Instructions to use osbm/protbert-finetuned-CAFA5-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osbm/protbert-finetuned-CAFA5-1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="osbm/protbert-finetuned-CAFA5-1000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("osbm/protbert-finetuned-CAFA5-1000") model = AutoModelForSequenceClassification.from_pretrained("osbm/protbert-finetuned-CAFA5-1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2eea09aea4b1e46fd87d7788ffc7670c3a427f3ef1f71a465cfdcc51fa775e98
- Size of remote file:
- 1.68 GB
- SHA256:
- 1f2803b5ca605ca63e45abafb9d1024dfbdb54efcb32a9d50c36f2b5634f97ad
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