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srijanratrey/distilbert-jigsaw-multilabel

sourceHugging Facemitupdated 2mo agoView on Hugging Face

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DistilBERT — multi-label content / policy classification

Fine-tuned distilbert-base-uncased predicting all applicable labels for a comment: toxic, severe_toxic, obscene, threat, insult, identity_hate.

Six independent sigmoid outputs trained with BCEWithLogitsLoss — not softmax — so any combination of labels can fire and the probabilities do not sum to 1.

Metrics (held-out test split, n=23,936)

Macro-F1: 0.6836 · micro-F1: 0.7875 · macro average precision: 0.7359

label	threshold	precision	recall	F1	AP	support
`toxic`	0.55	0.847	0.832	0.839	0.921	2,295
`severe_toxic`	0.25	0.441	0.697	0.540	0.573	241
`obscene`	0.65	0.832	0.826	0.829	0.910	1,268
`threat`	0.20	0.456	0.662	0.540	0.538	71
`insult`	0.50	0.733	0.787	0.759	0.832	1,181
`identity_hate`	0.50	0.667	0.535	0.594	0.641	213

Why not accuracy

A model that predicts nothing scores 89.83% exact-match accuracy and 96.33% mean per-label accuracy on this data, at macro-F1 of exactly 0.0. Macro-F1 is the headline for that reason.

Thresholds

The per-label thresholds in the table were calibrated on the validation split and applied unchanged to test. thresholds_transformer.json in this repo holds them. Using them instead of a flat 0.5 is worth +0.021 macro-F1 with no retraining.

Limitations

—Trained on 2018 English Wikipedia talk-page comments; other domains will degrade.
—Rare labels are weak: F1 ~0.54 on threat and severe_toxic; identity_hate recall 0.535.
—threat's threshold rests on only 72 validation positives, so it is a noisy estimate.
—Inputs truncated at 256 tokens (~6.9% of training comments were longer).
—A demo, not a moderation system. Do not make consequential decisions about people with it.

Code, full write-up, and a live demo: https://github.com/Srijan-Ratrey/Multilabel-content-classification