Table 4: Zero-shot text classiﬁcation results. “Majority” always predicts the majority class label.
Zero-shot BERTbase uses the average pooling of the last layer as text embeddings.

Majority BERTbase

SST-2 [52]

50.9

58.9

Zero-shot
E5small
79.7

E5base
81.3

E5large
85.3

Full Fine-tune

BERTbase BERTlarge

93.5

94.9

Table 4 shows the zero-shot text classiﬁcation results on the dev set of the SST-2 dataset [52]. By
formulating text classiﬁcation as embedding matching between input and label texts, our model can
be much better than the “majority” baseline in a zero-shot setting. We use the prompt template from
Section 4.3.

5.5 Analysis
E5