
Developed and delivered an end-to-end benchmark data workflow for the docling-project/docling-eval repository, focusing on dataset creation, iteration, visualization, and evaluation. Leveraged Python and YAML to implement robust CI/CD pipelines with GitHub Actions, enhancing automation and accelerating release cycles. Refactored the CLI for improved type safety and usability, introduced the docling_eval CLI for TEDS evaluation on DPBench, and expanded test infrastructure for comprehensive end-to-end coverage. Addressed code quality through MyPy type checking, import management, and pre-commit hooks. These efforts improved data reliability, extended evaluation capabilities, and ensured maintainable, well-documented workflows for benchmarking and data analysis tasks.
December 2024: Delivered an end-to-end benchmark data workflow, stabilized evaluation components, and strengthened automation and documentation. Key deliverables include benchmark data handling with visualization and BenchmarkNames support, CI/CD rollout, type-safety improvements and CLI refactor, new docling_eval CLI with initial TEDS evaluation on DPBench, and enhanced end-to-end testing with improved test infrastructure and pre-commit checks. These efforts improve data reliability for benchmarks, accelerate release cycles, and extend evaluation capabilities for DPBench-based workflows.
December 2024: Delivered an end-to-end benchmark data workflow, stabilized evaluation components, and strengthened automation and documentation. Key deliverables include benchmark data handling with visualization and BenchmarkNames support, CI/CD rollout, type-safety improvements and CLI refactor, new docling_eval CLI with initial TEDS evaluation on DPBench, and enhanced end-to-end testing with improved test infrastructure and pre-commit checks. These efforts improve data reliability for benchmarks, accelerate release cycles, and extend evaluation capabilities for DPBench-based workflows.

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