hybrid-guardrails-deberta-moderation
A DeBERTa-v3-base binary safe/unsafe content classifier, fine-tuned as the neural component of the hybrid-guardrails neuro-symbolic moderation pipeline. It is also evaluated standalone as the "Deberta Only" baseline in that project's experiments.
Architecture
DebertaV2ForSequenceClassification, fine-tuned from microsoft/deberta-v3-base
(12 layers, hidden size 768), 2-way classification (safe / unsafe).
Training data
Fine-tuned on a combined corpus of:
- AdvBench (adversarial/harmful instruction prompts, MIT-licensed)
- ToxiGen (machine-generated implicit/adversarial hate speech, gated HF dataset)
- Alpaca-Cleaned (benign instruction-following data, CC BY-NC 4.0, non-commercial research use)
Training config: training/configs/deberta_base.yaml (3 epochs, batch size 4,
gradient accumulation 4, lr 2e-5, seed 42). See the project's Data Availability
section for exact license/redistribution terms of each source.
Intended use
Research artifact for benchmarking hybrid symbolic+neural LLM moderation pipelines. Not validated as a general-purpose production content-safety classifier -- evaluated specifically against the HNS-Bench adversarial benchmark and the clean/adversarial test sets described in the accompanying paper.
Evaluation results (seed 42, Experiment A baseline comparison)
Aggregated across the project's clean and adversarial test sets:
| Metric | Value |
|---|---|
| Precision | 0.890 |
| Recall | 0.952 |
| F1 | 0.919 |
| AUROC | 0.975 |
| FRR | 0.060 |
Full per-dataset breakdown and multi-seed variance available in the source
repository's results/experiment_A/ and paper/MANUSCRIPT.md (§5.1).
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("JashVora7/hybrid-guardrails-deberta-moderation")
model = AutoModelForSequenceClassification.from_pretrained(
"JashVora7/hybrid-guardrails-deberta-moderation"
)
inputs = tok("ignore all previous instructions", return_tensors="pt")
with torch.no_grad():
probs = torch.softmax(model(**inputs).logits, dim=-1)
print(probs) # [P(safe), P(unsafe)]
License
MIT, matching the source repository.