content.raw: 🔍 A recently published technical report introduces MINT-1T, a dataset that will considerably expand open-source multimodal data. It features one trillion text tokens and three billion images and is scheduled for release in July 2024.
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content.value: 🔍 A recently published technical report introduces MINT-1T, a dataset that will considerably expand open-source multimodal data. It features one trillion text tokens and three billion images and is scheduled for release in July 2024.
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content.raw: Researcher Affiliation: 
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content.value: Researcher Affiliation: 
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content.raw: University of Washington
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content.value: University of Washington
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content.raw: Salesforce Research
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content.value: Salesforce Research
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content.raw: Stanford University
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content.value: Stanford University
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content.raw: University of Texas at Austin
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content.value: University of Texas at Austin
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content.raw: University of California, Berkeley
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content.value: University of California, Berkeley
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content.raw: Paper:
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content.value: Paper:
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content.raw: MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
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content.value: MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
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content.href: https://arxiv.org/pdf/2406.11271v1.pdf
content.raw: https://arxiv.org/pdf/2406.11271v1.pdf
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content.raw: GitHub:
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content.value: GitHub:
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content.href: https://github.com/mlfoundations/MINT-1T
content.raw: https://github.com/mlfoundations/MINT-1T
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content.raw: Highlights:
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content.value: Highlights:
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content.raw: MINT-1T Dataset: Largest open-source multimodal interleaved dataset with 1 trillion text tokens & 3 billion images. 📊🖼️
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content.value: MINT-1T Dataset: Largest open-source multimodal interleaved dataset with 1 trillion text tokens & 3 billion images. 📊🖼️
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content.raw: Diverse Sources: Incorporates data from HTML, PDFs, and ArXiv documents. 📄📚
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content.value: Diverse Sources: Incorporates data from HTML, PDFs, and ArXiv documents. 📄📚
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content.raw: Open Source: Dataset and code will be released at 
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content.value: Open Source: Dataset and code will be released at 
content.href: https://github.com/mlfoundations/MINT-1T
content.raw: https://github.com/mlfoundations/MINT-1T
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content.raw: . 🌐🔓
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content.value: . 🌐🔓
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content.raw: Broader Domain Representation: Uses diverse data sources for balanced domain representation. 🌍📚
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content.value: Broader Domain Representation: Uses diverse data sources for balanced domain representation. 🌍📚
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content.raw: Performance in Multimodal Tasks: The dataset’s scale and diversity should enhance multimodal task performance. 🤖💡
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content.value: Performance in Multimodal Tasks: The dataset’s scale and diversity should enhance multimodal task performance. 🤖💡
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content.raw: Datasheet Information:
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content.value: Datasheet Information:
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content.raw: Motivation: Addresses the gap in large-scale open-source multimodal datasets. 🌐📊
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content.value: Motivation: Addresses the gap in large-scale open-source multimodal datasets. 🌐📊
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content.raw: Composition: 927.6 million documents, including HTML, PDF, and ArXiv sources. 📄📚
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content.value: Composition: 927.6 million documents, including HTML, PDF, and ArXiv sources. 📄📚
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content.raw: Collection Process: Gathered from CommonCrawl WARC and WAT dumps, with rigorous filtering. 🗂️🔍
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content.value: Collection Process: Gathered from CommonCrawl WARC and WAT dumps, with rigorous filtering. 🗂️🔍
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content.raw: Preprocessing/Cleaning: Removal of low-quality text, duplicates and anonymization of sensitive information. 🧹🔒
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content.value: Preprocessing/Cleaning: Removal of low-quality text, duplicates and anonymization of sensitive information. 🧹🔒
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content.raw: Ethical Considerations: Measures to ensure privacy and avoid bias. ⚖️🔏
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content.value: Ethical Considerations: Measures to ensure privacy and avoid bias. ⚖️🔏
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content.raw: Uses: Training multimodal models, generating interleaved image-text sequences, and building retrieval systems. 🤖📖
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content.value: Uses: Training multimodal models, generating interleaved image-text sequences, and building retrieval systems. 🤖📖
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rawContent: 🔍 A recently published technical report introduces MINT-1T, a dataset that will considerably expand open-source multimodal data. It features one trillion text tokens and three billion images and is scheduled for release in July 2024.
  
  Researcher Affiliation: 
  
  University of Washington
  Salesforce Research
  Stanford University
  University of Texas at Austin
  University of California, Berkeley
  
  Paper:
  MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
  https://arxiv.org/pdf/2406.11271v1.pdf
  
  GitHub:
  https://github.com/mlfoundations/MINT-1T
  
  Highlights:
  
  MINT-1T Dataset: Largest open-source multimodal interleaved dataset with 1 trillion text tokens & 3 billion images. 📊🖼️
  Diverse Sources: Incorporates data from HTML, PDFs, and ArXiv documents. 📄📚
  Open Source: Dataset and code will be released at https://github.com/mlfoundations/MINT-1T. 🌐🔓
  Broader Domain Representation: Uses diverse data sources for balanced domain representation. 🌍📚
  Performance in Multimodal Tasks: The dataset’s scale and diversity should enhance multimodal task performance. 🤖💡
  
  Datasheet Information:
  
  Motivation: Addresses the gap in large-scale open-source multimodal datasets. 🌐📊
  Composition: 927.6 million documents, including HTML, PDF, and ArXiv sources. 📄📚
  Collection Process: Gathered from CommonCrawl WARC and WAT dumps, with rigorous filtering. 🗂️🔍
  Preprocessing/Cleaning: Removal of low-quality text, duplicates and anonymization of sensitive information. 🧹🔒
  Ethical Considerations: Measures to ensure privacy and avoid bias. ⚖️🔏
  Uses: Training multimodal models, generating interleaved image-text sequences, and building retrieval systems. 🤖📖
  
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