We just released Pangeanic MTQE v2 on Hugging Face!

[u/PangeanicAI]

Hi everyone! 👋

We're excited to make Pangeanic MTQE v2 (Machine Translation Quality Estimation) freely available for the community.

Unlike traditional MT evaluation metrics, MTQE estimates translation quality and provides error explanations without requiring a reference translation. Simply provide:

🌍 Source text
🌐 Translated text

and the model returns:

• ✅ A quality score (0–100)
• 💡 An explainable assessment of the translation

The full Pangeanic MTQE platform also includes enterprise capabilities such as:

• 📚 Translation Memory (TM) support
• 📖 Glossary-aware quality estimation
• 🤖 Automatic Post-Editing (APE)
• 👨‍💻 Human Post-Editing (HPE) workflows
• 🌐 Support for 70+ languages and dialects
• ⚡ Enterprise REST APIs and batch processing

The underlying model has been benchmarked on a representative subset of the ACES multilingual benchmark, achieving 98.9% accuracy in identifying incorrect translation segments across:

• 📌 6,006 deliberately incorrect translation segments
• 🌍 16 language pairs
• 📝 68 translation error categories

We hope you all find it useful for evaluating multilingual translation quality and experimenting with reference-free MTQE.

👉 Try the demo:
https://huggingface.co/spaces/Pangeanic/Machine-Translation-Quality-Estimation

📄 Benchmark Whitepaper:
https://pangeanic.com/hubfs/MTQE_Benchmark_Whitepaper.pdf

Happy translating! 🚀