ExplainaBoard
Model Comparer
An interactive tool to analyze and compare the performance of natural language processing models
Interpretable Evaluation for AI Systems
362 stars
11 watching
36 forks
Language: Python
last commit: over 3 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
| neulab/compare-mt | A tool for comparing the performance of different language generation systems. | 467 |
 maluuba/nlg-eval | A toolset for evaluating and comparing natural language generation models | 1,350 |
 interpretml/dice | Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. | 1,373 |
 modeloriented/dalex | A tool to help understand and explain the behavior of complex machine learning models | 1,390 |
 pair-code/what-if-tool | An interactive tool for exploring and understanding the behavior of machine learning models | 928 |
 blobcity/autoai | A Python-based framework for automating the process of finding and training the best-performing machine learning model for regression and classification tasks on numerical data. | 176 |
 tensorflow/model-analysis | Evaluates and visualizes the performance of machine learning models. | 1,258 |
 blent-ai/alepython | An ALE plot generation tool for explaining machine learning model predictions | 160 |
 johnsnowlabs/langtest | A tool for testing and evaluating large language models with a focus on AI safety and model assessment. | 506 |
 openai/simple-evals | Evaluates language models using standardized benchmarks and prompting techniques. | 2,059 |
 marcelrobeer/explabox | An exploratory tool for analyzing and understanding machine learning models | 14 |
 pbiecek/xaiaterum2020 | An R package and workshop materials for explaining machine learning models using explainable AI techniques | 52 |
| marcelrobeer/contrastiveexplanation | Provides explanations for why an instance has a certain outcome by contrasting it with what would have happened if the outcome had been different. | 45 |
 thomasp85/lime | An R package for providing explanations of predictions made by black box classifiers. | 486 |
 giuseppec/iml | Provides methods to interpret and explain the behavior of machine learning models | 494 |