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:
- df268a1e5433a2946b47f8edf0811474f3ea80e29cc8ffb3782a4fb396e3db93
- Size of remote file:
- 497 MB
- SHA256:
- b46b6d871fec37cdba4b0a97dccdde20004c96e7d34a6f8204ecc5a33961f171
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.