Open-Finance-Lab/FinLLM-Leaderboard
Feb 2025 – Mar 2025
2 Months active
Languages Used
CSVMarkdownPython
Technical Skills
Data AnalysisDeep LearningDocumentationMachine LearningModel EvaluationNatural Language Processing
Minna Huang
PROFILE
Minna Huang
During two months on the Open-Finance-Lab/FinLLM-Leaderboard repository, Huang delivered end-to-end sentiment analysis features for financial news, expanding model evaluation coverage and enhancing documentation for reproducibility. Using Python and CSV, Huang implemented sentiment scoring pipelines leveraging models like flare_fpb and flare_fiqasa, and extended evaluation metrics for Xuanyuan and CodeLlama-7B across diverse datasets. The work included detailed performance tracking, clear documentation of evaluation workflows, and updates to benchmarking data for DeepSeek-R1-Distill-Qwen-1.5B. Huang’s contributions focused on enabling reproducible research, improving model comparison, and providing actionable metrics, demonstrating depth in data analysis, model evaluation, and natural language processing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
5
Lines of code
15,922
Activity Months2
Your Network
10 people
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Open-Finance-Lab/FinLLM-Leaderboard: Delivered concrete evaluation updates for the DeepSeek-R1-Distill-Qwen-1.5B workflow, enabling precise performance visibility and reproducible benchmarks. The work strengthens decision-making for model improvements and accelerates downstream research and productization by providing ready-to-use metrics and clear statuses for evaluation datasets.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Open-Finance-Lab/FinLLM-Leaderboard: Delivered concrete evaluation updates for the DeepSeek-R1-Distill-Qwen-1.5B workflow, enabling precise performance visibility and reproducible benchmarks. The work strengthens decision-making for model improvements and accelerates downstream research and productization by providing ready-to-use metrics and clear statuses for evaluation datasets.
March 2025
February 2025
8 Commits • 4 Features
Feb 1, 2025February 2025 (Open-Finance-Lab/FinLLM-Leaderboard) delivered multi-faceted advances in end-to-end sentiment analysis, expanded model evaluation coverage, and enhanced documentation. Key features were implemented with a strong emphasis on business value and reproducibility, supported by structured commit activity that improved model orchestration, evaluation pipelines, and guidance for future work.
February 2025
8 Commits • 4 Features
Feb 1, 2025February 2025 (Open-Finance-Lab/FinLLM-Leaderboard) delivered multi-faceted advances in end-to-end sentiment analysis, expanded model evaluation coverage, and enhanced documentation. Key features were implemented with a strong emphasis on business value and reproducibility, supported by structured commit activity that improved model orchestration, evaluation pipelines, and guidance for future work.
Activity
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Quality Metrics
Correctness88.8%
Maintainability91.2%
Architecture84.6%
Performance80.0%
AI Usage42.2%
Skills & Technologies
Programming Languages
CSVMarkdownPython
Technical Skills
Data AnalysisDeep LearningDocumentationEvaluationMachine LearningModel EvaluationNatural Language ProcessingPerformance EvaluationPythonSentiment Analysis
Repositories Contributed To
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