What can MINT-1T-PDF-CC-2023-14 do?

large-scale multimodal document-image-text dataset loading, ocr-aligned image-text pair extraction from pdfs, streaming-based distributed dataset loading for multi-gpu training, mlcroissant metadata standard compliance and reproducibility, common crawl 2023-14 snapshot filtering and deduplication, variable-resolution image rendering with dpi consistency

MINT-1T-PDF-CC-2023-14

DatasetFree

Dataset by mlfoundations. 5,72,108 downloads.

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

large-scale multimodal document-image-text dataset loading

Medium confidence

Provides access to 1 trillion tokens of PDF-derived multimodal data (images + OCR text) from Common Crawl 2023-14, organized in WebDataset format for distributed streaming. Uses tar-based sharding architecture enabling efficient parallel loading across GPUs without requiring full dataset materialization on disk. Integrates with HuggingFace datasets library and MLCroissant metadata standard for reproducible, versioned access to 5.7M+ document samples.

Solves for

train vision-language models on real-world document understanding tasks at scalebuild multimodal retrieval systems using paired image-text document dataevaluate OCR and document layout understanding on diverse PDF sourcescreate synthetic training data pipelines for document classification and extraction

Best for

ML researchers training large vision-language models (CLIP, LLaVA scale)

teams building document AI systems requiring diverse real-world PDF samples

organizations needing pre-processed, deduplicated multimodal training corpora

Requires

HuggingFace datasets library (>=2.14.0)

WebDataset library (>=0.2.0) for efficient tar-based streaming

Python 3.8+

Limitations

5.7M samples may be insufficient for training models >10B parameters without augmentation

OCR quality varies by source document; no per-sample quality scores provided

WebDataset format requires sequential access patterns; random sampling requires full enumeration

What makes it unique

Combines 1T tokens of PDF-derived content from Common Crawl with WebDataset sharding for distributed streaming, enabling sub-second per-sample access without full materialization — unlike static image-text datasets (LAION, CC3M) that require download or local indexing

vs alternatives

Offers 10x larger scale than LAION-5B for document-specific content with native OCR alignment, while maintaining streaming efficiency that COCO and Flickr30K lack due to their centralized file structures

ocr-aligned image-text pair extraction from pdfs

Medium confidence

Automatically extracts and aligns image renderings of PDF pages with their corresponding OCR text output, preserving spatial relationships and document structure. Uses PDF parsing to generate page images at consistent DPI (72-300) and applies OCR engines (likely Tesseract or similar) to produce character-level text with bounding box metadata. Deduplication via content hashing removes near-duplicate pages across Common Crawl crawls.

Solves for

train models to understand document layout and spatial text positioningbuild systems that link visual regions in documents to extracted textcreate datasets for document layout analysis and reading order predictionevaluate OCR quality and correction models on real-world PDF diversity

Best for

document layout analysis researchers

teams building document understanding pipelines (form extraction, table recognition)

OCR model developers needing diverse, real-world training examples

Requires

PDF rendering library (PyPDF2, pdfplumber, or similar) for local inspection

Understanding of OCR output format and limitations

Compute for PDF-to-image conversion if processing locally (~0.5-2s per page)

Limitations

OCR accuracy varies significantly by document quality, font, and language; no per-sample confidence scores

Spatial alignment between image and text may drift for complex multi-column layouts

Scanned PDFs with poor image quality produce degraded OCR; no quality filtering applied

What makes it unique

Provides 1T-token scale OCR-image pairs with automatic deduplication across Common Crawl snapshots, using content hashing to eliminate redundant pages — most document datasets (DocVQA, RVL-CDIP) manually curate smaller, domain-specific collections without cross-crawl deduplication

vs alternatives

Scales to 5.7M documents with automated deduplication, whereas DocVQA (12K docs) and IIT-CDIP (6M pages) require manual curation or are domain-specific; offers broader diversity than academic paper datasets (arXiv, S2-ORC)

streaming-based distributed dataset loading for multi-gpu training

Medium confidence

Implements WebDataset-compatible tar-based sharding that enables efficient parallel loading across distributed training clusters without materializing the full dataset on local storage. Each shard contains ~1000 samples; workers fetch shards on-demand and decompress in-memory, with built-in support for HuggingFace Datasets streaming mode and PyTorch DataLoader integration. Supports deterministic shuffling via seed-based shard ordering for reproducible training runs.

Solves for

train large models on multi-GPU/multi-node clusters without requiring centralized NASreduce training startup time by streaming data on-demand rather than pre-downloadingenable fault-tolerant training with automatic shard re-fetching on worker failurescale training to datasets larger than any single machine's storage capacity

Best for

ML teams with distributed training infrastructure (Ray, PyTorch DDP, DeepSpeed)

organizations training models on cloud infrastructure with limited persistent storage

researchers requiring reproducible, version-controlled dataset access across runs

Requires

PyTorch 1.9+ with DataLoader support

WebDataset library (>=0.2.0)

HuggingFace datasets (>=2.14.0)

Limitations

Streaming adds ~50-200ms latency per shard fetch depending on network bandwidth

Deterministic shuffling requires knowing total shard count upfront; dynamic dataset growth not supported

WebDataset format requires sequential access within shards; random access requires full enumeration

What makes it unique

Uses tar-based WebDataset sharding with on-demand decompression and deterministic seed-based shuffling, enabling distributed training without centralized storage — most large datasets (ImageNet, COCO) require pre-download or NAS mounting, adding deployment complexity

vs alternatives

Eliminates storage bottleneck compared to LAION-5B (requires 330GB download) and provides native streaming support that static dataset formats (COCO, Flickr30K) lack; comparable to LAION's WebDataset approach but with larger scale and PDF-specific preprocessing

mlcroissant metadata standard compliance and reproducibility

Medium confidence

Publishes dataset metadata in MLCroissant format (W3C standard for machine learning datasets), enabling automated discovery, versioning, and reproducible access through standardized schema. Includes structured descriptions of splits, features, licenses, and data provenance (Common Crawl 2023-14 snapshot). Enables tools like HuggingFace Hub and Croissant parsers to automatically validate dataset integrity and generate data cards.

Solves for

ensure reproducible dataset access across research teams and timeenable automated dataset discovery and filtering by metadata (license, modality, size)generate standardized data documentation for model cards and research papersvalidate dataset integrity and track provenance through Common Crawl versions

Best for

research teams publishing models requiring reproducible dataset specifications

organizations building dataset catalogs and discovery systems

ML practitioners needing standardized metadata for compliance and auditing

Requires

MLCroissant parser library (croissant-py or similar)

Understanding of W3C Croissant schema

HuggingFace Datasets library for automated metadata loading

Limitations

MLCroissant standard is still evolving; not all dataset properties map cleanly to schema

Metadata does not include per-sample quality scores or filtering recommendations

Provenance tracking limited to Common Crawl snapshot version; no fine-grained source attribution per document

What makes it unique

Implements W3C MLCroissant standard for dataset metadata, enabling automated discovery and validation through standardized schema — most large datasets (LAION, COCO) publish metadata in ad-hoc formats (JSON, YAML) without formal schema compliance

vs alternatives

Provides machine-readable, standardized metadata that enables automated tooling and discovery, whereas LAION and other large datasets rely on unstructured documentation; comparable to Hugging Face's dataset cards but with formal W3C compliance

common crawl 2023-14 snapshot filtering and deduplication

Medium confidence

Curates and deduplicates content from Common Crawl's 2023-14 snapshot using content hashing (likely SHA-256 or similar) to remove near-duplicate PDF pages across multiple crawl cycles. Applies language detection to filter predominantly English documents and removes known low-quality sources. Preserves document source URLs and metadata for traceability.

Solves for

obtain diverse, real-world document samples without redundancy from web crawlstrain models on authentic document distributions as they appear on the webevaluate model robustness on varied document quality and formattingtrace document provenance back to original source URLs for validation

Best for

researchers building models that must generalize to real-world web documents

teams needing authentic document diversity without synthetic augmentation

organizations requiring source attribution and URL traceability

Requires

Understanding of Common Crawl structure and WARC format

Familiarity with content hashing and deduplication techniques

Access to Common Crawl 2023-14 snapshot metadata

Limitations

Deduplication may remove legitimate variations of similar documents (e.g., different versions of same form)

Language filtering is imperfect; non-English documents may remain, and English-heavy bias is introduced

Low-quality source filtering is heuristic-based; no manual review of excluded content

What makes it unique

Applies cross-crawl deduplication using content hashing to Common Crawl 2023-14 snapshot, eliminating redundant PDFs that appear in multiple crawl cycles — most web-scale datasets (LAION, C4) deduplicate within a single crawl but not across temporal snapshots

vs alternatives

Provides cleaner, deduplicated content than raw Common Crawl while maintaining web-scale diversity; more authentic than manually curated datasets (DocVQA, RVL-CDIP) but less curated than academic paper collections (arXiv, S2-ORC)

variable-resolution image rendering with dpi consistency

Medium confidence

Renders PDF pages to images at configurable DPI (72-300 range) to balance visual fidelity with storage efficiency. Uses PDF rendering engines (likely poppler or similar) to convert vector-based PDF content to raster images while preserving text and layout information. Applies consistent DPI across dataset to enable batch processing without resolution normalization.

Solves for

create training data with consistent visual quality across diverse PDF sourcesenable models to learn document understanding at realistic screen/print resolutionsbalance storage efficiency with visual fidelity for different downstream taskssupport OCR training on images with consistent rendering quality

Best for

vision-language model developers requiring consistent image quality

document understanding researchers needing realistic rendering fidelity

teams optimizing storage-to-quality tradeoffs for large-scale training

Requires

PDF rendering library (poppler, pdfplumber, PyMuPDF)

Sufficient compute for PDF-to-image conversion

Understanding of DPI tradeoffs for specific use cases

Limitations

Fixed DPI may be suboptimal for documents designed for specific resolutions (e.g., 600 DPI scans)

Vector PDF content may render differently across rendering engines; no standardization guarantee

Rendering quality depends on embedded fonts; missing fonts may degrade output

What makes it unique

Applies consistent DPI rendering across 5.7M documents from diverse PDF sources, enabling batch processing without per-sample resolution normalization — most document datasets (DocVQA, RVL-CDIP) use variable resolutions or require downstream normalization

vs alternatives

Provides consistent rendering quality that enables efficient batching, whereas raw PDF rendering varies by engine; more scalable than manual curation but less controlled than synthetic document generation

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with MINT-1T-PDF-CC-2023-14, ranked by overlap. Discovered automatically through the match graph.

Dataset26

MINT-1T-PDF-CC-2023-23

Dataset by mlfoundations. 6,33,111 downloads.

multimodal image-text pair extraction from pdf documents at scalestreaming access to large-scale multimodal samples via webdataset formatpdf-native image-text alignment extraction with layout preservation

3 shared capabilities

Dataset26

MINT-1T-PDF-CC-2024-18

Dataset by mlfoundations. 10,34,415 downloads.

large-scale multimodal document-image dataset curation and indexingdocument-image pair extraction and alignment from pdf sourcesstreaming dataset access with lazy loading and memory-efficient batching

3 shared capabilities

Dataset26

MINT-1T-PDF-CC-2023-06

Dataset by mlfoundations. 5,39,406 downloads.

large-scale multimodal document-image-text dataset curation and indexingstreaming dataset access with lazy loading and batchingimage-text pair extraction with layout-aware alignment

3 shared capabilities

Dataset23

nbchr_pdfs

Dataset by daniilakk. 3,12,297 downloads.

distributed dataset loading for parallel model traininglarge-scale pdf document collection for model training

2 shared capabilities

Dataset26

MINT-1T-PDF-CC-2023-40

Dataset by mlfoundations. 8,57,357 downloads.

multimodal document-to-text extraction at scalepaired image-text dataset construction for vision-language training

2 shared capabilities

Repository64

PaddleOCR

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

multilingual text detection and recognition via pp-ocrv5 pipelineparallel and multi-device inference orchestration

2 shared capabilities

Best For

✓ML researchers training large vision-language models (CLIP, LLaVA scale)
✓teams building document AI systems requiring diverse real-world PDF samples
✓organizations needing pre-processed, deduplicated multimodal training corpora
✓document layout analysis researchers
✓teams building document understanding pipelines (form extraction, table recognition)
✓OCR model developers needing diverse, real-world training examples
✓ML teams with distributed training infrastructure (Ray, PyTorch DDP, DeepSpeed)
✓organizations training models on cloud infrastructure with limited persistent storage

Known Limitations

⚠5.7M samples may be insufficient for training models >10B parameters without augmentation
⚠OCR quality varies by source document; no per-sample quality scores provided
⚠WebDataset format requires sequential access patterns; random sampling requires full enumeration
⚠CC-BY-4.0 license requires attribution in derivative works; commercial use requires compliance verification
⚠No built-in filtering for sensitive document types (medical, financial, PII); requires downstream curation
⚠OCR accuracy varies significantly by document quality, font, and language; no per-sample confidence scores

Requirements

HuggingFace datasets library (>=2.14.0)WebDataset library (>=0.2.0) for efficient tar-based streamingPython 3.8+Minimum 100GB free disk space for partial caching; full dataset requires ~2TBNetwork bandwidth for streaming from HuggingFace Hub or local mirrorPDF rendering library (PyPDF2, pdfplumber, or similar) for local inspectionUnderstanding of OCR output format and limitationsCompute for PDF-to-image conversion if processing locally (~0.5-2s per page)

Input / Output

Accepts: dataset identifier string (mlfoundations/MINT-1T-PDF-CC-2023-14), configuration parameters (split, streaming mode, batch size), PDF documents from Common Crawl 2023-14 snapshot, dataset configuration (split, batch_size, num_workers), seed for deterministic shuffling, MLCroissant JSON-LD metadata file, Common Crawl 2023-14 WARC records and metadata, PDF documents at variable resolutions

Produces: image tensors (variable resolution, typically 72-300 DPI), OCR text strings (UTF-8 encoded), metadata dictionaries (document source, page count, language tags), PNG/JPEG page images (variable resolution), UTF-8 OCR text strings, Metadata: document source URL, page number, language tag, batched tensors (images, text) compatible with PyTorch DataLoader, metadata dictionaries per sample, structured metadata dictionary (splits, features, license, provenance), data card HTML/markdown for documentation, deduplicated PDF documents with source URLs, metadata: document source, crawl timestamp, language tag, PNG/JPEG images at consistent DPI (72-300 range), variable dimensions depending on page size and DPI

UnfragileRank

Adoption15%(35% weight)

Quality14%(25% weight)

Ecosystem60%(20% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Dataset

6 capabilities

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About

MINT-1T-PDF-CC-2023-14 — a dataset on HuggingFace with 5,72,108 downloads

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Data Sources

huggingface

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Capabilities6 decomposed

large-scale multimodal document-image-text dataset loading

Medium confidence

Solves for

Best for

ML researchers training large vision-language models (CLIP, LLaVA scale)

teams building document AI systems requiring diverse real-world PDF samples

organizations needing pre-processed, deduplicated multimodal training corpora

Requires

HuggingFace datasets library (>=2.14.0)

WebDataset library (>=0.2.0) for efficient tar-based streaming

Python 3.8+

Limitations

5.7M samples may be insufficient for training models >10B parameters without augmentation

OCR quality varies by source document; no per-sample quality scores provided

WebDataset format requires sequential access patterns; random sampling requires full enumeration

What makes it unique

vs alternatives

ocr-aligned image-text pair extraction from pdfs

Medium confidence

Solves for

Best for

document layout analysis researchers

teams building document understanding pipelines (form extraction, table recognition)

OCR model developers needing diverse, real-world training examples

Requires

PDF rendering library (PyPDF2, pdfplumber, or similar) for local inspection

Understanding of OCR output format and limitations

Compute for PDF-to-image conversion if processing locally (~0.5-2s per page)

Limitations

OCR accuracy varies significantly by document quality, font, and language; no per-sample confidence scores

Spatial alignment between image and text may drift for complex multi-column layouts

Scanned PDFs with poor image quality produce degraded OCR; no quality filtering applied

What makes it unique

vs alternatives

streaming-based distributed dataset loading for multi-gpu training

Medium confidence

Solves for

Best for

ML teams with distributed training infrastructure (Ray, PyTorch DDP, DeepSpeed)

organizations training models on cloud infrastructure with limited persistent storage

researchers requiring reproducible, version-controlled dataset access across runs

Requires

PyTorch 1.9+ with DataLoader support

WebDataset library (>=0.2.0)

HuggingFace datasets (>=2.14.0)

Limitations

Streaming adds ~50-200ms latency per shard fetch depending on network bandwidth

Deterministic shuffling requires knowing total shard count upfront; dynamic dataset growth not supported

WebDataset format requires sequential access within shards; random access requires full enumeration

What makes it unique

vs alternatives

mlcroissant metadata standard compliance and reproducibility

Medium confidence

Solves for

Best for

research teams publishing models requiring reproducible dataset specifications

organizations building dataset catalogs and discovery systems

ML practitioners needing standardized metadata for compliance and auditing

Requires

MLCroissant parser library (croissant-py or similar)

Understanding of W3C Croissant schema

HuggingFace Datasets library for automated metadata loading

Limitations

MLCroissant standard is still evolving; not all dataset properties map cleanly to schema

Metadata does not include per-sample quality scores or filtering recommendations

Provenance tracking limited to Common Crawl snapshot version; no fine-grained source attribution per document

What makes it unique

vs alternatives

common crawl 2023-14 snapshot filtering and deduplication

Medium confidence

Solves for

Best for

researchers building models that must generalize to real-world web documents

teams needing authentic document diversity without synthetic augmentation

organizations requiring source attribution and URL traceability

Requires

Understanding of Common Crawl structure and WARC format

Familiarity with content hashing and deduplication techniques

Access to Common Crawl 2023-14 snapshot metadata

Limitations

Deduplication may remove legitimate variations of similar documents (e.g., different versions of same form)

Language filtering is imperfect; non-English documents may remain, and English-heavy bias is introduced

Low-quality source filtering is heuristic-based; no manual review of excluded content

What makes it unique

vs alternatives

variable-resolution image rendering with dpi consistency

Medium confidence

Solves for

Best for

vision-language model developers requiring consistent image quality

document understanding researchers needing realistic rendering fidelity

teams optimizing storage-to-quality tradeoffs for large-scale training

Requires

PDF rendering library (poppler, pdfplumber, PyMuPDF)

Sufficient compute for PDF-to-image conversion

Understanding of DPI tradeoffs for specific use cases

Limitations

Fixed DPI may be suboptimal for documents designed for specific resolutions (e.g., 600 DPI scans)

Vector PDF content may render differently across rendering engines; no standardization guarantee

Rendering quality depends on embedded fonts; missing fonts may degrade output

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Compare →

@vibe-agent-toolkit/rag-lancedb27Agent

LanceDB implementation of RAG interfaces for vibe-agent-toolkit

Compare →

vectra41Repository

A lightweight, file-backed vector database for Node.js and browsers with Pinecone-compatible filtering and hybrid BM25 search.

Compare →

MINT-1T-PDF-CC-2023-14

Capabilities6 decomposed

large-scale multimodal document-image-text dataset loading

ocr-aligned image-text pair extraction from pdfs

streaming-based distributed dataset loading for multi-gpu training

mlcroissant metadata standard compliance and reproducibility

common crawl 2023-14 snapshot filtering and deduplication

variable-resolution image rendering with dpi consistency

Related Artifactssharing capabilities

MINT-1T-PDF-CC-2023-23

MINT-1T-PDF-CC-2024-18

MINT-1T-PDF-CC-2023-06

nbchr_pdfs

MINT-1T-PDF-CC-2023-40

PaddleOCR

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

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Data Sources

MINT-1T-PDF-CC-2023-14

Capabilities6 decomposed

large-scale multimodal document-image-text dataset loading

ocr-aligned image-text pair extraction from pdfs

streaming-based distributed dataset loading for multi-gpu training

mlcroissant metadata standard compliance and reproducibility

common crawl 2023-14 snapshot filtering and deduplication

variable-resolution image rendering with dpi consistency

Related Artifactssharing capabilities

MINT-1T-PDF-CC-2023-23

MINT-1T-PDF-CC-2024-18

MINT-1T-PDF-CC-2023-06

nbchr_pdfs

MINT-1T-PDF-CC-2023-40

PaddleOCR

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to MINT-1T-PDF-CC-2023-14

Are you the builder of MINT-1T-PDF-CC-2023-14?

Get the weekly brief

Data Sources