---
license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
  - qwen3
  - bias-detection
  - news-debiasing
  - text-generation
  - fine-tuned
  - unsloth
  - sft
datasets:
  - vector-institute/unbias-plus-dataset
language:
  - en
---

# Qwen3-8B-UnBias-Plus-SFT

A fine-tuned version of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) for news media bias detection and neutral rewriting, developed by the [Vector Institute](https://vectorinstitute.ai) as part of the [UnBias-Plus](https://github.com/VectorInstitute/unbias-plus) project.

Given a news article, the model identifies biased language segments, classifies their bias type and severity, provides neutral replacements, and returns a fully rewritten unbiased version of the article — all in a single structured JSON response.

> **Trained on:** [train_1 — UnBias-Plus](https://huggingface.co/datasets/vector-institute/unbias-plus-dataset)


## Model Details

| Property | Value |
|---|---|
| Base model | Qwen/Qwen3-8B |
| Fine-tuning method | Supervised Fine-Tuning (SFT) with LoRA |
| Training precision | bf16 (full precision, no quantization during training) |
| LoRA rank | 16 |
| Training framework | Unsloth + TRL |
| Context length | 8192 tokens |
| Output format | Structured JSON |
| Training dataset   | [UnBias-Plus (train_1)](https://huggingface.co/datasets/vector-institute/unbias-plus-dataset) |


## Usage

```python
from unsloth import FastLanguageModel
import torch, json

model, tokenizer = FastLanguageModel.from_pretrained(
    "vector-institute/Qwen3-8B-UnBias-Plus-SFT",
    max_seq_length=8192,
    load_in_4bit=False,   # set True for ~5GB VRAM (laptop)
    dtype=torch.bfloat16,
)
FastLanguageModel.for_inference(model)

SYSTEM_PROMPT = """You are an expert linguist and bias detection specialist.
Your task is to carefully read a news article, detect ALL biased language,
and return a structured JSON response. Return ONLY valid JSON, no extra text."""

article = "Your news article here..."

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": f"Analyze the following article for bias and return the result in the required JSON format.\n\nARTICLE:\n{article}"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=True,
    return_tensors="pt",
    return_dict=True,
)

outputs = model.generate(
    input_ids=inputs["input_ids"].to("cuda"),
    attention_mask=inputs["attention_mask"].to("cuda"),
    max_new_tokens=4096,
    temperature=0.1,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id,
)

new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
response = tokenizer.decode(new_tokens, skip_special_tokens=True)

# Extract JSON — strip thinking block if present
if "</think>" in response:
    response = response.split("</think>", 1)[-1].strip()

result = json.loads(response)
```

## Output Schema

```json
{
  "binary_label": "biased" | "unbiased",
  "severity": 0 | 2 | 3 | 4,
  "bias_found": true | false,
  "biased_segments": [
    {
      "original": "exact substring from input article",
      "replacement": "neutral alternative phrase",
      "severity": "high" | "medium" | "low",
      "bias_type": "loaded language | dehumanizing framing | false generalizations | framing bias | euphemism/dysphemism | politically charged terminology | sensationalism",
      "reasoning": "1-2 sentence explanation"
    }
  ],
  "unbiased_text": "Full rewritten neutral article"
}
```

### Severity Scale
| Value | Meaning |
|---|---|
| 0 | Neutral — no bias detected |
| 2 | Recurring biased framing |
| 3 | Strong persuasive tone |
| 4 | Inflammatory rhetoric |

## Bias Types Detected

- **Loaded language** — words with strong emotional connotations
- **Dehumanizing framing** — language that strips dignity from groups
- **False generalizations** — sweeping statements ("they always", "all of them")
- **Framing bias** — selective wording that implies a viewpoint
- **Euphemism/dysphemism** — softening or hardening language to manipulate perception
- **Politically charged terminology** — labels used to provoke rather than describe
- **Sensationalism** — exaggerated language to evoke emotional responses


## Hardware Requirements

| Setup | Configuration |
|---|---|
| Recommended (server) | `load_in_4bit=False, dtype=torch.bfloat16` (~16GB VRAM) |
| Lightweight (laptop) | `load_in_4bit=True` (~5GB VRAM) |


## Limitations

- Trained primarily on English-language news articles
- Political bias detection reflects patterns in the training data
- Best performance on articles under 5000 characters
- As with all language models, outputs should be reviewed by a human before use in production

## Citation

If you use this model in your research or application, please cite:

```@article{radwan2026unbias,
  title={UnBias-Plus: Detect, Explain, and Rewrite Bias},
  author={Radwan, Ahmed Y and ElKady, Ahmed and Chaduvula, Sindhuja and Hafez, Mohamed and Krishnan, Amrit and Raza, Shaina},
  journal={arXiv preprint arXiv:2606.23412},
  year={2026}
}
```

## Links

- 📊 Leaderboard: [UnBias-Plus Leaderboard](https://huggingface.co/spaces/vector-institute/UnBias-Plus-Leaderboard)
- 📁 Dataset: [vector-institute/unbias-plus-dataset](https://huggingface.co/datasets/vector-institute/unbias-plus-dataset)
- 🏛️ Organization: [Vector Institute](https://vectorinstitute.ai)
- 🌐 Project: [AIXpert](https://aixpert-project.eu/)