Awesome GitHub RepositoriesExtraction Model Evaluation

ExplainShell is a shell command explainer and syntax analyzer that matches command line arguments to manual page documentation. It functions as a man page parser and documentation extraction tool, converting roff-formatted manual pages into a structured database of command options and metadata. The project uses a combination of large language models and roff-macro parsing to identify specific line ranges that define flags and arguments. It employs a command syntax analyzer to deconstruct shell commands into tokens, which are then mapped against documented entries to provide plain language exp

PDF-Extract-Kit is a document extraction toolkit designed to convert PDF documents into structured formats such as Markdown, HTML, and LaTeX. It functions as a multi-stage parsing framework that combines a document layout analyzer, a formula recognition engine, an OCR text extractor, and a table extraction system. The project focuses on recovering complex document elements by translating images of mathematical formulas and tabular structures into editable source code. It utilizes model-driven layout analysis to identify structural elements in reports and textbooks while ignoring noise like wa

The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating, defending, and certifying machine learning models against adversarial threats. It wraps models from any framework behind a common estimator interface, enabling composable pipelines for attack generation, defense application, robustness certification, and privacy auditing across evasion, poisoning, and extraction threats. The library distinguishes itself by covering the full adversarial ML security lifecycle within a single toolkit. It supports gradient-based adversarial example