bert-cls-token-for-classification

IN premise — entries/2026/06/21/wiki-BERT_language_model-chunk-1.md

Created 2026-06-21T09:50:09+00:00

BERT's [CLS] token output is used for sequence-level classification tasks; for token-level tasks, individual token outputs are used

Summary

BERT splits its outputs into two roles: the [CLS] token summarizes the whole input for tasks like sentiment analysis, while each individual token output handles per-word tasks like part-of-speech tagging. This distinction matters because using the wrong output produces meaningless results, so any system building on BERT must pick the correct signal based on whether it needs a whole-sentence answer or a word-by-word one.

Dependents

These beliefs depend on this one:

IN bert-established-pretrain-finetune-paradigm — BERT (Devlin et al., 2018) is an encoder-only Transformer pre-trained on BookCorpus and English Wikipedia using two simultaneous self-supervised objectives — masked language modeling (MLM) and next sentence prediction (NSP) — to achieve bidirectional pre-training. Its [CLS] token output serves as a sequence-level representation for classification tasks, while individual token outputs support token-level tasks, enabling a single pre-trained model to be adapted to diverse downstream tasks.