Abstract: A model training system is described that obtains a training dataset including videos and text labels. The model training system generates a video-text classification model by causing a model having a dual image text encoder architecture to predict which of the text labels describes each video in the training dataset. Predictions output by the model are compared to the training dataset to determine distillation and contrastive losses, which are used to adjust internal weights of the model during training. The internal weights of the model are then combined with internal weights of a trained image-text classification model to generate the video-text classification model. The video text-classification model is configured to generate a video or text output that classifies a video or text input.