MTEB Leaderboard

What is MTEB Leaderboard ?

The MTEB Leaderboard is a comprehensive platform designed for evaluating and comparing text embeddings across various models, benchmarks, and languages. It provides a standardized framework for assessing the performance of different embedding techniques, enabling researchers and developers to identify the most effective solutions for their specific use cases.

Features

• Embedding Evaluation: Compare multiple embedding models based on their performance across different datasets and languages.
• Multi-Benchmark Support: Access a diverse range of benchmarks tailored for different tasks in text analysis.
• Cross-Lingual Capabilities: Evaluate embeddings across various languages, enabling a deeper understanding of model performance in multilingual contexts.
• User-Friendly Interface: Easily navigate through the platform to select benchmarks, languages, and models for evaluation.

How to use MTEB Leaderboard ?

1. Select Benchmarks: Choose the specific benchmarks that align with your evaluation goals.
2. Choose Languages: Filter results by the languages you are interested in analyzing.
3. Generate Embeddings: For custom models, generate embeddings for the selected benchmarks and languages.
4. Upload Results: Submit your model's embeddings to the leaderboard for evaluation.
5. Review Results: Compare your model's performance with other models on the leaderboard.

Frequently Asked Questions

What benchmarks are available on the MTEB Leaderboard?
The MTEB Leaderboard supports a wide range of benchmarks tailored for specific tasks in text analysis, including but not limited to text classification, clustering, and information retrieval.

How do I interpret the scores on the leaderboard?
Scores are typically represented as performance metrics (e.g., accuracy, F1-score, or Spearman correlation) depending on the benchmark. Higher scores generally indicate better performance for the specific task.

Can I evaluate my custom model on the MTEB Leaderboard?
Yes, you can evaluate custom models by generating embeddings for the selected benchmarks and languages, and then uploading the results to the leaderboard for comparison.