Instructions to use neuralsentry/vulnfixClassification-StarEncoder-DCMB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralsentry/vulnfixClassification-StarEncoder-DCMB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralsentry/vulnfixClassification-StarEncoder-DCMB")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neuralsentry/vulnfixClassification-StarEncoder-DCMB") model = AutoModelForSequenceClassification.from_pretrained("neuralsentry/vulnfixClassification-StarEncoder-DCMB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from neuralsentry/vulnfixClassification-StarEncoder-DCMB: direct link, hf CLI and curl.
- Browser
- Download file 199 Bytes
-
https://huggingface.co/neuralsentry/vulnfixClassification-StarEncoder-DCMB/resolve/main/train_results.json
- Command line
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hf download hf://neuralsentry/vulnfixClassification-StarEncoder-DCMB/train_results.json
-
curl -L -o train_results.json https://huggingface.co/neuralsentry/vulnfixClassification-StarEncoder-DCMB/resolve/main/train_results.json
199 Bytes
| { | |
| "epoch": 10.0, | |
| "train_loss": 0.040063528825431106, | |
| "train_runtime": 1111.3299, | |
| "train_samples": 27992, | |
| "train_samples_per_second": 251.878, | |
| "train_steps_per_second": 1.971 | |
| } |