Persian Text Embedding Benchmark
Run benchmarks on prediction models
Display model benchmark results
Determine GPU requirements for large language models
Create and upload a Hugging Face model card
Explore and submit models using the LLM Leaderboard
Measure BERT model performance using WASM and WebGPU
Find and download models from Hugging Face
Push a ML model to Hugging Face Hub
Calculate survival probability based on passenger details
Compare code model performance on benchmarks
Create demo spaces for models on Hugging Face
Upload ML model to Hugging Face Hub
The PTEB Leaderboard is a comprehensive benchmarking platform designed for evaluating and comparing the performance of Persian text embedding models. It provides a centralized space to view detailed performance metrics, enabling users to understand the capabilities and limitations of various models. By leveraging this tool, researchers and developers can make informed decisions about which models best suit their specific use cases.
• Model Comparison: Compare multiple Persian text embedding models side-by-side based on their performance metrics. • Performance Metrics: View detailed benchmarks, including accuracy, F1-score, and embedding quality for different models. • Visualization Tools: Access interactive charts and graphs to better understand model performance trends. • Customizable Filters: Narrow down results based on specific criteria such as dataset, task type, or model architecture. • Community Sharing: Share benchmark results with colleagues or the broader research community. • Regular Updates: Stay up-to-date with the latest models and their performance metrics.
What is the PTEB Leaderboard?
The PTEB Leaderboard is a benchmarking platform for Persian text embedding models, providing detailed performance metrics for comparison.
Which models are supported by the PTEB Leaderboard?
The platform supports a wide range of Persian text embedding models, including state-of-the-art and community-submitted models.
How often is the leaderboard updated?
The leaderboard is updated regularly to reflect the latest advancements in Persian text embedding models and their performance metrics.