Masterskaya-YP/Masterskaya
Mar 2025 – Apr 2025
2 Months active
Languages Used
JSONPythonJupyter Notebook
Technical Skills
Abstractive SummarizationBART ModelData CleaningData LoadingData ParsingData Processing
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PROFILE
Agsave
Over a two-month period, contributed to the Masterskaya-YP/Masterskaya repository by developing modular pipelines for chat data ingestion, preprocessing, and abstractive summarization. Built a system in Python and Jupyter Notebook that loads, parses, and cleans JSON chat data, structuring it into pandas DataFrames for analysis. Implemented chunked summarization using T5 and BART models, later expanding support to Russian-language models such as rut5 and rugpt3. Enhanced the pipeline’s flexibility by refactoring for modularity and adding cosine similarity evaluation for model comparison. Produced detailed reports to support reproducibility, enabling analytics-ready data and scalable, automated text summarization workflows for chat histories.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
3
Lines of code
114,217
Activity Months2
Your Network
6 peopleShared Repositories
6
Work History
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Masterskaya project. Delivered Russian-language text summarization capabilities by integrating and evaluating models rut5, rugpt3large_based_on_gpt2, and rut5_base_sum_gazeta. Refactored the summarization pipeline to accept a rugpt3 usage prefix, added a new summarization function, and incorporated cosine similarity as an evaluation metric to enable robust model comparison. Compiled an abstractive summarization task report and prepared the suite for reproducibility and production evaluation.
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Masterskaya project. Delivered Russian-language text summarization capabilities by integrating and evaluating models rut5, rugpt3large_based_on_gpt2, and rut5_base_sum_gazeta. Refactored the summarization pipeline to accept a rugpt3 usage prefix, added a new summarization function, and incorporated cosine similarity as an evaluation metric to enable robust model comparison. Compiled an abstractive summarization task report and prepared the suite for reproducibility and production evaluation.
April 2025
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025: Delivered two core pipelines for Masterskaya: a modular chat data ingestion and preprocessing pipeline and an abstractive summarization pipeline for chat data. The ingestion pipeline loads and parses JSON chat data, cleans text (removing emojis, newline characters, and extra spaces), and structures results into a pandas DataFrame for analysis. It was refactored into separate loading and parsing components, with an MVP wired to parse the example data file (data/example/result.json). The abstractive summarization pipeline processes chat data in chunks, producing summaries via T5 and BART; evaluation indicated BART provides higher-quality results. These efforts enable analytics-ready data and automated summaries, accelerating insight generation from chat histories and establishing a scalable foundation for future data workflows.
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025: Delivered two core pipelines for Masterskaya: a modular chat data ingestion and preprocessing pipeline and an abstractive summarization pipeline for chat data. The ingestion pipeline loads and parses JSON chat data, cleans text (removing emojis, newline characters, and extra spaces), and structures results into a pandas DataFrame for analysis. It was refactored into separate loading and parsing components, with an MVP wired to parse the example data file (data/example/result.json). The abstractive summarization pipeline processes chat data in chunks, producing summaries via T5 and BART; evaluation indicated BART provides higher-quality results. These efforts enable analytics-ready data and automated summaries, accelerating insight generation from chat histories and establishing a scalable foundation for future data workflows.
Activity
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Quality Metrics
Correctness82.8%
Maintainability82.8%
Architecture80.0%
Performance65.6%
AI Usage45.8%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPython
Technical Skills
Abstractive SummarizationBART ModelData AnalysisData CleaningData LoadingData ParsingData ProcessingFile HandlingJSON ParsingJupyter NotebookMachine LearningNatural Language ProcessingNatural Language Processing (NLP)PandasPython
Repositories Contributed To
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