Analyze model errors with interactive pages
Export Hugging Face models to ONNX
Visualize model performance on function calling tasks
Compare code model performance on benchmarks
Convert Hugging Face model repo to Safetensors
Evaluate and submit AI model results for Frugal AI Challenge
Display leaderboard of language model evaluations
Display and submit LLM benchmarks
Display LLM benchmark leaderboard and info
Compare and rank LLMs using benchmark scores
Retrain models for new data at edge devices
Evaluate code generation with diverse feedback types
Benchmark AI models by comparison
ExplaiNER is a cutting-edge tool designed for model benchmarking and error analysis. It provides an interactive environment to help users identify and understand model errors through detailed, user-friendly pages. Whether you're refining your model's performance or comparing different AI solutions, ExplaiNER offers the insights you need to make data-driven decisions.
What models does ExplaiNER support?
ExplaiNER supports a wide range of models, including popular frameworks like TensorFlow, PyTorch, and Scikit-learn.
Can I compare multiple models at once?
Yes, ExplaiNER allows you to upload and compare multiple models simultaneously, making it easy to identify the best-performing solution for your needs.
How do I access historical benchmarking data?
Historical data is stored automatically in ExplaiNER. You can retrieve it by navigating to the "Reports" section and selecting the desired date or model configuration.