TurkWeb-Edu-Classifier-V3

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.

Model Details

Scoring Rubric

Usage

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

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).