Instructions to use BrianS15/prot_bert-finetuned-tchard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BrianS15/prot_bert-finetuned-tchard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BrianS15/prot_bert-finetuned-tchard")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BrianS15/prot_bert-finetuned-tchard") model = AutoModelForMaskedLM.from_pretrained("BrianS15/prot_bert-finetuned-tchard") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44cc6cc7bbbd9fd97a8859bb215dab25ec5ace4b2eca4eecbdab5cee7d92a288
- Size of remote file:
- 1.68 GB
- SHA256:
- b2631930fb2b3edabc81dd53f1e1005c46210462de8591dea1123ab2fe8baca0
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