language benchmark
Translation Set1→en spBleu
spBLEU (SentencePiece BLEU) evaluation metric for machine translation quality assessment, using language-agnostic SentencePiece tokenization with BLEU scoring. Part of the FLORES-101 evaluation benchmark for low-resource and multilingual machine translation.
Updated Aug 7, 2026
Published models3
Registry coverage3
MetricScore
EvidenceB
Translation Set1→en spBleu leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | AM Nova ProAmazon | 44.4% | 100.0% | 3 | C | |
| 2 | AMNova LiteAmazon | 43.1% | 50.0% | 3 | C | |
| 3 | AMNova MicroAmazon | 42.6% | 0.0% | 3 | C |
Score distribution
Top published rows on the benchmark's original scale.
Translation Set1→en spBleu highlights
The top published results on this benchmark's own scale.
What is Translation Set1→en spBleu?
Definition and scoring fields from the benchmark registry.
spBLEU (SentencePiece BLEU) evaluation metric for machine translation quality assessment, using language-agnostic SentencePiece tokenization with BLEU scoring. Part of the FLORES-101 evaluation benchmark for low-resource and multilingual machine translation.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- Translation Set1→en spBleu
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- translation-set1→en-spbleu|llm-stats-current
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
FAQ
Common questions about Translation Set1→en spBleu.
Which model scores highest on Translation Set1→en spBleu?
Nova Pro is currently ranked first with 44.4%.
What does Translation Set1→en spBleu measure?
spBLEU (SentencePiece BLEU) evaluation metric for machine translation quality assessment, using language-agnostic SentencePiece tokenization with BLEU scoring. Part of the FLORES-101 evaluation benchmark for low-resource and multilingual machine translation.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 unique published model results are currently shown.
Does this benchmark affect the overall score?
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.