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Model: vector-institute/Qwen3-8B-UnBias-Plus-SFT Source: Original Platform
2026-08-09 23:50:15 +08:00
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+---
+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/)