Text Classification
Transformers
Safetensors
English
deberta-v2
secret-detection
secrets
security
cybersecurity
devsecops
sast
code-security
code-analysis
supply-chain-security
pre-commit
ci-cd
github-actions
password-detection
api-key-detection
token-detection
credential-detection
secure-coding
deberta
deberta-v3
binary-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use hypn05/secrets-sentinel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hypn05/secrets-sentinel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hypn05/secrets-sentinel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hypn05/secrets-sentinel") model = AutoModelForSequenceClassification.from_pretrained("hypn05/secrets-sentinel") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
v5.0.0: Full fine-tune on data_v10 (1.14M lines, 195 negative patterns). Fixes CloudFormation Default: FNs (F1 0.0→1.0), Redis --requirepass FNs (F1 0.67→1.0), and all 14 confirmed FP categories from real-world scanner runs. F1=0.9999, Precision=1.0000, Recall=0.9998 on held-out eval.
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