Code peer review by Analytical Avengers - Githubissues

INFO523-S24 / project-final-KGCompetitors

https://info523-s24.github.io/project-final-KGCompetitors/

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Code peer review by Analytical Avengers #6

Closed tanyageorge closed 6 months ago

tanyageorge commented 6 months ago

The following is the peer review of the project code by Analytical Avengers. The team members that participated in this review are

Sunday Usman - @sundayusman
Tanya Evita George - @tanyageorge
Mellika Akbarsharifi - @mellica97
Divya Liladhar Dhole - @Divyadhole
Mohammad Ali Farmani - @mfarmani95
H M Abdul Fattah - @hmfattah
Gabriel Gedaliah Geffen - @gabegef

1. Purpose of Code

[X] Clearly defined and understood
[ ] Ambiguous or unclear

2. Code Quality

[X] Code is clean, readable, and well-organized
[ ] Adequate comments explaining complex or tricky code sections
[X] Variables, methods, and classes have meaningful names
[X] Consistent code style (indentation, naming conventions, etc.)

3. Functionality

[X] Code functions as intended and produces the expected outcome
[X] Edge cases have been considered and handled
[X] No obvious bugs or issues found

4. Testing

[X] Adequate unit tests covering key functionality
[ ] Tests are clear and can be easily understood
[X] All tests pass successfully

5. Documentation

[ ] Code documentation (e.g., docstrings, comments) is sufficient
[ ] Readme or other documentation updated if necessary

6. Best Practices

[ ] No hardcoded values; constants used appropriately
[X] Security and privacy concerns addressed (e.g., no sensitive data exposed)
[X] Code avoids repetition and promotes reusability

7. Additional Comments

Please provide feedback, areas of improvement, or other observations.

There are almost no comments and its pretty very high-level data so adding them would help understand the code better. It is also a little difficult to identify the source data and where the model fine-tuning takes place. The explanation of why specific DeBERTa models were chosen is somewhat superficial. Including more technical details about the advantages of each model size, such as computational efficiency, memory usage, or specific features of the DeBERTa architecture that benefit PII detection, could provide a more compelling justification for your choices.

8. Recommendation

[ ] Approved - ready to be merged
[X] Approved with minor issues - can be addressed later
[ ] Requires changes - provide feedback and request another review

xinformatics commented 6 months ago

The code is provided in the analysis folder which contains detailed jupyter notebooks for all the three models.

remisublette commented 6 months ago

Thanks for your feedback!