---
license: other
pipeline_tag: text-classification
tags:
- promptguard
- text-classification
- privacy
- dlp
- korean
- roberta
- sklearn
- logistic-regression
- joblib
base_model:
- klue/roberta-base
---

# PromptGuard Context Classifier v287

This repository hosts the complete runtime artifact package for PromptGuard's context-risk classifier.

It contains both required runtime stages:

- `LR candidate generator`: `models/context_with_patch_v287_lr_c4_dev_classifier.joblib`
- `RoBERTa verifier`: `models/context_verifier_klue_roberta_base_lrmined_v287_global002_compactv2_lpft_focal_1p2ep/model.safetensors`

The Hugging Face `base_model` metadata points only to the fine-tuned verifier base, `klue/roberta-base`. The LR stage is a scikit-learn linear classifier over Qwen embedding vectors, so it has no Transformer base model of its own.

## Runtime Architecture

PromptGuard uses a two-stage context classifier. This repository is a runtime artifact package, not a single Transformer checkpoint loaded from the repository root.

1. Frozen Qwen embedding dependency + One-vs-Rest Logistic Regression candidate generator
2. Fine-tuned KLUE RoBERTa label-aware verifier

The Logistic Regression stage is a high-recall linear candidate generator stored as a `joblib` artifact. It is not a fine-tuned Qwen model and does not have its own Hugging Face base model. It expects normalized embedding vectors produced by `Qwen/Qwen3-Embedding-0.6B`.

The RoBERTa verifier is the fine-tuned Transformer stage. Its base model is `klue/roberta-base`, and the fine-tuned verifier checkpoint is stored under `models/context_verifier_klue_roberta_base_lrmined_v287_global002_compactv2_lpft_focal_1p2ep/`.

## Artifact Inventory

The runtime is complete only when all of these files are present:

| Runtime part | File |
| --- | --- |
| Source-of-truth manifest | `models/context_lr_roberta_active_best_f1_manifest.json` |
| Target label list | `models/context_target_labels.json` |
| Verifier label definitions | `models/context_label_definitions_verifier_compact_v2.json` |
| LR candidate generator | `models/context_with_patch_v287_lr_c4_dev_classifier.joblib` |
| RoBERTa verifier checkpoint | `models/context_verifier_klue_roberta_base_lrmined_v287_global002_compactv2_lpft_focal_1p2ep/model.safetensors` |
| RoBERTa tokenizer/config | `models/context_verifier_klue_roberta_base_lrmined_v287_global002_compactv2_lpft_focal_1p2ep/*` |

If only the LR `joblib` file is downloaded, the PromptGuard context pipeline is incomplete. Download with `--include "models/*"` so both stages are available.

## Files

Download the `models/` directory into the PromptGuard project root. The application reads:

- `models/context_lr_roberta_active_best_f1_manifest.json`
- `models/context_target_labels.json`
- `models/context_label_definitions_verifier_compact_v2.json`
- `models/context_with_patch_v287_lr_c4_dev_classifier.joblib`
- `models/context_verifier_klue_roberta_base_lrmined_v287_global002_compactv2_lpft_focal_1p2ep/`

`models/SHA256SUMS` contains checksums for the uploaded runtime files.

## Target Labels

- `SECRET_CREDENTIAL_CONTEXT`
- `PERSONAL_DATA_CONTEXT`
- `FINANCIAL_IDENTIFIER_CONTEXT`
- `CONFIDENTIAL_BUSINESS_CONTEXT`
- `PROPRIETARY_TECHNICAL_CONTEXT`
- `SECURITY_CONTROL_CONTEXT`
- `INTERNAL_OPERATION_CONTEXT`
- `BULK_SENSITIVE_RECORD_CONTEXT`

## Threshold Policy

The source-of-truth policy is stored in `models/context_lr_roberta_active_best_f1_manifest.json`.

- LR candidate threshold: `0.02`
- Verifier threshold mode: label-wise
- Verifier chunk policy: `chunk_chars=900`, `chunk_overlap=120`, `max_chunks=8`
- Verifier pooling: max verifier score per label across chunks

## Usage

From the PromptGuard repository root:

```bash
pip install "huggingface_hub[hf_xet]"
hf download OASecure/promptguard-context-classifier \
  --revision v287-20260623 \
  --include "models/*" \
  --local-dir .
```

Then enable the runtime:

```env
PROMPTGUARD_CLASSIFIER_RUNTIME_ENABLED=true
PROMPTGUARD_CLASSIFIER_MANIFEST_PATH=/opt/promptguard/models/context_lr_roberta_active_best_f1_manifest.json
PROMPTGUARD_VERIFIER_RUNTIME_ENABLED=true
PROMPTGUARD_VERIFIER_MANIFEST_PATH=/opt/promptguard/models/context_lr_roberta_active_best_f1_manifest.json
```

Do not load this repository root directly with `AutoModelForSequenceClassification.from_pretrained("OASecure/promptguard-context-classifier")`. PromptGuard reads `models/context_lr_roberta_active_best_f1_manifest.json`, loads the LR `joblib` file for candidate generation, and loads the verifier directory named in that manifest for RoBERTa verification.

## Data and Privacy

This repository contains runtime artifacts only. It does not include raw prompts, source documents, extracted text, filenames, training/evaluation detail rows, embedding vectors, logits, exact classifier scores, or full masked prompts.

## License Notes

The PromptGuard application code is distributed separately on GitHub. These model artifacts are published for PromptGuard runtime use. `Qwen/Qwen3-Embedding-0.6B` is used as a frozen embedding dependency for the LR stage, not as the fine-tuned base model of this repository. `klue/roberta-base` is the verifier base model. Downstream users should review base-model and training-data obligations before redistribution or commercial use.

## Limitations

This model is a context-risk classifier. It is not a complete DLP system by itself and does not replace PromptGuard's separate regex/span masking policy layer.