qwen3-4b-instruct-2507-vihsd-explainable
Overview
This repository provides a LoRA-finetuned and merged version of
unsloth/Qwen3-4B-Instruct-2507 for Vietnamese toxic content moderation (ViHSD).
Unlike standard classification models, this model is trained to generate structured, explainable outputs in JSON format, including:
label: CLEAN | OFFENSIVE | HATEexplanation: short Vietnamese rationaleevidence: verbatim substrings supporting the decision
The primary goal of this work is task-aligned, explainable moderation, not only raw label prediction.
Base model
- Architecture: Qwen3-4B-Instruct-2507
- Provider: unsloth
- Type: Instruction-tuned causal language model
Fine-tuned model
- Fine-tuning method: LoRA (parameter-efficient fine-tuning)
- Trainable parameters: ~0.8% of total parameters
- Output format: Strict JSON (label + explanation + evidence)
- Language: Vietnamese
Dataset
- Dataset name:
vominhmanh/vihsd-explainable
- Derived from: ViHSD (Vietnamese Hate Speech Detection)
- Splits:
- Train: 24,048 samples
- Validation: 2,672 samples
- Test: 6,680 samples
- Annotations:
label (CLEAN / OFFENSIVE / HATE)explanation (Vietnamese rationale)evidence (verbatim toxic spans, empty for CLEAN)
Training setup
- Objective: Causal language modeling (NLL loss)
- Prompt format: Chat-style (user → assistant JSON response)
- LoRA configuration:
- r = 16
- alpha = 32
- dropout = 0
- Precision: bf16
- Optimizer: AdamW (8-bit)
- Epochs: 2
- Sequence length: 512
- Framework: Unsloth + TRL
SFTTrainer
Evaluation protocol
Metric
- Macro F1 (3-class) over labels {CLEAN, OFFENSIVE, HATE}
Evaluation procedure
- Base model and fine-tuned model were evaluated using:
- identical prompt
- identical JSON parsing logic
- identical evaluation script
- Evaluation performed on 6,680 test samples
Results
| Model | Macro F1 (3-class) |
|---|
| Base: Qwen3-4B-Instruct-2507 | 0.5085 |
| Fine-tuned (this work) | 0.6370 |
Interpretation
- Fine-tuning yields a +12.85 absolute Macro F1 improvement over the base model.
- This demonstrates that the base instruction-tuned model is not well-aligned with ViHSD-specific moderation definitions.
- LoRA fine-tuning significantly improves task alignment and label discrimination, even with <1% trainable parameters.
Why this improvement matters
The improvement is not only quantitative but also qualitative:
- Better label discrimination
- Reduced confusion between OFFENSIVE and HATE
- Stronger alignment with ViHSD definitions
- Fine-tuned model follows dataset-specific guidelines
- Explainable outputs
- Consistent explanations and evidence spans
- Structured generation
- Reliable JSON output suitable for downstream pipelines
Limitations
- Evaluation focuses on label-level Macro F1; explanation and evidence quality are not fully captured by this metric.
- Explanations are generated heuristically and may still contain errors.
- Not intended for fully automated moderation without human review.
Intended use
- Research on Vietnamese toxic content moderation
- Explainable AI for content review systems
- Human-in-the-loop moderation pipelines
License
Please verify compatibility with:
- Base model license:
unsloth/Qwen3-4B-Instruct-2507
- Dataset license:
vominhmanh/vihsd-explainable
Citation
If you use this model, please cite:
- ViHSD dataset
- Qwen3-4B-Instruct
- This repository
