Instructions to use CALDISS-AAU/DA-BERT_Old_News_V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CALDISS-AAU/DA-BERT_Old_News_V3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CALDISS-AAU/DA-BERT_Old_News_V3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CALDISS-AAU/DA-BERT_Old_News_V3") model = AutoModelForMaskedLM.from_pretrained("CALDISS-AAU/DA-BERT_Old_News_V3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download rng_state_1.pth from CALDISS-AAU/DA-BERT_Old_News_V3: direct link, hf CLI and curl.
- Browser
- Download file 15.4 kB
-
https://huggingface.co/CALDISS-AAU/DA-BERT_Old_News_V3/resolve/main/rng_state_1.pth
- Command line
-
hf download hf://CALDISS-AAU/DA-BERT_Old_News_V3/rng_state_1.pth
-
curl -L -o rng_state_1.pth https://huggingface.co/CALDISS-AAU/DA-BERT_Old_News_V3/resolve/main/rng_state_1.pth
15.4 kB
- Xet hash:
- 0d5553ac6bb493960a97b49b301ce1d147bfba24febab423786400964f06afac
- Size of remote file:
- 15.4 kB
- SHA256:
- efd64f71d26d4ca1e89c7c9ce75dedbc1f38766c11917afb2d4e32db963034f2
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