Text Classification
Transformers
PyTorch
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use CIRCL/cwe-parent-vulnerability-classification-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/cwe-parent-vulnerability-classification-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIRCL/cwe-parent-vulnerability-classification-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/cwe-parent-vulnerability-classification-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/cwe-parent-vulnerability-classification-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download metrics.json from CIRCL/cwe-parent-vulnerability-classification-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 243 Bytes
-
https://huggingface.co/CIRCL/cwe-parent-vulnerability-classification-roberta-base/resolve/main/metrics.json
- Command line
-
hf download hf://CIRCL/cwe-parent-vulnerability-classification-roberta-base/metrics.json
-
curl -L -o metrics.json https://huggingface.co/CIRCL/cwe-parent-vulnerability-classification-roberta-base/resolve/main/metrics.json
243 Bytes
| { | |
| "eval_loss": 4.251086711883545, | |
| "eval_accuracy": 0.5683355886332883, | |
| "eval_f1_macro": 0.21005870716463157, | |
| "eval_runtime": 1.706, | |
| "eval_samples_per_second": 433.183, | |
| "eval_steps_per_second": 7.034, | |
| "epoch": 40.0 | |
| } |