metadata

license: mit
language:
  - en
tags:
  - ranking
  - fashion
size_categories:
  - n<1K
configs:
  - config_name: benchmark
    data_files:
      - split: test
        path: dataset.csv

outfitter-advice

On the OUTFITS subreddit, users frequently post multiple images and ask commenters to pick one (with a numeric comment like "2")

This evaluation collects the top two comments and their relative scores (for example, if there is overwhelming support for one option over another, your evaluation should score differently from a close match).

The first and second images in the "images" field are the best and second-best images. We shuffle them before evaluating.
If there is overwhelming support for the top choice, the second-best will be arbitrary and given 1 vote.

Standards and Practices

Rules of the Road

This should only evaluate posts with Male, Female, or Nonbinary Advice flair (no unsolicited advice)
Remove posts from the dataset if the image, user, or post is deleted
Do not post or comment on the subreddit
Do not upload the images separately from Reddit

Technical Details

Only pick posts which select between multiple outfits (not freeform advice, shoes, etc)
Limited string replace support for first, second, third, last, etc.
A top comment which mentions multiple outfits is not parseable, so the post is not parseable
This assumes that a comment supports the outfit which they mentioned
This assumes that the user posted one image per outfit (i.e. outfit 3 = third image)
If there is overwhelming support for the top choice, the second choice will be arbitrary and given 1 vote.

Code

Python script preparing /r/OUTFITS posts for dataset: scraper.py

Python script testing OpenAI's o4-mini model:

Evaluation Scores

Currently scoring by:

1 point for best choice
secondChoice/firstChoiceVotes for second-best choice
0 points for other options

OpenAI's o4-mini: 8.875 / 12

With more options we should be able to break down scores by M/F/NB flair.

Dependencies

For adding to evaluation set:
Requires a Reddit app_id, secret, and user_agent saved in .env in JSON format. See https://praw.readthedocs.io/en/stable/getting_started/quick_start.html

praw

For evaluating with OpenAI:

openai