Instructions to use neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced") model = AutoModelForSequenceClassification.from_pretrained("neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 083274e75d9bb0bede0b171837d88d7341e6d9a59f0956870bff0b9fc9f00169
- Size of remote file:
- 497 MB
- SHA256:
- 7e05fa1d9315a57fdb4893ccd371b38ebbd2b84ffc15f02cf3075e1802672bce
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