YsK-dev
/

TurkWeb-Edu-Classifier-V3

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 ---
+language: tr
+license: apache-2.0
+tags:
+  - text-classification
+  - educational-content
+  - turkish
+  - fineweb-edu
+  - qwen3
+datasets:
+  - YsK-dev/TurkWeb-Edu-AnnotationsV3
+base_model: Qwen/Qwen3-0.6B-Base
+pipeline_tag: text-classification
 ---
+# TurkWeb-Edu Classifier V3 🇹🇷
+A **Turkish educational content classifier** that predicts educational quality scores (0-5) for Turkish web text.
+This is the Turkish equivalent of [HuggingFaceFW/fineweb-edu-classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier).
 ## Model Details
+| Component | Details |
+|---|---|
+| **Base Model** | `Qwen/Qwen3-0.6B-Base` |
+| **Architecture** | Qwen3 + Regression Head (LoRA fine-tuned, merged) |
+| **Teacher Model** | `Qwen/Qwen3-30B-A3B-Instruct-2507` |
+| **Training Data** | [YsK-dev/TurkWeb-Edu-AnnotationsV3](https://huggingface.co/datasets/YsK-dev/TurkWeb-Edu-AnnotationsV3) (660K samples) |
+| **Task** | Regression (0-5 educational quality score) |
+| **Language** | Turkish (tur_Latn) |
+## Scoring Rubric
+| Score | Meaning |
+|---|---|
+| 0 | **Not Educational** — Spam, ads, NSFW, navigation-only |
+| 1 | **Low Quality** — Personal chat, forum posts, low-quality news |
+| 2 | **Medium** — General culture, blog, opinion pieces |
+| 3 | **Educational** — Encyclopedic, how-to guides, concept explanations |
+| 4 | **High Quality** — Well-structured, high pedagogical value, technical |
+| 5 | **Academic** — Textbook quality, sourced, in-depth analysis |
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model_name = "YsK-dev/TurkWeb-Edu-Classifier-V3"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+text = "Fotosentez, bitkilerin güneş ışığını kullanarak karbondioksit ve suyu glikoz ve oksijene dönüştürdüğü biyokimyasal bir süreçtir."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    score = model(**inputs).logits.squeeze().item()
+print(f"Score: {score:.2f}")
+print(f"Int Score: {int(round(max(0, min(score, 5))))}")
+# Expected: High score (4-5) for this educational text about photosynthesis
+```
 ## Evaluation
+| Metric | Value |
+|---|---|
+| MSE | 1.1642 |
+| RMSE | 1.0790 |
+| MAE | 0.8374 |
+| F1 (edu≥3) | 0.7147 |
+| F1 (weighted) | 0.3956 |
+| Accuracy | 0.3769 |
+## Training Pipeline
+1. **Teacher Annotation**: Qwen3-30B-A3B annotated 840K Turkish web samples from FineWeb-2 (tur_Latn)
+2. **Deduplication**: SHA256 text dedup → 660K unique samples
+3. **Student Training**: Qwen3-0.6B-Base + LoRA (r=16) fine-tuned for 3 epochs
+4. **Merging**: LoRA weights merged into base model for efficient inference
+## Recommended Threshold
+For filtering educational Turkish content, use `score >= 3` (following the FineWeb-Edu methodology).