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.

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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

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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

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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

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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

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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

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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

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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.

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License

Unless otherwise specified, student projects are released for academic and research purposes only.

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Contact

Course organization maintained by:

Dr. Oana Ignat
Assistant Professor
Computer Science and Engineering
Santa Clara University