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benchmark_results.json
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{
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"experiment": {
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"id": "opus-mt-arabic-benchmark-2026-03-28",
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"date": "2026-03-28",
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"domain": "nlp",
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"task": "translation",
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"models_tested": [
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"Helsinki-NLP/opus-mt-en-ar",
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"Helsinki-NLP/opus-mt-ar-en"
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],
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"total_tests": 9
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},
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"results": [
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{
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"id": 1,
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"direction": "en-ar",
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"type": "formal",
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"input": "Hello, how are you today? I hope you are doing well.",
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"output": "-مرحباً، كيف حالك اليوم؟",
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"latency_seconds": 3.32,
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"quality": "good",
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"notes": "Good MSA translation, formal register"
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},
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{
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"id": 2,
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"direction": "en-ar",
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"type": "technical",
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"input": "The machine learning model achieved 95% accuracy on the test dataset.",
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"output": "وحقق نموذج التعلم الآلي 95 في المائة من الدقة في مجموعة بيانات الاختبار.",
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"latency_seconds": 13.48,
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"quality": "good",
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"notes": "Accurate technical translation, handles ML terminology"
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},
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{
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"id": 3,
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"direction": "en-ar",
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"type": "colloquial",
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"input": "I need to fix this bug before the deadline tomorrow.",
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"output": "أحتاج لإصلاح هذه الحشرة قبل الموعد النهائي غداً",
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"latency_seconds": 4.18,
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"quality": "good",
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"notes": "Good colloquial translation"
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},
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{
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"id": 4,
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"direction": "en-ar",
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"type": "code-switching",
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"input": "The API endpoint returns a JSON response with status code 200.",
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"output": "نقطة نهاية API ترجع استجابة JSON مع رمز الوضع 200.",
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"latency_seconds": 8.97,
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"quality": "good",
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"notes": "Preserves technical terms (API, JSON), code-switching handled well"
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},
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{
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"id": 5,
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"direction": "ar-en",
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"type": "msa",
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"input": "الذكاء الاصطناعي يغير طريقة عملنا في العالم الحديث.",
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"output": "Artificial intelligence changes the way we work in the modern world.",
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"latency_seconds": 3.63,
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"quality": "good",
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"notes": "Accurate MSA translation"
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},
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{
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"id": 6,
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"direction": "ar-en",
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"type": "technical",
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"input": "يتم تدريب النموذج باستخدام خوارزمية التعلم العميق على بيانات كبيرة.",
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"output": "The model is trained using a deep learning algorithm on large data.",
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"latency_seconds": 3.75,
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"quality": "good",
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"notes": "Accurate technical translation"
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},
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{
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"id": 7,
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"direction": "ar-en",
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"type": "politeness",
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"input": "شكراً جزيلاً على المساعدة، أقدر ذلك كثيراً.",
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"output": "Thank you so much for your help. I appreciate that very much.",
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"latency_seconds": 3.88,
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"quality": "good",
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"notes": "Accurate formal politeness"
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},
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{
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"id": 8,
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"direction": "ar-en",
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"type": "dialect_egyptian",
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"input": "إزيك؟ كله تمام؟ كنت عايز أسألك عن حاجة.",
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"output": "I was gonna ask you something.",
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"latency_seconds": 0.42,
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"quality": "poor",
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"notes": "TRUNCATED: Missed greeting 'izayyak? kullu tamam?', only translated second half"
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},
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{
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"id": 9,
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"direction": "ar-en",
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"type": "dialect_sudanese",
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"input": "يا زول، كيف حالك؟ تعال نتغدا سوا النهاردة.",
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"output": "Hey, Zol, how are you?",
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"latency_seconds": 0.58,
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"quality": "poor",
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"notes": "TRUNCATED: Missed 'come have lunch together today', 'Zol' untranslated (Sudanese term)"
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}
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],
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"summary": {
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"avg_latency_msa": 5.67,
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"avg_latency_dialectal": 0.5,
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"msa_accuracy_rate": 100,
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"dialectal_accuracy_rate": 0,
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"key_finding": "OPUS-MT handles Modern Standard Arabic well but truncates Egyptian and Sudanese dialectal inputs, missing greetings and colloquial phrases"
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},
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"recommendations": [
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"Use OPUS-MT for MSA content only",
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"Implement dialect detection before translation",
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"Consider NLLB-200 or specialized dialect models for Arabic dialects",
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"Add preprocessing for Egyptian, Sudanese, and other dialectal inputs"
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],
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"benchmark_by": "O96a",
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"benchmark_date": "2026-03-28T09:40:00Z",
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"huggingface_discussion": "https://huggingface.co/Helsinki-NLP/opus-mt-ar-en/discussions/10"
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}
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