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://distilbert/distilbert-base-uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/distilbert/distilbert-base-uncased/resolve/main/flax_model.msgpack
268 MB
- Xet hash:
- 246ee9245eecfaa1f1f0a3161fb22e3f1e7794413009eb579a40520cdebb38ce
- Size of remote file:
- 268 MB
- SHA256:
- 101fa75462994e55b265fd62b0f44b9bb9f4d3fcb0c9d8986d2542850d0f0688
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