Text Classification
Transformers
Safetensors
GGUF
English
modernbert
security
vulnerability-management
aspm
ettin
Eval Results (legacy)
text-embeddings-inference
Instructions to use NilHRH/vulnerability-severity-clf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NilHRH/vulnerability-severity-clf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NilHRH/vulnerability-severity-clf")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NilHRH/vulnerability-severity-clf") model = AutoModelForSequenceClassification.from_pretrained("NilHRH/vulnerability-severity-clf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_card_data.json from NilHRH/vulnerability-severity-clf: direct link, hf CLI and curl.
- Browser
- Download file 3.4 kB
-
https://huggingface.co/NilHRH/vulnerability-severity-clf/resolve/main/model_card_data.json
- Command line
-
hf download hf://NilHRH/vulnerability-severity-clf/model_card_data.json
-
curl -L -o model_card_data.json https://huggingface.co/NilHRH/vulnerability-severity-clf/resolve/main/model_card_data.json
3.4 kB
| { | |
| "run": "gguf1", | |
| "base_model": "jhu-clsp/ettin-encoder-150m", | |
| "classes": [ | |
| "Info", | |
| "Low", | |
| "Medium", | |
| "High", | |
| "Critical" | |
| ], | |
| "head": "CLS -> Linear(768,768) -> GELU -> LayerNorm -> Dropout -> Linear(768,5)", | |
| "max_len": 320, | |
| "train_config": { | |
| "epochs": 1.0, | |
| "batch": 96, | |
| "lr": 0.0003, | |
| "max_len": 320, | |
| "params_m": 149.6, | |
| "train": 63974, | |
| "val": 3535, | |
| "total_steps": 666, | |
| "head": "ModernBertForSequenceClassification" | |
| }, | |
| "test": { | |
| "run": "gguf1", | |
| "split": "test", | |
| "ckpt": "hf_pt", | |
| "n": 3598, | |
| "accuracy": 0.9953, | |
| "macro_f1": 0.9907, | |
| "per_class": { | |
| "Info": { | |
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| "P": 1.0, | |
| "R": 0.9755, | |
| "F1": 0.9876 | |
| }, | |
| "Low": { | |
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| "R": 0.9981, | |
| "F1": 0.9977 | |
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| "F1": 0.995 | |
| }, | |
| "Critical": { | |
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| "P": 1.0, | |
| "R": 0.9583, | |
| "F1": 0.9787 | |
| } | |
| }, | |
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| "legacy": { | |
| "run": "enc150", | |
| "test": { | |
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| "ckpt": "best.pt", | |
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| "accuracy": 0.9956, | |
| "macro_f1": 0.9836, | |
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| "P": 0.92, | |
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