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")# 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
- Xet hash:
- e8d10e2d9272301faf153e881b0b3ccd7c3e4a5676348895286d23cd56c977e9
- Size of remote file:
- 261 MB
- SHA256:
- cc5b0665fec58476b83b262450de920d57c7e0aaf66126731b10834c52d5aa1c
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