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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87bd5b988fdbfbd3bdf681da043e86994267189bcf797507f998e822f671b01d
- Size of remote file:
- 1.78 kB
- SHA256:
- e1147ba114819d716d2c7a0b6e07e6197c88d505d947f04f5f412af80c145bf2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.