Yadget

Kagura AI is an AI-powered testing tool designed for modern development teams. It generates, executes, and evolves QA tests automatically from a URL and a description of what to test. Kagura AI adapts to UI changes, allowing users to pause mid-test to interact, eliminating scripting and maintenance.

Shapash is a Python library that enhances machine learning interpretability. It offers visualizations with clear labels, making it easier to understand model features and interactions. Shapash simplifies the comprehension of machine learning models for users of all levels. It generates a web application for seamless navigation between local features.

transformers-interpret is a model explainability tool designed for use with the 🤗 transformers package. It allows users to explain their transformer models in just two lines of code. The tool provides explainers for various model types, enhancing transparency and understanding of model predictions. It aids in debugging and improving model performance.

TransformerLens is a library for mechanistic interpretability of GPT-style language models. It allows researchers and engineers to analyze the internal workings of transformer models. The tool supports in-depth investigation of model behavior.

Lm-evaluation-harness is a framework designed for few-shot evaluation of language models. It allows researchers and engineers to assess model performance across various tasks. The tool supports CLI refactoring with subcommands and YAML config files. It also offers lighter installation options with separate model backends.

uptrain is an open-source platform for evaluating and improving Generative AI applications. It provides grades for 20+ preconfigured checks, covering language, code, and embedding use-cases. The tool performs root cause analysis on failure cases and gives insights on how to resolve them. It features a web-based dashboard.