MINT-1T-PDF-CC-2023-50
DatasetFreeDataset by mlfoundations. 7,96,577 downloads.
Capabilities6 decomposed
multimodal pdf-to-text extraction at scale
Medium confidenceExtracts text and image content from 796K+ PDF documents sourced from Common Crawl 2023, using a structured pipeline that preserves document layout and image-text relationships. The dataset uses WebDataset format for efficient streaming access to tar-archived samples, enabling distributed training without requiring full dataset materialization. Implementation leverages MLCroissant metadata standards to expose dataset schema and provenance, making it compatible with automated data discovery and validation workflows.
Uses WebDataset tar-based streaming architecture instead of row-based formats, enabling efficient distributed training without downloading entire dataset; preserves PDF document structure and image-text spatial relationships rather than flattening to generic image-caption pairs
Larger and more diverse than LAION-5B for document-specific tasks, and preserves layout context that generic image-text datasets discard, making it superior for document intelligence vs. general vision-language training
streaming dataset access via webdataset protocol
Medium confidenceImplements efficient streaming access to 796K+ samples through WebDataset tar-archive format, allowing models to load batches directly from cloud storage without full dataset materialization. The architecture uses tar-based sharding with configurable batch sizes, enabling distributed training across multiple GPUs/TPUs by streaming different tar shards to different workers. Integration with HuggingFace Hub provides automatic caching, resumable downloads, and version management.
Uses tar-based sharding with per-worker shard assignment rather than row-level shuffling, reducing coordination overhead in distributed settings; integrates with HuggingFace Hub's resumable download and caching layer for fault tolerance
More efficient than downloading full dataset before training (saves weeks of setup time) and more scalable than row-based formats like Parquet for distributed training due to reduced metadata overhead per sample
mlcroissant metadata schema exposure
Medium confidenceExposes dataset structure, provenance, and licensing through MLCroissant metadata standard, enabling automated discovery, validation, and integration with data governance tools. The metadata includes field schemas (text vs. image), record counts, source attribution (Common Crawl 2023), and CC-BY-4.0 licensing terms. This enables downstream tools to automatically validate data compatibility, generate data cards, and enforce licensing compliance without manual inspection.
Implements MLCroissant standard for machine-readable dataset metadata, enabling automated schema validation and licensing compliance checks rather than relying on human-readable documentation alone
More structured and machine-actionable than HuggingFace dataset cards (which are markdown-based); enables programmatic validation and governance that generic dataset documentation cannot provide
common crawl pdf document sourcing and deduplication
Medium confidenceSources 796K+ PDF documents from Common Crawl 2023 snapshot using URL-based deduplication and content filtering to ensure dataset diversity. The pipeline crawls Common Crawl's WARC archives, extracts PDF URLs, filters by document type and size, and deduplicates based on URL canonicalization and optional content hashing. This ensures the dataset represents a broad cross-section of real-world PDFs rather than duplicates or spam.
Leverages Common Crawl's pre-crawled WARC archives rather than performing independent web crawling, reducing infrastructure costs and ensuring reproducibility; applies URL canonicalization and optional content hashing for deduplication at scale
More cost-effective and reproducible than independent web crawling; larger and more diverse than manually curated document datasets, though with lower average quality due to lack of human filtering
image-text spatial relationship preservation in document extraction
Medium confidencePreserves spatial layout and image-text relationships during PDF extraction, maintaining document structure rather than flattening to generic image-caption pairs. The extraction pipeline preserves page coordinates, image bounding boxes, and text positioning, enabling downstream models to learn document layout patterns. This is critical for tasks like table extraction, form understanding, and document classification where spatial relationships carry semantic meaning.
Preserves document spatial structure and image-text relationships rather than flattening to generic image-caption pairs, enabling models to learn layout-aware representations critical for document understanding tasks
Superior to generic image-text datasets (LAION, Conceptual Captions) for document-specific tasks because spatial relationships are preserved; enables training of layout-aware models that generic datasets cannot support
cc-by-4.0 licensed dataset with transparent attribution
Medium confidenceProvides dataset under CC-BY-4.0 open license with transparent source attribution to Common Crawl and original document creators. The licensing model enables commercial and research use with attribution requirements, and the dataset includes source URL metadata enabling downstream users to provide proper attribution. This transparency supports reproducible research and compliance with open licensing standards.
Provides transparent CC-BY-4.0 licensing with source URL metadata enabling proper attribution, rather than generic 'open source' claims without clear provenance tracking
More legally transparent than proprietary datasets; clearer licensing than some academic datasets that lack explicit license declarations, enabling confident commercial use
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓ML researchers training vision-language models (CLIP, LLaVA, etc.)
- ✓Teams building document intelligence systems for enterprise use
- ✓Researchers studying multimodal learning on real-world data distributions
- ✓Teams with distributed training infrastructure (multi-GPU, multi-node setups)
- ✓Researchers with limited local storage but good network bandwidth
- ✓Production ML pipelines requiring fault-tolerant data loading
- ✓ML teams with governance requirements (compliance, licensing tracking)
- ✓Researchers publishing models and needing transparent data attribution
Known Limitations
- ⚠English-only content — no multilingual document support
- ⚠Fixed to 2023 Common Crawl snapshot — no real-time updates or historical versions
- ⚠WebDataset format requires compatible loaders; not directly compatible with standard PyTorch DataLoader without adapter code
- ⚠Image quality varies by source PDF; no quality filtering or deduplication applied
- ⚠No built-in train/val/test splits — requires manual partitioning for reproducible experiments
- ⚠Sequential access pattern within tar archives — random access requires full archive decompression
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
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MINT-1T-PDF-CC-2023-50 — a dataset on HuggingFace with 7,96,577 downloads
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