Instructions to use virtual-human-chc/prot_bert_bfd_ss3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use virtual-human-chc/prot_bert_bfd_ss3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="virtual-human-chc/prot_bert_bfd_ss3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("virtual-human-chc/prot_bert_bfd_ss3") model = AutoModelForTokenClassification.from_pretrained("virtual-human-chc/prot_bert_bfd_ss3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from virtual-human-chc/prot_bert_bfd_ss3: direct link, hf CLI and curl.
- Browser
- Download file 1.68 GB
-
https://huggingface.co/virtual-human-chc/prot_bert_bfd_ss3/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://virtual-human-chc/prot_bert_bfd_ss3/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/virtual-human-chc/prot_bert_bfd_ss3/resolve/main/flax_model.msgpack
1.68 GB
- Xet hash:
- b7aecdbb7de7f6fb7f1cb15a9f801f7270c90a0c730e746f60d44b2b3796a866
- Size of remote file:
- 1.68 GB
- SHA256:
- e3e64dcaa6f841a603d59691916d4dfa9670346baf49b208485d4bf82338956e
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