Instructions to use rampasek/prot_bert_bfd_rosetta204060aa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rampasek/prot_bert_bfd_rosetta204060aa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rampasek/prot_bert_bfd_rosetta204060aa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rampasek/prot_bert_bfd_rosetta204060aa") model = AutoModelForSequenceClassification.from_pretrained("rampasek/prot_bert_bfd_rosetta204060aa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rampasek/prot_bert_bfd_rosetta204060aa: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/rampasek/prot_bert_bfd_rosetta204060aa/resolve/main/training_args.bin
- Command line
-
hf download hf://rampasek/prot_bert_bfd_rosetta204060aa/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rampasek/prot_bert_bfd_rosetta204060aa/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 1806151fde3a450ae64d9270607ddf6ccc720c4eeab9dcbb3ca1b2d3d6a484e1
- Size of remote file:
- 2.99 kB
- SHA256:
- 2e28fb0e1bc54eb95ba166aaa5181e97f33801131164088bd55fea45251f95eb
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