Text Classification
Scikit-learn
Joblib
Safetensors
promptguard
privacy
dlp
korean
roberta
logistic-regression
Instructions to use OASecure/promptguard-context-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use OASecure/promptguard-context-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("OASecure/promptguard-context-classifier", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
PromptGuard Context Runtime Artifacts
This directory is the PromptGuard context classifier runtime package.
It is not a single Hugging Face Transformer model directory. PromptGuard loads the source-of-truth manifest first, then resolves the LR and verifier paths from that manifest.
Required Files
| Purpose | File |
|---|---|
| Runtime manifest | context_lr_roberta_active_best_f1_manifest.json |
| Target labels | context_target_labels.json |
| Verifier label definitions | context_label_definitions_verifier_compact_v2.json |
| LR candidate generator | context_with_patch_v287_lr_c4_dev_classifier.joblib |
| Verifier model directory | context_verifier_klue_roberta_base_lrmined_v287_global002_compactv2_lpft_focal_1p2ep/ |
Model Roles
- The LR file is a scikit-learn One-vs-Rest Logistic Regression classifier. It consumes Qwen embedding vectors and proposes candidate context-risk labels.
- The verifier directory is a fine-tuned
klue/roberta-basesequence classifier. It accepts or rejects candidate labels using the label definitions and thresholds in the manifest.
Loading Rule
Do not load this directory with AutoModelForSequenceClassification.from_pretrained() at the repository root. PromptGuard should load context_lr_roberta_active_best_f1_manifest.json, then load the LR joblib and the verifier directory named in that manifest.