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Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
The main features of argilla-io/distilabel are: Data Generation Frameworks, Data Processing, Data Processing Tools, Feature Engineering.
Projects with overlapping indexed features include: jpmens/jo — Jo is a command-line utility designed to construct and manipulate JSON objects and arrays directly from shell… catchthetornado/pdf-extract-api. 599yongyang/datasetloom. allenai/olmocr — Olmocr is a distributed document processing framework designed to convert PDF and image files into structured… bytedance/dolphin — Dolphin is a multimodal layout analyzer and image-to-structure converter that transforms photographed or digital… bespokelabsai/curator.
Jo is a command-line utility designed to construct and manipulate JSON objects and arrays directly from shell arguments and standard input. It functions as a data processing tool that transforms raw input into structured formats, enabling the generation of complex payloads for APIs, configuration files, and automated data pipelines. The tool distinguishes itself through its ability to resolve hierarchical data structures using delimiter-based path definitions and its integrated type-inference engine, which automatically casts input values into native boolean, numeric, or null types. Users can
Olmocr is a distributed document processing framework designed to convert PDF and image files into structured markdown. It functions as a vision-based document parser that utilizes multimodal neural networks to interpret complex visual layouts and translate them into standardized text representations. The system operates as a remote inference orchestrator, offloading heavy document analysis tasks to external servers or cloud APIs to minimize local computational requirements. By employing a stateless worker architecture, it decouples document ingestion from inference, allowing for the distribu
