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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced: direct link, hf CLI and curl.
- Browser
- Download file 497 MB
-
https://huggingface.co/neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced/resolve/refs%2Fpr%2F1/pytorch_model.bin
497 MB
- Xet hash:
- df268a1e5433a2946b47f8edf0811474f3ea80e29cc8ffb3782a4fb396e3db93
- Size of remote file:
- 497 MB
- SHA256:
- b46b6d871fec37cdba4b0a97dccdde20004c96e7d34a6f8204ecc5a33961f171
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