Can't pre-produce the result reported in paper with 'similar' dataset

[miniweeds]

I tried to test the moderation API performance with the jigsaw dataset from Kaggle. The performance is quite worse than what was reported in the paper. Why? What am I missing? Here are the parameters for my test:

• moderation API: https://api.openai.com/v1/moderations
• Date: 1/26/2023
• Label: 1 (toxic), 0 (non-toxic)
• Test dataset: 181,000 records, 0: 167344, 1: 13656 1/10 data from train.csv from this link https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-

My Test result:

• accuracy: 0.92
• f1: 0.23

• AUPRC: 0.33

  Paper (https://arxiv.org/pdf/2208.03274.pdf) reported much better AUPRC, as below: Jigsaw Identity-hate .6890 Insult .8548 Obscene .8353 Threat .6144 Toxic .9304*

[miniweeds]

Or is it because the jigsaw dataset in the paper is different from what I used? I tried the non-English jigsaw dataset too. The performance was worse.

[miniweeds]

I see the problem now. The moderation API only classifies the following categories: "hate”, “hate/threatening”, "self-harm”, "sexual”, "sexual/minors”, "violence”, “violence/graphic". The jigsaw dataset I used covers much more categories. That explains why the API got such low AUPRC, the model and the test set don't align. In this case this API is not suitable for the jigsaw type of problems.

Here are the categories in Jigsaw train dataset: severe_toxicity,obscene,identity_attack,insult,threat,asian,atheist,bisexual,black,buddhist,christian,female,heterosexual,hindu,homosexual_gay_or_lesbian,intellectual_or_learning_disability,jewish,latino,male,muslim,other_disability,other_gender,other_race_or_ethnicity,other_religion,other_sexual_orientation,physical_disability,psychiatric_or_mental_illness,transgender,white.