huggingface/feel
Oct 2024 – Feb 2025
4 Months active
Languages Used
Jupyter NotebookPythonJSON
Technical Skills
Deep LearningMachine LearningModel EvaluationModel TrainingNatural Language ProcessingPython
Jen Ben Arye
PROFILE
Jen Ben Arye
Jen Ben contributed to the huggingface/feel repository by developing end-to-end model training and evaluation pipelines, automating benchmarking, and enhancing dataset processing for scalable machine learning workflows. She generalized the KTO pipeline to support multiple datasets, modularized configuration using Python dataclasses, and improved data loading and formatting for reproducibility. Jen implemented context-aware prompt construction and LoRA-based training scripts, integrating Hugging Face Transformers and PyTorch to enable efficient model fine-tuning. Her work included migrating evaluation notebooks to scripts for maintainability, streamlining dataset preparation, and maintaining repository hygiene. These efforts resulted in robust, reproducible experimentation and improved data quality for model development.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
15Total
Bugs
1
Commits
15
Features
8
Lines of code
15,663
Activity Months4
Your Network
8 peopleSame Organization
@align-3.csail.mit.edu1
Work History
February 2025
6 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for huggingface/feel: Focused on delivering robust dataset processing for KTO trainer, context-aware prompts, and LoRA-based training tooling, while maintaining repository hygiene. These efforts improved data quality, model performance potential, and developer productivity, aligning with business goals of scalable, reproducible ML workflows.
6 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for huggingface/feel: Focused on delivering robust dataset processing for KTO trainer, context-aware prompts, and LoRA-based training tooling, while maintaining repository hygiene. These efforts improved data quality, model performance potential, and developer productivity, aligning with business goals of scalable, reproducible ML workflows.
February 2025
December 2024
5 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for hugingface/feel focused on expanding the KTO pipeline to support multiple datasets beyond OpenAssistant through generalization and modular configuration, and on enhancing dataset processing with Ultrafeedback integration. Delivered a unified configuration dataclass that separates script, model, and training arguments, improved data loading, dataset formatting, and logging to boost experiment reliability and reproducibility. Added Ultrafeedback Dataset Processing Enhancements to convert HuggingFaceH4/ultrafeedback_binarized into the unified KTO schema with labeled preferences, then refined to process only preference data, enforce non-empty completions, and ensure tokenizer chat-format compatibility. These changes collectively enable scalable, reproducible experiments across datasets and improve data quality for training and evaluation.
December 2024
5 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for hugingface/feel focused on expanding the KTO pipeline to support multiple datasets beyond OpenAssistant through generalization and modular configuration, and on enhancing dataset processing with Ultrafeedback integration. Delivered a unified configuration dataclass that separates script, model, and training arguments, improved data loading, dataset formatting, and logging to boost experiment reliability and reproducibility. Added Ultrafeedback Dataset Processing Enhancements to convert HuggingFaceH4/ultrafeedback_binarized into the unified KTO schema with labeled preferences, then refined to process only preference data, enforce non-empty completions, and ensure tokenizer chat-format compatibility. These changes collectively enable scalable, reproducible experiments across datasets and improve data quality for training and evaluation.
November 2024
2 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for huggingface/feel focused on strengthening the evaluation workflow and reducing maintenance debt. Delivered a Model Evaluation Automation Suite to streamline model evaluation (setup evaluation arguments, perform Bradley-Terry comparisons between model generations, and generate model responses for multiple datasets with JSON output). Migrated the Jupyter notebook kto_eval.ipynb to a Python script to improve maintainability and reproducibility (no feature-change, just file-format migration). No major bugs fixed this month. Impact includes faster, standardized benchmarking, clearer data-driven decision making, and reduced maintenance overhead. Key technologies demonstrated include Python scripting, JSON I/O, Bradley-Terry evaluation, and notebook-to-script migration.
2 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for huggingface/feel focused on strengthening the evaluation workflow and reducing maintenance debt. Delivered a Model Evaluation Automation Suite to streamline model evaluation (setup evaluation arguments, perform Bradley-Terry comparisons between model generations, and generate model responses for multiple datasets with JSON output). Migrated the Jupyter notebook kto_eval.ipynb to a Python script to improve maintainability and reproducibility (no feature-change, just file-format migration). No major bugs fixed this month. Impact includes faster, standardized benchmarking, clearer data-driven decision making, and reduced maintenance overhead. Key technologies demonstrated include Python scripting, JSON I/O, Bradley-Terry evaluation, and notebook-to-script migration.
November 2024
October 2024
2 Commits • 1 Features
Oct 1, 2024In Oct 2024, delivered end-to-end model lifecycle tooling for the huggingface/feel project, focusing on training, evaluation, and metrics persistence. The work establishes a reproducible workflow with an evaluation pipeline, supporting notebooks, and cleanup of the training setup, enabling faster experimentation and more reliable model readiness.
October 2024
2 Commits • 1 Features
Oct 1, 2024In Oct 2024, delivered end-to-end model lifecycle tooling for the huggingface/feel project, focusing on training, evaluation, and metrics persistence. The work establishes a reproducible workflow with an evaluation pipeline, supporting notebooks, and cleanup of the training setup, enabling faster experimentation and more reliable model readiness.
Activity
Loading activity data...
Quality Metrics
Correctness84.0%
Maintainability84.6%
Architecture83.4%
Performance68.6%
AI Usage21.4%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPython
Technical Skills
Code ConversionData ProcessingData ScienceDataset LoadingDataset ManagementDataset ManipulationDataset PreparationDeep LearningFile ManagementHugging Face DatasetsHugging Face TransformersLoRA (Low-Rank Adaptation)Machine LearningModel EvaluationModel Training
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline