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 pytorch_model.bin from rampasek/prot_bert_bfd_rosetta204060aa: direct link, hf CLI and curl.
- Browser
- Download file 1.68 GB
-
https://huggingface.co/rampasek/prot_bert_bfd_rosetta204060aa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rampasek/prot_bert_bfd_rosetta204060aa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rampasek/prot_bert_bfd_rosetta204060aa/resolve/main/pytorch_model.bin
1.68 GB
- Xet hash:
- d66bcfbf9b893c3268df210362858868e150fb96dc7ba8e8d71faf99355d7c3b
- Size of remote file:
- 1.68 GB
- SHA256:
- 12d7ef6b70b6d2aec36a03d432f4f29a324823538b2d3761e0893273b87ae96d
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