opus-mt-th-en
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Architecture | Transformer-align |
| BLEU Score | 48.1 |
| chrF Score | 0.644 |
What is opus-mt-th-en?
opus-mt-th-en is a specialized machine translation model developed by Helsinki-NLP for translating Thai text to English. Built using the transformer-align architecture, this model demonstrates strong performance with a BLEU score of 48.1 on the Tatoeba test set.
Implementation Details
The model employs normalization and SentencePiece tokenization with spm32k vocabulary for both source and target languages. It was trained on the OPUS dataset and released on June 17, 2020.
- Pre-processing: Normalization + SentencePiece (spm32k,spm32k)
- Source language: Thai (tha)
- Target language: English (eng)
- Testing metrics: BLEU score of 48.1 and chrF score of 0.644
Core Capabilities
- High-quality Thai to English translation
- Optimized for general-purpose translation tasks
- Supports inference endpoints for deployment
- Compatible with both PyTorch and TensorFlow frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in Thai-to-English translation with state-of-the-art performance, demonstrated by its high BLEU score of 48.1. It uses advanced preprocessing techniques and the transformer-align architecture for optimal translation quality.
Q: What are the recommended use cases?
The model is ideal for applications requiring Thai-to-English translation, including content localization, document translation, and cross-lingual information processing. With its strong performance metrics, it's suitable for both production and research environments.
