opus-mt-hi-en
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Framework | PyTorch, TensorFlow |
| Task | Hindi to English Translation |
| Downloads | 43,164 |
What is opus-mt-hi-en?
opus-mt-hi-en is a specialized machine translation model developed by Helsinki-NLP for translating Hindi text to English. Built on the transformer-align architecture, this model has been trained on the OPUS dataset with careful pre-processing including normalization and SentencePiece tokenization.
Implementation Details
The model employs a transformer-based architecture optimized for neural machine translation. It has demonstrated strong performance across various benchmark datasets, particularly excelling on the Tatoeba dataset with a BLEU score of 40.4.
- Pre-processing: Includes normalization and SentencePiece tokenization
- Architecture: transformer-align
- Training Dataset: OPUS collection
- Benchmark Performance: BLEU scores ranging from 9.1 to 40.4
Core Capabilities
- Direct Hindi to English translation
- Handles various text domains
- Supports both PyTorch and TensorFlow frameworks
- Production-ready with inference endpoints
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in Hindi to English translation with impressive performance on the Tatoeba dataset (BLEU 40.4). It's particularly noteworthy for its dual framework support and production-ready implementation.
Q: What are the recommended use cases?
The model is ideal for Hindi to English translation tasks, particularly effective for general-purpose translation as evidenced by its strong performance on diverse test sets including news and Tatoeba datasets.
