Nexus-Analytics/multilingual-hatespeech-detection-model

Bilingual hate / personal-toxicity detection for university course evaluations (EN + Egyptian AR)

Binary classifier: `hate` = personal abuse / insult / contempt toward an individual (instructor, TA); `normal` = everything else — including legitimate harsh-but-fair criticism, which must not be flagged (the project's cardinal rule).

This repo hosts every checkpoint of the campaign under models/<run_id>/. Use the champion below; everything else is history.

🏆 Production champion: cln5B + cmarnols (probability ensemble, 0.7 / 0.3)

No language router — the same blend scores every input.

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

REPO = "Nexus-Analytics/multilingual-hatespeech-detection-model"
PARTS = [("models/20260612_subset_cln5B", 0.7),
         ("models/20260612_133541_MARBERTv2_v38dsoupnols", 0.3)]

def load(sub):
    tok = AutoTokenizer.from_pretrained(REPO, subfolder=sub)
    m = AutoModelForSequenceClassification.from_pretrained(REPO, subfolder=sub).eval()
    hate = next(i for i, v in m.config.id2label.items() if str(v).lower() == "hate")
    return tok, m, int(hate)

members = [(load(sub), w) for sub, w in PARTS]

def p_hate(text):
    p = 0.0
    for (tok, m, hi), w in members:
        enc = tok(text, return_tensors="pt", truncation=True, max_length=256)
        with torch.no_grad():
            p += w * m(**enc).logits.softmax(-1)[0, hi].item()
    return p   # hate if >= 0.5; route p in [0.35, 0.65] to human review

Headline metrics (frozen 738-case bilingual challenge sets + held-out test split)

Single-model alternatives (one checkpoint, one forward pass)

How it was built (short version)

1. 1.~26k-row bilingual dataset; the key labeling decision: the Arabic survey's offensive class (harsh criticism) maps to normal, with only ~32 genuinely contemptuous rows excepted.
2. 2.+1,050 verified hard-negatives teaching criticism→normal, sarcastic-praise→hate, hedged-contempt→hate.
3. 3.Seed variance discovery: identical recipes swing ±10 challenge misses across random seeds → single runs are never compared. Each recipe trains 6 seeds (3 seeds × ±label smoothing); all 57 seed-subsets are searched in probability space, ranked by validation only, and the winners are built as parameter-averaged weight soups.
4. 4.Architecture diversity (XLM-R + MARBERT) rescues knife-edge cases neither family fixes alone (19 → 13 misses).

Intended use & limitations

• —Built for Egyptian university course-evaluation comments (English, Egyptian Arabic, and their code-switching). Out of that domain, re-validate before trusting it.
• —Nearly all Arabic training data is synthetic (Gemini-generated, human-audited); the remaining known weakness is Arabic "write-off" hate (total dismissals without insult words): 9–11/13 recall on the hardest curated cases.
• —Recommended serving: abstain band p ∈ [0.35, 0.65] → human review; strip one trailing period before scoring (measured robustness guardrail).
• —This is a moderation aid, not a disciplinary instrument: scores are evidence for a human process, never an automatic verdict on a student or instructor.