| | --- |
| | language: tr |
| | license: apache-2.0 |
| | tags: |
| | - text-classification |
| | - educational-content |
| | - turkish |
| | - fineweb-edu |
| | - encoder |
| | - regression |
| | datasets: |
| | - YsK-dev/TurkWeb-Edu-AnnotationsV3 |
| | base_model: boun-tabilab/TabiBERT |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | # TurkWeb-Edu Classifier V4 🇹🇷 |
| |
|
| | Fast, accurate Turkish educational content classifier. 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).** |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| | import torch |
| | |
| | model_name = "YsK-dev/TurkWeb-Edu-Classifier-V4" |
| | 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=1024) |
| | with torch.no_grad(): |
| | score = model(**inputs).logits.squeeze().item() |
| | |
| | print(f"Score: {score:.2f}") |
| | print(f"Educational: {score >= 3}") |
| | ``` |
| |
|
| | ## Model Details |
| |
|
| | | Component | Details | |
| | |-----------|---------| |
| | | Base Model | `boun-tabilab/TabiBERT` | |
| | | Architecture | Encoder + Regression Head | |
| | | Training Data | [YsK-dev/TurkWeb-Edu-AnnotationsV3](https://huggingface.co/datasets/YsK-dev/TurkWeb-Edu-AnnotationsV3) (660K samples) | |
| | | Teacher | Qwen3-30B-A3B-Instruct-2507 | |
| | | Task | Regression (0-5 educational quality score) | |
| | | Language | Turkish (tur_Latn) | |
| | |
| | ## Evaluation |
| | |
| | | Metric | Value | |
| | |--------|-------| |
| | | Pearson | 0.8312000036239624 | |
| | | RMSE | 0.8874 | |
| | | MAE | 0.6416000127792358 | |
| | | F1 (edu≥3) | 0.7197 | |
| | | Exact Accuracy | 0.5044 | |
| | |
| | ## 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 | |
| | |
| | ## Recommended Threshold |
| | |
| | For filtering educational Turkish content, use `score >= 3` (following FineWeb-Edu methodology). |
| | |