Instructions to use alexanderfalk/danbert-small-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexanderfalk/danbert-small-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alexanderfalk/danbert-small-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("alexanderfalk/danbert-small-cased") model = AutoModelForMaskedLM.from_pretrained("alexanderfalk/danbert-small-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from alexanderfalk/danbert-small-cased: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/alexanderfalk/danbert-small-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://alexanderfalk/danbert-small-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/alexanderfalk/danbert-small-cased/resolve/main/flax_model.msgpack
334 MB
- Xet hash:
- 698681d92f41f7caa41fbb940400b62a967169b99f979beae860584aae6351cb
- Size of remote file:
- 334 MB
- SHA256:
- a6a71558a1ba862b185a3b30529536d342f8a0d2e75c292304ead842f0671410
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