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MINT 1T
Overview :
MINT-1T is a multimodal dataset open-sourced by Salesforce AI, containing one trillion text tokens and 3.4 billion images, making it ten times larger than existing open-source datasets. It includes not only HTML documents but also PDF documents and ArXiv papers, enriching the dataset's diversity. The construction of MINT-1T involves multiple data collection, processing, and filtering steps to ensure high quality and diversity of the data.
Target Users :
The MINT-1T dataset is designed for researchers and developers in the field of artificial intelligence, particularly for training and research in multimodal learning and deep learning models. Its large scale and high-quality data provide a rich resource for training models, enhancing their performance in image and text processing tasks.
Use Cases
The XGen-MM multimodal model pre-trained on MINT-1T performs exceptionally well in image captioning and visual question answering tasks.
On the Multidisciplinary Multimodal Understanding and Reasoning benchmark (MMMU), MINT-1T shows significantly better performance in the science and technology domains compared to other datasets.
Under the Idefics2 architecture, MINT-1T exhibits outstanding performance in image captioning and visual question answering tasks.
Features
Large scale: The dataset consists of one trillion tokens, ten times larger than existing datasets.
Diversity: Includes various document types such as HTML, PDF, and ArXiv papers.
High quality: Ensured through rigorous data filtering and deduplication processes.
Cross-modal reasoning: Capable of training large multimodal models for reasoning across images and text.
Broad domain coverage: Documents span multiple fields including science, technology, and humanities.
Strong contextual learning performance: Demonstrates superior learning capabilities across varying example sizes.
Excellent multi-task performance: Outstanding results in tasks such as image captioning and visual question answering.
How to Use
1. Visit the open-source page of the MINT-1T dataset to learn about its basic information and features.
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2. Download the dataset, selecting the appropriate subset based on your research or development needs.
3. Use the dataset for pre-training or fine-tuning your model to adapt to specific multimodal tasks.
4. Test your model's performance on tasks such as image captioning and visual question answering.
5. Analyze the model's performance across different domains and tasks, optimizing its structure and parameters.
6. Explore the dataset's potential and application scope based on your experimental results.
7. Publish your research findings and share your experiences and discoveries using the MINT-1T dataset.
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