Explore and compare LLM models through interactive leaderboards and submissions
Simulate causal effects and determine variable control
Try the Hugging Face API through the playground
Parse bilibili bvid to aid / cid
Execute commands and visualize data
Create a detailed report from a dataset
Migrate datasets from GitHub or Kaggle to Hugging Face Hub
Analyze data using Pandas Profiling
Check system health
Display competition information and manage submissions
Analyze data to generate a comprehensive profile report
Embed and use ZeroEval for evaluation tasks
Evaluate diversity in data sets to improve fairness
The Open Japanese LLM Leaderboard is an open-source, community-driven platform designed to evaluate and compare large language models (LLMs) specifically for the Japanese language. It provides a comprehensive framework for benchmarking LLMs, allowing users to assess their performance across various tasks, datasets, and evaluation metrics. The platform aims to promote transparency and collaboration within the AI research community by enabling developers to submit their models for evaluation and share results publicly.
The Open Japanese LLM Leaderboard offers a range of features to support the evaluation and comparison of Japanese LLMs:
• Interactive Leaderboards: A dynamic interface that displays the performance of different LLMs across multiple benchmarks and tasks.
• Model Submissions: Developers can submit their own models for evaluation, fostering community participation and model improvements.
• Customizable Benchmarks: Users can filter results based on specific tasks, datasets, or evaluation metrics to focus on relevant use cases.
• Visualization Tools: Detailed charts and graphs to help users understand model performance trends over time.
• Community Forum: A space for discussions, feedback, and collaboration among researchers and developers.
What is the purpose of the Open Japanese LLM Leaderboard?
The leaderboard aims to provide a standardized platform for evaluating and comparing Japanese LLMs, fostering innovation and collaboration in the field of natural language processing.
How can I submit my model to the leaderboard?
Submission guidelines are available on the platform's documentation page. Ensure your model meets the specified requirements and follows the submission process outlined.
What criteria are used to rank models on the leaderboard?
Models are ranked based on their performance on predefined benchmarks and evaluation metrics such as BLEU, ROUGE, perplexity, and task-specific accuracy. The exact criteria may vary depending on the task or dataset selected.