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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73ca65384093a730930e8158cc24a8ff0774df850cdfb8e4a8a3085b2e28e6bd
- Size of remote file:
- 4.09 kB
- SHA256:
- 2abf13de8c30857952e6c89af733241bad58ad8158e67cddbe483710ddea4dea
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