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# 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