Instructions to use Rostlab/prot_bert_bfd_ss3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rostlab/prot_bert_bfd_ss3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Rostlab/prot_bert_bfd_ss3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Rostlab/prot_bert_bfd_ss3") model = AutoModelForTokenClassification.from_pretrained("Rostlab/prot_bert_bfd_ss3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Rostlab/prot_bert_bfd_ss3: direct link, hf CLI and curl.
- Browser
- Download file 1.68 GB
-
https://huggingface.co/Rostlab/prot_bert_bfd_ss3/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Rostlab/prot_bert_bfd_ss3/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Rostlab/prot_bert_bfd_ss3/resolve/main/flax_model.msgpack
1.68 GB
- Xet hash:
- d72c037caaf20fff6d2237c76079c3efc7fa2a590d07fcef37a09af7f9408b51
- Size of remote file:
- 1.68 GB
- SHA256:
- e3e64dcaa6f841a603d59691916d4dfa9670346baf49b208485d4bf82338956e
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