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_3.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_3.pth
- Command line
-
hf download hf://CALDISS-AAU/DA-BERT_Old_News_V3/rng_state_3.pth
-
curl -L -o rng_state_3.pth https://huggingface.co/CALDISS-AAU/DA-BERT_Old_News_V3/resolve/main/rng_state_3.pth
15.4 kB
- Xet hash:
- e55fc064e4dc41da6e7f8b4df06f9a3fae6915816eff67d362214e62b1c2a33b
- Size of remote file:
- 15.4 kB
- SHA256:
- 943faeaf0c720a56a3afd5cd359d9c2cb8fcbce849e0c3bb50be65d18bd49c27
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