d2cml-ai/Data-Science-Python
Jun 2025 – Jun 2025
1 Month active
Languages Used
JSONPython
Technical Skills
Data AnalysisDocument ProcessingHugging Face TransformersMachine LearningNatural Language ProcessingPython
Victor-Arica
PROFILE
Victor-arica
Developed a Financial NLP Toolkit feature for the d2cml-ai/Data-Science-Python repository, enabling sentiment analysis on financial phrases, document summarization, and multilingual named entity recognition. Leveraged Hugging Face Transformers, specifically finBERT for sentiment analysis and bart-large-cnn for summarization, to process and extract insights from financial documents. Integrated PyMuPDF for robust PDF text extraction and incorporated a Spanish NER model to support multilingual entity extraction. The resulting end-to-end NLP pipeline streamlines the analysis of financial documents, reducing time to actionable insights and supporting enterprise-scale workflows. Work was implemented in Python, utilizing data analysis and document processing expertise throughout the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,766
Activity Months1
Your Network
32 peopleWork History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly deliverable: Delivered a Financial NLP Toolkit feature in d2cml-ai/Data-Science-Python that enables sentiment analysis on financial phrases using finBERT, document summarization with NER on summarized text, and multilingual entity extraction. Implemented via Hugging Face transformers (finbert sentiment, bart-large-cnn summarization), PyMuPDF-based PDF text extraction, and a Spanish NER model for entity extraction. This work reduces time to insights for financial documents and supports multilingual analysis at scale, with a clean integration path for end-to-end NLP workflows.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly deliverable: Delivered a Financial NLP Toolkit feature in d2cml-ai/Data-Science-Python that enables sentiment analysis on financial phrases using finBERT, document summarization with NER on summarized text, and multilingual entity extraction. Implemented via Hugging Face transformers (finbert sentiment, bart-large-cnn summarization), PyMuPDF-based PDF text extraction, and a Spanish NER model for entity extraction. This work reduces time to insights for financial documents and supports multilingual analysis at scale, with a clean integration path for end-to-end NLP workflows.
June 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage90.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
Data AnalysisDocument ProcessingHugging Face TransformersMachine LearningNatural Language ProcessingPython
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline