Evaluate evaluators in Grounded Question Answering
Organize and process datasets using AI
Search and find similar datasets
ReWrite datasets with a text instruction
Search narrators and view network connections
Display trending datasets from Hugging Face
A collection of parsers for LLM benchmark datasets
Convert PDFs to a dataset and upload to Hugging Face
Convert a model to Safetensors and open a PR
Colabora para conseguir un Carnaval de Cádiz más accesible
Convert a model to Safetensors and open a PR
Speech Corpus Creation Tool
Build datasets and workflows using AI models
Grouse is a tool designed for dataset creation, specifically focused on evaluating evaluators in Grounded Question Answering (GQA). It provides a framework to assess the effectiveness of question-answering models by analyzing their evaluators, ensuring that the evaluation methods and metrics used are reliable and grounded in real-world scenarios.
• Evaluator Analysis: Helps identify biases and inconsistencies in evaluator behavior. • Benchmarking Support: Provides tools to benchmark evaluators across different datasets and models. • Automated Insights: Generates detailed reports on evaluator performance and reliability. • Customization Options: Allows users to define custom metrics and evaluation criteria. • Integration Friendly: Works seamlessly with popular GQA frameworks and models. • Open Source: Free to use, modify, and distribute for research and development purposes.
What is the purpose of Grouse?
Grouse is designed to evaluate evaluators in Grounded Question Answering, ensuring that the evaluation process is fair, consistent, and reliable.
How does Grouse improve dataset creation?
By analyzing evaluator performance, Grouse helps identify and mitigate biases, leading to higher-quality datasets for training and testing AI models.
Can I customize the evaluation metrics in Grouse?
Yes, Grouse allows users to define custom metrics and evaluation criteria to suit their specific needs.