Instructions to use distilbert/distilbert-base-german-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use distilbert/distilbert-base-german-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="distilbert/distilbert-base-german-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-german-cased") model = AutoModelForMaskedLM.from_pretrained("distilbert/distilbert-base-german-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from distilbert/distilbert-base-german-cased: direct link, hf CLI and curl.
- Browser
- Download file 270 MB
-
https://huggingface.co/distilbert/distilbert-base-german-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://distilbert/distilbert-base-german-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/distilbert/distilbert-base-german-cased/resolve/main/pytorch_model.bin
270 MB
- Xet hash:
- bb97a16826f3e4d1900e71e5927b0af08b7bec9bca19a6bcb0b67268c8b1c835
- Size of remote file:
- 270 MB
- SHA256:
- 17f4f5b2f7e83b0a6f6031af68d16164716743e8edb8c555ab3ba540ac6e818b
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