toxicity-classifier-xlmr-base-v5

Multilingual Toxicity Classifier (XLM-RoBERTa-base V5)

Binary toxicity classifier for Turkish, Arabic, and English text, built on XLM-RoBERTa-base. V5 focuses on real-world chat robustness: Arabic dialects, Arabizi / Franco-Arabic, obfuscation (diacritics, tatweel, elongation, homoglyphs), implicit toxicity, and reduced false positives on dialectal greetings and negation / counter-speech.

Author

• Görkem Yıldız · GitHub: gorkem371

Model Details

Performance

On an internal hard adversarial "frontier" set (dialects, Arabizi, obfuscation, implicit, negation), V5 scores 88% vs 38% for V4 and 41% for V3 — while preserving V4's explicit-toxicity and Arabizi gains.

⚠️ Important: apply the same text normalization at inference

V5 was trained on normalized text (strip Arabic diacritics/tatweel, unify alef/ya, collapse character elongation). You must apply the same normalization before tokenizing, or robustness to diacritics/obfuscation degrades. The text_normalize.py used for training is included in this repo.

import re, unicodedata, torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

_HARAKAT = re.compile("[ؐ-ًؚ-ٰٟۖ-ۜ۟-۪ۨ-ۭـ]")  # diacritics + tatweel
_ALEF = re.compile("[آأإٱ]")
def normalize_text(t):
    t = unicodedata.normalize("NFKC", t)
    t = _HARAKAT.sub("", t)
    t = _ALEF.sub("ا", t)
    t = t.replace("ى", "ي").replace("ؤ", "و").replace("ئ", "ي")
    t = re.sub(r"(.)\1{2,}", r"\1\1", t)  # collapse 3+ repeats
    return re.sub(r"\s+", " ", t).strip()

name = "gorkem371/toxicity-classifier-xlmr-base-v5"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForSequenceClassification.from_pretrained(name).eval()

def classify(text):
    text = normalize_text(text)
    inputs = tok(text, return_tensors="pt", truncation=True, max_length=256)
    with torch.no_grad():
        probs = torch.softmax(model(**inputs).logits, dim=-1)[0]
    label = model.config.id2label[int(probs.argmax())]
    return label, float(probs.max())

print(classify("يا كلب اخرس"))          # ('toxic', ...)
print(classify("this is fucking awesome"))  # ('not-toxic', ...)  (profanity-as-emphasis)
print(classify("lan naber nasılsın"))    # ('not-toxic', ...)

What V5 handles well

• Arabic dialects: MSA, Egyptian, Levantine, Gulf, Iraqi, Maghrebi, Sudanese, Libyan (toxic + clean greetings)
• Arabizi / Franco-Arabic (Latin+numbers) in both directions
• Obfuscation: diacritics (tashkeel), tatweel, character elongation, homoglyphs, dotted/spaced letters, emoji
• Negation / counter-speech and profanity-as-positive-emphasis (fewer false positives)
• Self-deprecation and casual interjections (e.g. Turkish "lan")

Known limitations

• Veiled / polite threats with no explicit words ("I know where you live")
• Sarcasm / irony where the surface is positive
• Coded bias / dog-whistles
• Some benign homographs (e.g. talking about a pet dog/pig can occasionally false-positive)
• Turkish negation ("X is not stupid") can still false-positive
• In-group / reclaimed slurs are flagged (a policy gray area)

Training approach

Base data (~100K balanced samples across TR/AR/EN from public datasets) plus iteratively hand-crafted targeted sets that close specific failure modes found by adversarial testing (Arabizi, "يا [insult]" vocative, implicit toxicity, dialect greetings, negation, homographs). Targeted samples are repeated 10× and merged onto the base; all text is normalized at train and inference time.

License

Apache 2.0