Property	Value
Base Model	`xlm-roberta-base` (280M params)
Task	Binary text classification (toxic / not-toxic)
Languages	Turkish, Arabic, English
Training Data	105K balanced samples
Training	Focal loss, bf16, 15 epochs

Performance

Metric	Score
F1	91.3%
Accuracy	91.2%
Stress Test	97.7% (260/266)

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "gorkem371/toxicity-classifier-xlmr-base-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

text = "You are a wonderful person!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=-1)
    prediction = torch.argmax(probs, dim=-1).item()

labels = {0: "not-toxic", 1: "toxic"}
print(f"Text: {text}")
print(f"Prediction: {labels[prediction]} (confidence: {probs[0][prediction]:.3f})")

Training Details

Architecture: XLM-RoBERTa-base with a classification head (2 labels)
Loss Function: Focal loss (gamma=2) with inverse class weights
Precision: bf16 (bfloat16)
Epochs: 15
Dataset: 105K balanced samples across Turkish, Arabic, and English

License

Apache 2.0