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xlmr-shield
jainsatyam26/xlmr-shield
xlmr-shield is a text classification model from jainsatyam26. Use it when you need a label for a piece of text. The card lists the license as apache-2.0.
XLM-RoBERTa-base dual-head classifier for content moderation and prompt injection detection. Fine-tuned with 40:60 resampling + per-class cap (30K) + WeightedRandomSampler.
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At a glance
- Task
- Text Classification
- License
- apache-2.0
- Access
- Public
- Created
- May 12, 2026
- Updated
- May 12, 2026
- SHA
- 0e18018d
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Model card
From the Hugging Face model README
XLMRShield
XLM-RoBERTa-base dual-head classifier for content moderation and prompt injection detection. Fine-tuned with 40:60 resampling + per-class cap (30K) + WeightedRandomSampler.
Test Results
| Metric | Score |
|---|---|
| Safety Accuracy | 0.8561 |
| Safety F1 | 0.5321 |
| Category Macro F1 | 0.6116 |
| Category Micro F1 | 0.6502 |
Per-Class F1
| Category | F1 |
|---|---|
| benign | 0.5339 |
| child_sexual_exploitation | 0.4491 |
| hate_and_harassment | 0.6302 |
| indiscriminate_weapons | 0.5208 |
| misinformation_and_specialized_advice | 0.4748 |
| non_violent_crimes | 0.7812 |
| pi_and_jailbreak | 0.6587 |
| privacy | 0.6972 |
| sexual_content | 0.6889 |
| suicide_and_self_harm | 0.5734 |
| violent_crimes | 0.7195 |
Architecture
- Backbone: xlm-roberta-base (278M params)
- Head 1: Safety binary (sigmoid)
- Head 2: 11-class multi-label (sigmoid)
- Tokenizer: xlm-roberta-base
Training
- Dataset: satyamsaf3ai/merged_content_moderation_and_prompt_injection_new
- Balancing: 40:60 resampling + per-class cap 30K + WeightedRandomSampler
- BF16 mixed precision, AdamW + cosine LR, 4 epochs
- max_length=128, batch_size=32, lr=2e-05
- Task
- Text Classification
- License
- apache-2.0
- Languages
- en
- Created
- May 12, 2026
- Updated
- May 12, 2026