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 pytorch_model.bin from alexanderfalk/danbert-small-cased: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/alexanderfalk/danbert-small-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://alexanderfalk/danbert-small-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/alexanderfalk/danbert-small-cased/resolve/main/pytorch_model.bin
336 MB
- Xet hash:
- 02b83b4b9c291fe569ab6630c8bc8dada7d35f9d5d10e65ec3a51aa3cbbec933
- Size of remote file:
- 336 MB
- SHA256:
- 50f13e6f1a7d9cee4002ed8c89cd77b6f7c71afdde16924d7e822dce7d8c29c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.