🐢 Open-Source Evaluation & Testing library for LLM Agents
A comprehensive Python framework for evaluating LLM-extracted structured data against ground truth labels. Supports binary classification, scalar values, and list fields with detailed performance metrics, confidence-based evaluation, and statistical uncertainty quantification via non-parametric bootstrap confidence intervals.
Evaluation & testing framework for computer vision models
Build confidence in your AI with systematic slice-based testing
A multi-agent research design council that uses Flask and Backboard AI to critique, refine, and evaluate experimental ideas.
Simple implementation of text classifier in Java with built in SVM, C4.5, kNN, and naive Bayesian classifiers. Support for common text preprocessors and for CVS format. You can plugin your own classifier, tokenizer, transformer, stopwords, synonyms, and TF-IDF formula etc. Supports automatic validation and confusion matrix.
Deepchecks AI context pack for Claude Code, Codex, Cursor, and Aider: ML model validation, data drift detection, continuous monitoring, and tracing verification.
Validation harness for FFN-style EM segmentation: seed-stability, structural split leakage, and biological red-flag checks -> a ranked proofreader review queue
Lightweight Python library for automated data leakage detection in ML datasets. Detects duplicate rows, ID overlap, target leakage, and temporal leakage.
Reusable engineering procedures for validation, security triage, stub detection, and evidence-based release decisions.
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