sillsdev/silnlp
Dec 2024 – Mar 2026
4 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
AWS S3ClearMLData ScienceDeep LearningHugging Face TransformersLLM
Laura Burdick
PROFILE
Laura Burdick
Worked on the sillsdev/silnlp repository, delivering five features over four months focused on natural language processing and machine translation evaluation. Developed and refactored the LLM training and inference pipeline to support multilingual deployments, improving resource utilization and security by separating credentials and enhancing maintainability. Implemented verse-level segment metrics, including m-bleu and m-chrf3 variants, to enable finer-grained translation quality evaluation. Enhanced verse segmentation robustness and introduced vref output options for compatibility with downstream workflows. Leveraged Python, AWS S3, and Hugging Face Transformers, applying algorithm design and data processing skills to improve pipeline reliability, scalability, and evaluation accuracy throughout the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
5
Lines of code
5,705
Activity Months4
Your Network
61 peopleSame Organization
@sil.org49
Shared Repositories
12
Work History
March 2026
2 Commits • 2 Features
Mar 1, 2026March 2026: Delivered two key features for sillsdev/silnlp, improving data fidelity and pipeline reliability. Key outcomes include (1) Translation Command: added Vref output option to emit original versification alongside SFM, enabling richer downstream processing; (2) Verse segmentation robustness: introduced multi-run Eflomal alignment averaging with new run/average logic, missing-file handling, and performance tracking. These changes enhance translation accuracy, verse alignment quality, and observability.
2 Commits • 2 Features
Mar 1, 2026March 2026: Delivered two key features for sillsdev/silnlp, improving data fidelity and pipeline reliability. Key outcomes include (1) Translation Command: added Vref output option to emit original versification alongside SFM, enabling richer downstream processing; (2) Verse segmentation robustness: introduced multi-run Eflomal alignment averaging with new run/average logic, missing-file handling, and performance tracking. These changes enhance translation accuracy, verse alignment quality, and observability.
March 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12. Focused on delivering Verse Segmentation and Vref Output Enhancement in sillsdev/silnlp. Key accomplishments include a robust fix for single-verse passages in verse segmentation, introduction of an option to output verses in vref format for direct feed into NLLB, and preserving the original versification before vref mapping to support future multi-versification workflows. These changes improve reliability of the NLP pipeline, enable seamless translation workflows, and reduce manual intervention in downstream processes.
December 2025
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12. Focused on delivering Verse Segmentation and Vref Output Enhancement in sillsdev/silnlp. Key accomplishments include a robust fix for single-verse passages in verse segmentation, introduction of an option to output verses in vref format for direct feed into NLLB, and preserving the original versification before vref mapping to support future multi-versification workflows. These changes improve reliability of the NLP pipeline, enable seamless translation workflows, and reduce manual intervention in downstream processes.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for sillsdev/silnlp: Implemented verse-level segment metrics in scoring, introducing m-bleu, m-chrf3, m-chrf3+, and m-chrf3++ to the default experiment scoring options, with computation performed at the verse level rather than the sentence level in alignment with recent research. The change enhances evaluation granularity for verse-level quality and informs model selection and tuning.
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for sillsdev/silnlp: Implemented verse-level segment metrics in scoring, introducing m-bleu, m-chrf3, m-chrf3+, and m-chrf3++ to the default experiment scoring options, with computation performed at the verse level rather than the sentence level in alignment with recent research. The change enhances evaluation granularity for verse-level quality and informs model selection and tuning.
October 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for sillsdev/silnlp: Delivered a major refactor of the LLM training and inference pipeline with multilingual support, improved resource utilization, and security enhancements. Implemented data preprocessing, model loading, training, and evaluation components; separated credentials; added maintainability comments. This work lays groundwork for scalable experiments and reduces pipeline friction for multilingual deployments.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for sillsdev/silnlp: Delivered a major refactor of the LLM training and inference pipeline with multilingual support, improved resource utilization, and security enhancements. Implemented data preprocessing, model loading, training, and evaluation components; separated credentials; added maintainability comments. This work lays groundwork for scalable experiments and reduces pipeline friction for multilingual deployments.
Activity
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Quality Metrics
Correctness84.0%
Maintainability80.0%
Architecture80.0%
Performance76.0%
AI Usage44.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
AWS S3ClearMLData ScienceDeep LearningHugging Face TransformersLLMMachine LearningMachine Translation EvaluationNatural Language ProcessingPythonPython DevelopmentPython programmingUnslothalgorithm designback end development
Repositories Contributed To
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