The milestone improvements brought about by deep representation learning and pre-training techniques have led to large performance gains across downstream NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage high-quality visio-linguistic datasets for learning complementary information (across image and text modalities). In this paper, we introduce the Wikipedia-based Image Text (WIT) Dataset to better facilitate multimodal, multilingual learning. WIT is composed of 11 million+ unique images with over 37 million entity rich text descriptions associated with these images in Wikipedia from over 100 languages. Its size enables WIT to be used as a pretraining dataset for multimodal models, as we show when applied to downstream tasks such as image-text retrieval. WIT has four main and unique advantages. First, WIT is the largest multimodal dataset (at the time of writing). Second, it is massively multilingual (first of its kind) with coverage over 100+ languages (each of which has at least 10K examples) and provides cross-lingual texts for many images. Third, it represents a more diverse set of concepts and real world entities relative to what previous datasets cover. Lastly, as we demonstrate empirically, WIT provides a very challenging real-world test set that empirically highlights the need for learning improvements in tasks such as Retrieval and Captioning.