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