Datasets:
experiment dict | results list | summary dict | recommendations list | benchmark_by string | benchmark_date timestamp[s] | huggingface_discussion string |
|---|---|---|---|---|---|---|
{
"id": "opus-mt-arabic-benchmark-2026-03-28",
"date": "2026-03-28T00:00:00",
"domain": "nlp",
"task": "translation",
"models_tested": [
"Helsinki-NLP/opus-mt-en-ar",
"Helsinki-NLP/opus-mt-ar-en"
],
"total_tests": 9
} | [
{
"id": 1,
"direction": "en-ar",
"type": "formal",
"input": "Hello, how are you today? I hope you are doing well.",
"output": "-مرحباً، كيف حالك اليوم؟",
"latency_seconds": 3.32,
"quality": "good",
"notes": "Good MSA translation, formal register"
},
{
"id": 2,
"direction"... | {
"avg_latency_msa": 5.67,
"avg_latency_dialectal": 0.5,
"msa_accuracy_rate": 100,
"dialectal_accuracy_rate": 0,
"key_finding": "OPUS-MT handles Modern Standard Arabic well but truncates Egyptian and Sudanese dialectal inputs, missing greetings and colloquial phrases"
} | [
"Use OPUS-MT for MSA content only",
"Implement dialect detection before translation",
"Consider NLLB-200 or specialized dialect models for Arabic dialects",
"Add preprocessing for Egyptian, Sudanese, and other dialectal inputs"
] | O96a | 2026-03-28T09:40:00 | https://huggingface.co/Helsinki-NLP/opus-mt-ar-en/discussions/10 |
OPUS-MT Arabic-English Translation Benchmark
Experiment Details
- Date: 2026-03-28
- Models Tested:
Helsinki-NLP/opus-mt-en-ar(English → Arabic)Helsinki-NLP/opus-mt-ar-en(Arabic → English)
- Total Tests: 9
- Domain: NLP / Translation
Summary
| Metric | Value |
|---|---|
| MSA Accuracy Rate | 100% |
| Dialectal Accuracy Rate | 0% |
| Avg Latency (MSA) | 5.67s |
| Avg Latency (Dialectal) | 0.5s |
Key Finding
OPUS-MT handles Modern Standard Arabic (MSA) well but truncates Egyptian and Sudanese dialectal inputs.
- Egyptian: "إزيك؟ كله تمام؟" → "I was gonna ask you something" (missed greeting entirely)
- Sudanese: "يا زول، كيف حالك؟ تعال نتغدا سوا" → "Hey, Zol, how are you?" (missed half)
Results Table
| # | Direction | Type | Latency | Quality |
|---|---|---|---|---|
| 1 | EN→AR | Formal | 3.32s | ✅ Good |
| 2 | EN→AR | Technical | 13.48s | ✅ Good |
| 3 | EN→AR | Colloquial | 4.18s | ✅ Good |
| 4 | EN→AR | Code-switching | 8.97s | ✅ Good |
| 5 | AR→EN | MSA | 3.63s | ✅ Good |
| 6 | AR→EN | Technical | 3.75s | ✅ Good |
| 7 | AR→EN | Politeness | 3.88s | ✅ Good |
| 8 | AR→EN | Egyptian dialect | 0.42s | ❌ Truncated |
| 9 | AR→EN | Sudanese dialect | 0.58s | ❌ Missed half |
Recommendations
- Use OPUS-MT for MSA content only
- Implement dialect detection before translation
- Consider NLLB-200 or specialized dialect models for Arabic dialects
- Add preprocessing for Egyptian, Sudanese, and other dialectal inputs
References
- Model Discussion: Helsinki-NLP/opus-mt-ar-en/discussions/10
- Benchmark by: O96a
- Downloads last month
- 33