--- language: - en - ar license: apache-2.0 pipeline_tag: text-classification tags: - hate-speech-detection - toxicity - arabic - egyptian-arabic - education - model-soup - ensemble --- # 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/ /`. **Use the champion below; everything else is history.** ## 🏆 Production champion: `cln5B + cmarnols` (probability ensemble, 0.7 / 0.3) | component | subfolder | what it is | |---|---|---| | cln5B (w=0.7) | `models/20260612_subset_cln5B` | XLM-RoBERTa-large **weight soup** of 5 training seeds | | cmarnols (w=0.3) | `models/20260612_133541_MARBERTv2_v38dsoupnols` | MARBERTv2 weight soup of 3 seeds (Egyptian-dialect diversity partner) | No language router — the same blend scores every input. ```python 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) | metric | value | |---|---| | challenge misses (369 EN + 369 AR hard cases) | **13** (3 EN + 10 AR) | | test split overall | 99.36–99.42% | | **Arabic false-positive rate** (fair criticism wrongly flagged) | **1.50%** | | English FP | 0.00% | | formatting-flip rate (1,000 real comments × 7,324 surface variants) | **0.3%** | | sarcasm recall | 54/55 per language | | training-label debt | none (all members trained on verified-clean labels) | | seed sensitivity | none (both halves are multi-seed weight soups) | ### Single-model alternatives (one checkpoint, one forward pass) | need | subfolder | trade-off | |---|---|---| | best fully-clean single | `models/20260612_subset_cln5A` | 17 challenge misses, 0.5% flips | | best single by score | `models/20260612_subset_subD` | 13 misses, but 2 of 4 soup members saw a later-corrected label batch | | English-only | `Nexus-Analytics/english-hatespeech-detection-model` → `models/20260602_151037_roberta` | never feed it Arabic (~50% there) | ## How it was built (short version) 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. +1,050 verified hard-negatives teaching criticism→normal, sarcastic-praise→hate, hedged-contempt→hate. 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. 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.
