CSEN 346 – Natural Language Processing (Santa Clara University)
Welcome to the official Hugging Face organization for CSEN 346: Natural Language Processing at Santa Clara University.
This space hosts the final course projects developed by students, including:
- NLP/AI models
- Datasets
- Research papers
- Evaluation benchmarks
- Demo applications
The goal is to encourage open, reproducible, and responsible NLP research while giving students experience contributing to the research community.
Course Information
Course: CSEN 346 – Natural Language Processing
Institution: Santa Clara University
Instructor: Prof. Oana Ignat
Focus areas:
- Large Language Models
- Generative AI
- Multimodal
- Mutliagents
- Multilingual
- Responsible and Inclusive AI
- Human-centered Evaluation
- Applied NLP systems
Project Goals
Student projects aim to:
- Apply NLP methods to real-world problems
- Build reproducible research
- Practice research communication
- Contribute open resources (models/datasets)
- Develop responsible AI systems
Projects typically include:
- Problem motivation
- Dataset description
- Model architecture
- Training setup
- Evaluation methodology
- Error analysis
- Future work
Repository Structure
Each project should include:
Required
- Project README
- Research paper (ACL-style)
- Model or dataset
- Evaluation results
Recommended
- Training scripts
- Inference scripts
- Model card
- Dataset card
- Demo notebook
- Error analysis
Documentation Standards
Projects should clearly describe:
Task
- What problem does the project solve?
Data
- Source
- Size
- Languages
- Limitations
Model
- Architecture
- Parameters
- Training setup
Evaluation
- Metrics
- Baselines
- Results
Ethical Considerations
- Bias risks
- Limitations
- Appropriate use
Responsible AI Guidelines
All projects must follow responsible AI practices:
- No private or sensitive data
- No harmful applications
- Clear documentation of limitations
- Proper dataset citations
- Transparency in evaluation
Academic Integrity
All work must follow SCU academic integrity policies.
Students must:
- Cite all external resources
- Clearly indicate use of AI models
- Document prompting or fine-tuning approaches
- Clearly state team member contributions
Acknowledgment
This course emphasizes that:
Research is most valuable when it is shared, reproducible, and benefits others.
By publishing projects here, students contribute to the broader NLP community while building their research portfolio.
License
Unless otherwise specified, student projects are released for academic and research purposes only.
Contact
Course organization maintained by:
Dr. Oana Ignat
Assistant Professor
Computer Science and Engineering
Santa Clara University