Panda-70M is a large-scale video dataset with 70 million video clips, each annotated with high-quality text captions. The dataset is created by leveraging multiple cross-modality teacher models to generate captions for videos, followed by fine-tuning a retrieval model to select the best captions. The dataset includes high-resolution videos with rich captions, averaging 13.2 words per caption. The captions are generated using a combination of textual video descriptions, subtitles, and individual video frames. The dataset is designed to support three downstream tasks: video captioning, video and text retrieval, and text-driven video generation. The models trained on Panda-70M show significant improvements in performance across these tasks. The dataset is compared with other video-language datasets, and it is shown that the captions in Panda-70M are more precise and semantically coherent than those in existing datasets. The dataset is also shown to be more effective in generating high-quality video captions and in video and text retrieval tasks. The dataset is created by splitting 3.8 million high-resolution videos from the HD-VILA-100M dataset into semantically coherent clips and using multiple cross-modality teacher models to generate captions. The captions are then refined using a retrieval model to select the best captions. The dataset is also used to train a student model that distills knowledge from the teacher models to improve captioning performance. The student model is trained on Panda-70M and is shown to outperform individual teacher models in captioning tasks. The dataset is also used to evaluate the effectiveness of text-to-video generation models, and it is shown that pretraining on Panda-70M improves the performance of these models. The dataset is also shown to be effective in video and text retrieval tasks, with pretraining on Panda-70M leading to significant improvements in retrieval accuracy. The dataset is also shown to be effective in generating high-quality video captions, with pretraining on Panda-70M leading to significant improvements in captioning accuracy. The dataset is also shown to be effective in text-driven video generation, with pretraining on Panda-70M leading to significant improvements in video generation quality. The dataset is also shown to be effective in video and text retrieval tasks, with pretraining on Panda-70M leading to significant improvements in retrieval accuracy. The dataset is also shown to be effective in video captioning tasks, with pretraining on Panda-70M leading to significant improvements in captioning accuracy. The dataset is also shown to be effective in text-driven video generation, with pretraining on Panda-70M leading to significant improvements in video generation quality. The dataset is also shown to be effective in video and text retrieval tasks, with pretraining on Panda-70M leading to significant improvements in retrieval accuracy. The dataset is also shown to be effective in video captioning tasks, with pretraining on Panda-70M leading to significant improvements inPanda-70M is a large-scale video dataset with 70 million video clips, each annotated with high-quality text captions. The dataset is created by leveraging multiple cross-modality teacher models to generate captions for videos, followed by fine-tuning a retrieval model to select the best captions. The dataset includes high-resolution videos with rich captions, averaging 13.2 words per caption. The captions are generated using a combination of textual video descriptions, subtitles, and individual video frames. The dataset is designed to support three downstream tasks: video captioning, video and text retrieval, and text-driven video generation. The models trained on Panda-70M show significant improvements in performance across these tasks. The dataset is compared with other video-language datasets, and it is shown that the captions in Panda-70M are more precise and semantically coherent than those in existing datasets. The dataset is also shown to be more effective in generating high-quality video captions and in video and text retrieval tasks. The dataset is created by splitting 3.8 million high-resolution videos from the HD-VILA-100M dataset into semantically coherent clips and using multiple cross-modality teacher models to generate captions. The captions are then refined using a retrieval model to select the best captions. The dataset is also used to train a student model that distills knowledge from the teacher models to improve captioning performance. The student model is trained on Panda-70M and is shown to outperform individual teacher models in captioning tasks. The dataset is also used to evaluate the effectiveness of text-to-video generation models, and it is shown that pretraining on Panda-70M improves the performance of these models. The dataset is also shown to be effective in video and text retrieval tasks, with pretraining on Panda-70M leading to significant improvements in retrieval accuracy. The dataset is also shown to be effective in generating high-quality video captions, with pretraining on Panda-70M leading to significant improvements in captioning accuracy. The dataset is also shown to be effective in text-driven video generation, with pretraining on Panda-70M leading to significant improvements in video generation quality. The dataset is also shown to be effective in video and text retrieval tasks, with pretraining on Panda-70M leading to significant improvements in retrieval accuracy. The dataset is also shown to be effective in video captioning tasks, with pretraining on Panda-70M leading to significant improvements in captioning accuracy. The dataset is also shown to be effective in text-driven video generation, with pretraining on Panda-70M leading to significant improvements in video generation quality. The dataset is also shown to be effective in video and text retrieval tasks, with pretraining on Panda-70M leading to significant improvements in retrieval accuracy. The dataset is also shown to be effective in video captioning tasks, with pretraining on Panda-70M leading to significant improvements in