Hugging Face

Hugging Face is used as a core research and experimentation platform for working with transformer-based models across natural language, vision, and audio domains.

In this portfolio, its usage primarily reflects academic and research-oriented work conducted as part of a Master’s thesis, rather than production platform operations.

Key Capabilities

• Transformer Model Ecosystem
  Provides state-of-the-art architectures for NLP, computer vision, and multimodal tasks.

• Pre-Trained Model Hub
  Enables rapid experimentation using a wide range of community and research-grade models.

• Dataset Abstractions
  Simplifies access to and handling of large, structured ML datasets.

• Fine-Tuning Workflows
  Supports efficient adaptation of models to domain-specific tasks.

• Experimentation & Prototyping
  Encourages fast iteration and reproducible research workflows.

Experience & Research Contribution

Used Hugging Face extensively during Master’s-level research, focusing on transformer-based approaches for domain-specific modeling tasks.

Key contributions included:

• Fine-tuning large language models for NLP and domain-adapted tasks
• Designing and evaluating transformer architectures for astronomical data analysis and text generation
• Conducting controlled experiments to compare model performance and generalization
• Managing datasets, training pipelines, and evaluation workflows
• Translating research findings into structured, reproducible results

Hugging Face served as a foundational research tool, enabling rigorous experimentation and exploration of modern transformer-based ML techniques within an academic context.