Display translation benchmark results from NTREX dataset
Manage and analyze labeled datasets
Convert PDFs to a dataset and upload to Hugging Face
Create a domain-specific dataset seed
List of French datasets not referenced on the Hub
Display html
Upload files to a Hugging Face repository
Convert and PR models to Safetensors
Access NLPre-PL dataset and pre-trained models
Manage and label your datasets
Launch and explore labeled datasets
Create a report in BoAmps format
TREX Benchmark En Ru Zh is a tool designed to display and compare translation benchmark results from the NTREX dataset. It focuses on evaluating machine translation systems between English, Russian, and Chinese. The benchmark provides a comprehensive framework to assess translation quality, accuracy, and performance across different language pairs.
• Multilingual Support: Covers English, Russian, and Chinese translations for a broad evaluation scope.
• Detailed Metrics: Offers in-depth analysis of translation quality through various evaluation metrics.
• Batch Processing: Allows users to process multiple translations simultaneously for efficient benchmarking.
• Interactive Visualizations: Provides graphical representations of results for easier interpretation.
• Custom Filtering: Enables users to focus on specific aspects of translation performance.
What languages does TREX Benchmark En Ru Zh support?
TREX Benchmark En Ru Zh supports English, Russian, and Chinese translations for benchmarking.
How do I interpret the benchmark results?
Results are provided in the form of scores and visualizations. Higher scores generally indicate better translation quality, depending on the metric used.
Can I use custom metrics for evaluation?
No, TREX Benchmark En Ru Zh currently uses predefined metrics like BLEU, ROUGE, and METEOR for consistency and comparability.