Instructions to use Rostlab/prot_bert_bfd_membrane with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rostlab/prot_bert_bfd_membrane with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rostlab/prot_bert_bfd_membrane")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rostlab/prot_bert_bfd_membrane") model = AutoModelForSequenceClassification.from_pretrained("Rostlab/prot_bert_bfd_membrane", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Rostlab/prot_bert_bfd_membrane: direct link, hf CLI and curl.
- Browser
- Download file 1.68 GB
-
https://huggingface.co/Rostlab/prot_bert_bfd_membrane/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Rostlab/prot_bert_bfd_membrane/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Rostlab/prot_bert_bfd_membrane/resolve/main/pytorch_model.bin
1.68 GB
- Xet hash:
- bcc6dde1232f2cfa19c4d7e54e3c7afb9f38e2bba58a9ef2c456e1405382f657
- Size of remote file:
- 1.68 GB
- SHA256:
- a0cfa940fcceea5abbbf0116d9c433cf57c95fa391ef72b3057a9709813c00d3
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