Display translation benchmark results from NTREX dataset
Convert PDFs to a dataset and upload to Hugging Face
Rename models in dataset leaderboard
Perform OSINT analysis, fetch URL titles, fine-tune models
ReWrite datasets with a text instruction
Organize and process datasets for AI models
Explore and edit JSON datasets
Data annotation for Sparky
Manage and orchestrate AI workflows and datasets
Build datasets using natural language
Manage and analyze labeled datasets
Explore recent datasets from Hugging Face Hub
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.