JohnSnowLabs/spark-nlp-workshop
Nov 2024 – Oct 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisJupyter NotebooksMachine LearningNatural Language ProcessingPythonSpark NLP
David Cecchini
PROFILE
David Cecchini
Contributed to the JohnSnowLabs/spark-nlp-workshop repository by developing advanced NLP pipelines and comprehensive training materials focused on legal and healthcare domains. Delivered features such as temporal context-aware entity extraction, negation handling, and RoBERTa-based assertion status detection for legal document analysis, leveraging Python, Spark NLP, and Jupyter Notebooks. Created hands-on workshop assets covering generative AI, ASR, and clinical de-identification, standardizing onboarding and certification processes with ready-to-use notebooks and slide decks. Emphasized reproducibility and asset availability, resolving workflow issues to ensure reliable training delivery. The work demonstrated depth in machine learning, audio processing, and large language model integration for practical applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
10Total
Bugs
0
Commits
10
Features
6
Lines of code
53,703
Activity Months3
Your Network
15 peopleShared Repositories
15
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Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025: Delivered content-focused training assets for Building Patient Journeys and Cohorts in JohnSnowLabs/spark-nlp-workshop. Focused on onboarding and certification readiness with no code changes, enabling scalable, self-service learning for teams. The work emphasizes documentation, asset packaging, and process improvements that support faster ramp-up and consistent training delivery.
2 Commits • 1 Features
Oct 1, 2025October 2025: Delivered content-focused training assets for Building Patient Journeys and Cohorts in JohnSnowLabs/spark-nlp-workshop. Focused on onboarding and certification readiness with no code changes, enabling scalable, self-service learning for teams. The work emphasizes documentation, asset packaging, and process improvements that support faster ramp-up and consistent training delivery.
October 2025
April 2025
6 Commits • 4 Features
Apr 1, 2025April 2025 monthly summary: Delivered comprehensive Spark NLP workshop materials across four focus areas (generative AI, ASR, medical language modeling, and Portuguese clinical de-identification). These efforts improve hands-on learning, standardize content, and accelerate adoption by providing ready-to-use notebooks, pre-trained models, and slide decks. Resolved asset availability issues to ensure uninterrupted training and reproducibility across sessions.
April 2025
6 Commits • 4 Features
Apr 1, 2025April 2025 monthly summary: Delivered comprehensive Spark NLP workshop materials across four focus areas (generative AI, ASR, medical language modeling, and Portuguese clinical de-identification). These efforts improve hands-on learning, standardize content, and accelerate adoption by providing ready-to-use notebooks, pre-trained models, and slide decks. Resolved asset availability issues to ensure uninterrupted training and reproducibility across sessions.
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for JohnSnowLabs/spark-nlp-workshop: Delivered an enhanced Legal NLP pipeline with temporal context-aware entity extraction, negation handling, and improved extraction of document signing times. Integrated RoBERTa embeddings for assertion status detection, enabling precise extraction and classification of elements with temporal or certainty status. Stabilized notebook workflows and models through targeted fixes to FinLeg notebooks and the RoBERTa integration, improving reliability and performance.
2 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for JohnSnowLabs/spark-nlp-workshop: Delivered an enhanced Legal NLP pipeline with temporal context-aware entity extraction, negation handling, and improved extraction of document signing times. Integrated RoBERTa embeddings for assertion status detection, enabling precise extraction and classification of elements with temporal or certainty status. Stabilized notebook workflows and models through targeted fixes to FinLeg notebooks and the RoBERTa integration, improving reliability and performance.
November 2024
Activity
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Quality Metrics
Correctness99.0%
Maintainability98.0%
Architecture98.0%
Performance92.0%
AI Usage26.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
ASRAudio ProcessingAutoGGUFModelData AnalysisDe-identificationHealthcare NLPJupyter NotebooksLarge Language ModelsMachine LearningNatural Language ProcessingNatural Language Processing (NLP)PythonQuestion AnsweringSpark NLPSpeech-to-Text
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