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 train_results.json from neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced/resolve/main/train_results.json
- Command line
-
hf download hf://neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced/train_results.json
-
curl -L -o train_results.json https://huggingface.co/neuralsentry/vulnfixClassification-StarEncoder-DCM-Balanced/resolve/main/train_results.json
195 Bytes
| { | |
| "epoch": 3.0, | |
| "train_loss": 0.19893105635567318, | |
| "train_runtime": 118.1813, | |
| "train_samples": 5342, | |
| "train_samples_per_second": 135.605, | |
| "train_steps_per_second": 1.066 | |
| } |