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Natural language supervision in the form of image captions was recently shown to be an effective way of training zero-shot image classification models. In this work, we focus on teasing out what parts of the language supervision are essential for training zero-shot models. Through extensive and careful experiments, we show that replacing intact captions with Bag-of-Words (BoW) does not significantly degrade the zero-shot performance. Surprisingly, we can even slightly improve the performance on some datasets by balancing the frequency of words in BoW.