What can MINT-1T-PDF-CC-2023-40 do?

multimodal document-to-text extraction at scale, paired image-text dataset construction for vision-language training, large-scale text corpus for language model pretraining, document-domain dataset sampling and filtering, document structure and layout preservation in extraction, common crawl pdf snapshot integration and versioning

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

DatasetFree

Dataset by mlfoundations. 8,57,357 downloads.

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

multimodal document-to-text extraction at scale

Medium confidence

Extracts text content from 1 trillion tokens of PDF documents using OCR and layout-aware parsing, preserving document structure and spatial relationships. The dataset combines Common Crawl PDF snapshots with machine-readable text extraction, enabling training of models that understand both visual layout and semantic content. Architecture uses distributed PDF processing pipelines to handle heterogeneous document formats (scanned PDFs, native PDFs, mixed content) across 857K+ document samples.

Solves for

Train vision-language models that understand document structure and layoutBuild OCR systems that preserve formatting and spatial relationshipsCreate datasets for document understanding and information extraction tasksDevelop models that can reason about both text content and visual presentation

Best for

ML researchers training document understanding models

Teams building enterprise document processing pipelines

Researchers working on multimodal vision-language models

Requires

Hugging Face Datasets library (datasets>=2.0)

Minimum 500GB storage for partial dataset access

Python 3.8+

Limitations

Dataset is 100B-1T tokens in size — requires significant storage (terabyte-scale infrastructure) and computational resources for full training

PDF quality varies across Common Crawl sources — some documents may have poor OCR quality or corrupted metadata

English-language focused — limited multilingual coverage despite global web crawl

What makes it unique

Combines 1 trillion tokens of Common Crawl PDFs with layout-aware extraction preserving spatial document structure, unlike generic text corpora that discard formatting. Uses distributed PDF parsing to handle heterogeneous document types (scanned, native, mixed) at web scale rather than curated document collections.

vs alternatives

Larger and more diverse than academic document datasets (e.g., DocVQA, RVL-CDIP) while maintaining layout information that generic text corpora like C4 or The Pile discard entirely.

paired image-text dataset construction for vision-language training

Medium confidence

Provides structured image-text pairs extracted from PDF documents where images are document pages and text is extracted content, enabling direct training of vision-language models without manual annotation. The dataset architecture preserves the natural alignment between visual document layout and corresponding text, creating implicit supervision signals. Processing pipeline handles page segmentation, text-image alignment, and quality filtering across millions of document samples.

Solves for

Train vision-language models on document understanding without manual annotationCreate aligned image-text datasets for contrastive learning (CLIP-style training)Build models that can answer questions about document content based on visual inputDevelop document classification and retrieval systems using multimodal embeddings

Best for

ML teams training CLIP-style vision-language models

Researchers building document question-answering systems

Organizations developing multimodal retrieval systems

Requires

Hugging Face Datasets library with streaming support

Image processing libraries (PIL, OpenCV)

Python 3.8+

Limitations

Implicit alignment between images and text may be noisy — some documents have complex layouts where text-image correspondence is ambiguous

Page-level granularity may be too coarse for fine-grained visual reasoning tasks requiring sub-document element understanding

No explicit quality scores for image-text pairs — requires downstream filtering for high-quality training data

What makes it unique

Leverages natural document structure to create implicit image-text alignment without manual annotation, using page-level visual-semantic correspondence from PDFs. Unlike manually-annotated datasets (Flickr30K, COCO), derives pairs automatically from document layout, enabling trillion-token scale.

vs alternatives

Provides orders of magnitude more image-text pairs than manually-curated datasets while maintaining document-specific semantic alignment that generic web image-text pairs (Laion) lack.

large-scale text corpus for language model pretraining

Medium confidence

Supplies 1 trillion tokens of English text extracted from PDF documents, suitable for pretraining or continued training of large language models. The corpus is derived from diverse document sources across Common Crawl, providing varied writing styles, domains, and content types. Processing pipeline includes tokenization, deduplication, and quality filtering to ensure training data suitability while maintaining scale.

Solves for

Pretrain or continue-train large language models with document-sourced textCreate domain-specific language models by fine-tuning on document corporaBuild specialized models for document understanding and analysis tasksAugment existing pretraining datasets with document-specific content

Best for

ML researchers training foundation models with document-heavy content

Teams building domain-specific language models (legal, scientific, technical)

Organizations needing large-scale English text corpora for model training

Requires

Hugging Face Datasets library

Tokenizer compatible with target model (e.g., GPT-2, LLaMA tokenizers)

Python 3.8+

Limitations

1 trillion tokens is substantial but smaller than largest modern pretraining corpora (e.g., Llama 2 used 2 trillion tokens) — may require supplementation for state-of-the-art models

English-only focus limits multilingual model development

Document-sourced text may have different statistical properties than web text (e.g., higher formality, different domain distribution) — requires careful mixing with other corpora

What makes it unique

Derives 1 trillion tokens specifically from PDF documents rather than generic web crawls, capturing formal, structured writing with higher information density than typical web text. Preserves document-level context and structure signals that web-only corpora lose.

vs alternatives

Complements web-text corpora (C4, The Pile) by providing document-sourced content with different statistical properties, useful for models requiring strong document understanding capabilities.

document-domain dataset sampling and filtering

Medium confidence

Enables selective access to dataset subsets filtered by document characteristics (source domain, document type, quality metrics) without downloading the full 1 trillion token corpus. The dataset infrastructure supports streaming access with client-side filtering, allowing researchers to construct domain-specific training sets from the larger collection. Filtering operates on document metadata including source URLs, extraction quality scores, and document type classifications.

Solves for

Create domain-specific training datasets (e.g., scientific papers, legal documents, technical manuals)Sample balanced datasets across document types for targeted model trainingFilter out low-quality documents or specific sources for quality-focused trainingExplore dataset composition and statistics without full download

Best for

Researchers building domain-specific models without full dataset download

Teams with limited storage requiring selective dataset access

Organizations needing quality-filtered subsets for production training

Requires

Hugging Face Datasets library with streaming support

Network connectivity for streaming access

Python 3.8+

Limitations

Filtering operates on available metadata — fine-grained content-based filtering (e.g., by topic or writing style) requires downloading and processing samples

Streaming access adds latency compared to local dataset copies — not suitable for repeated training iterations without caching

No pre-computed quality scores — filtering by quality requires custom evaluation

What makes it unique

Provides streaming access with metadata-based filtering on trillion-token dataset without requiring full download, using Hugging Face Datasets infrastructure for efficient subset construction. Enables on-demand domain-specific corpus creation from larger collection.

vs alternatives

More flexible than fixed-size domain datasets (e.g., ArXiv papers, legal documents) by allowing dynamic filtering from larger corpus; more efficient than downloading full dataset for subset access.

document structure and layout preservation in extraction

Medium confidence

Maintains document layout information (page structure, text positioning, formatting) during PDF-to-text conversion, enabling models to learn relationships between visual layout and semantic content. The extraction pipeline preserves spatial coordinates, text ordering, and structural hierarchy (headings, sections, lists) rather than flattening documents to linear text. This architectural choice enables training of layout-aware models that can reason about document organization.

Solves for

Train models that understand document structure and layout significanceBuild systems that can extract information based on visual document organizationCreate models that preserve formatting when processing documentsDevelop layout-aware document understanding and retrieval systems

Best for

Researchers building layout-aware document understanding models

Teams developing document structure analysis systems

Organizations building document-to-structured-data extraction pipelines

Requires

PDF parsing libraries with layout support (e.g., pdfplumber, PyPDF2)

Model architectures supporting spatial/layout information (vision transformers, layout-aware LLMs)

Python 3.8+

Limitations

Layout preservation adds complexity to data representation — requires specialized handling in model architectures

Scanned PDFs may have inconsistent or degraded layout information affecting structure preservation

Layout-aware training requires models with spatial reasoning capabilities — not compatible with simple text-only architectures

What makes it unique

Preserves document layout and spatial relationships during extraction rather than flattening to linear text, enabling training of models that understand how document organization conveys meaning. Uses coordinate-aware parsing to maintain structural hierarchy.

vs alternatives

Enables layout-aware training unlike text-only corpora (C4, The Pile) while providing larger scale than manually-annotated layout datasets (DocVQA, RVL-CDIP).

common crawl pdf snapshot integration and versioning

Medium confidence

Provides access to a specific snapshot of PDF documents from Common Crawl (2023-40 version), with consistent versioning and reproducibility guarantees. The dataset is built from a fixed Common Crawl snapshot, enabling reproducible research and consistent data across training runs. Infrastructure includes metadata linking documents to their Common Crawl source, enabling traceability and potential re-extraction with updated pipelines.

Solves for

Access reproducible, versioned document corpora for researchBuild models with traceable data provenance for publication and reproducibilityCompare model performance across different dataset versionsUnderstand document source distribution and Common Crawl composition

Best for

Researchers requiring reproducible datasets for published work

Teams building models with strict data provenance requirements

Organizations comparing model performance across dataset versions

Requires

Hugging Face Datasets library

Knowledge of Common Crawl structure and metadata

Python 3.8+

Limitations

Static snapshot from 2023 — does not include documents added to Common Crawl after snapshot date

Common Crawl PDF quality varies — includes spam, corrupted files, and low-quality documents without filtering

No automatic updates — requires manual re-processing for new Common Crawl snapshots

What makes it unique

Provides versioned, reproducible access to specific Common Crawl PDF snapshot (2023-40) with full provenance tracking, enabling research reproducibility. Unlike generic Common Crawl access, includes pre-processed extraction and structured metadata.

vs alternatives

More reproducible than direct Common Crawl access (which changes over time) while providing pre-processed documents unlike raw Common Crawl snapshots.

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-2023-40, ranked by overlap. Discovered automatically through the match graph.

Model52

GLM-OCR

image-to-text model by undefined. 75,19,420 downloads.

multilingual document text extraction from imageslanguage-agnostic text recognition with shared vocabulary

2 shared capabilities

Dataset45

ShareGPT4V

1.2M image-text pairs with GPT-4V captions.

vision-language model pretraining dataset construction

1 shared capability

Dataset48

LAION-5B

5.85 billion image-text pairs foundational for image generation.

web-scale image-text pair dataset provision

1 shared capability

Dataset26

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

Dataset by mlfoundations. 6,33,111 downloads.

multimodal image-text pair extraction from pdf documents at scale

1 shared capability

Dataset26

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

Dataset by mlfoundations. 5,39,406 downloads.

large-scale multimodal document-image-text dataset curation and indexing

1 shared capability

Dataset26

MINT-1T-PDF-CC-2023-50

Dataset by mlfoundations. 7,96,577 downloads.

multimodal pdf-to-text extraction at scale

1 shared capability

Best For

✓ML researchers training document understanding models
✓Teams building enterprise document processing pipelines
✓Researchers working on multimodal vision-language models
✓Organizations needing large-scale OCR training data
✓ML teams training CLIP-style vision-language models
✓Researchers building document question-answering systems
✓Organizations developing multimodal retrieval systems
✓Teams working on document-based RAG (retrieval-augmented generation) systems

Known Limitations

⚠Dataset is 100B-1T tokens in size — requires significant storage (terabyte-scale infrastructure) and computational resources for full training
⚠PDF quality varies across Common Crawl sources — some documents may have poor OCR quality or corrupted metadata
⚠English-language focused — limited multilingual coverage despite global web crawl
⚠Static snapshot from 2023 — does not include real-time or continuously updated documents
⚠No built-in quality filtering for document relevance — requires downstream curation for domain-specific applications
⚠Implicit alignment between images and text may be noisy — some documents have complex layouts where text-image correspondence is ambiguous

Requirements

Hugging Face Datasets library (datasets>=2.0)Minimum 500GB storage for partial dataset accessPython 3.8+For full training: distributed computing infrastructure (GPU/TPU clusters with 100GB+ VRAM)Hugging Face Datasets library with streaming supportImage processing libraries (PIL, OpenCV)For training: PyTorch or TensorFlow with multimodal model supportHugging Face Datasets library

Input / Output

Accepts: PDF documents (native and scanned), Document metadata (source URLs, timestamps), Layout annotations (bounding boxes, page structure), PDF pages (as images), Extracted text content, Document metadata and source information, Raw extracted text from PDFs, Document metadata (source, timestamp), Filter criteria (domain, document type, quality thresholds), Metadata queries, PDF documents with layout information, Page structure metadata (coordinates, text boxes), Common Crawl snapshot identifiers, Document source URLs

Produces: Extracted text with structure preservation, Image representations of document pages, Paired text-image samples for multimodal training, Metadata including document source and processing metadata, Image tensors (document page renderings), Text strings (extracted content), Paired samples for contrastive learning, Metadata linking images to source documents, Tokenized sequences, Text chunks at various granularities, Metadata-annotated text samples, Filtered dataset subsets, Dataset statistics and composition information, Sampled documents matching filter criteria, Structured text with layout annotations, Spatial coordinate information, Hierarchical document structure representations, Image-text pairs preserving layout context, Extracted documents with source metadata, Common Crawl provenance information, Version identifiers and timestamps

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-2023-40 — a dataset on HuggingFace with 8,57,357 downloads

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Data Sources

huggingface

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Capabilities6 decomposed

multimodal document-to-text extraction at scale

Medium confidence

Solves for

Best for

ML researchers training document understanding models

Teams building enterprise document processing pipelines

Researchers working on multimodal vision-language models

Requires

Hugging Face Datasets library (datasets>=2.0)

Minimum 500GB storage for partial dataset access

Python 3.8+

Limitations

Dataset is 100B-1T tokens in size — requires significant storage (terabyte-scale infrastructure) and computational resources for full training

PDF quality varies across Common Crawl sources — some documents may have poor OCR quality or corrupted metadata

English-language focused — limited multilingual coverage despite global web crawl

What makes it unique

vs alternatives

Larger and more diverse than academic document datasets (e.g., DocVQA, RVL-CDIP) while maintaining layout information that generic text corpora like C4 or The Pile discard entirely.

paired image-text dataset construction for vision-language training

Medium confidence

Solves for

Best for

ML teams training CLIP-style vision-language models

Researchers building document question-answering systems

Organizations developing multimodal retrieval systems

Requires

Hugging Face Datasets library with streaming support

Image processing libraries (PIL, OpenCV)

Python 3.8+

Limitations

Implicit alignment between images and text may be noisy — some documents have complex layouts where text-image correspondence is ambiguous

Page-level granularity may be too coarse for fine-grained visual reasoning tasks requiring sub-document element understanding

No explicit quality scores for image-text pairs — requires downstream filtering for high-quality training data

What makes it unique

vs alternatives

Provides orders of magnitude more image-text pairs than manually-curated datasets while maintaining document-specific semantic alignment that generic web image-text pairs (Laion) lack.

large-scale text corpus for language model pretraining

Medium confidence

Solves for

Best for

ML researchers training foundation models with document-heavy content

Teams building domain-specific language models (legal, scientific, technical)

Organizations needing large-scale English text corpora for model training

Requires

Hugging Face Datasets library

Tokenizer compatible with target model (e.g., GPT-2, LLaMA tokenizers)

Python 3.8+

Limitations

1 trillion tokens is substantial but smaller than largest modern pretraining corpora (e.g., Llama 2 used 2 trillion tokens) — may require supplementation for state-of-the-art models

English-only focus limits multilingual model development

Document-sourced text may have different statistical properties than web text (e.g., higher formality, different domain distribution) — requires careful mixing with other corpora

What makes it unique

vs alternatives

Complements web-text corpora (C4, The Pile) by providing document-sourced content with different statistical properties, useful for models requiring strong document understanding capabilities.

document-domain dataset sampling and filtering

Medium confidence

Solves for

Best for

Researchers building domain-specific models without full dataset download

Teams with limited storage requiring selective dataset access

Organizations needing quality-filtered subsets for production training

Requires

Hugging Face Datasets library with streaming support

Network connectivity for streaming access

Python 3.8+

Limitations

Filtering operates on available metadata — fine-grained content-based filtering (e.g., by topic or writing style) requires downloading and processing samples

Streaming access adds latency compared to local dataset copies — not suitable for repeated training iterations without caching

No pre-computed quality scores — filtering by quality requires custom evaluation

What makes it unique

vs alternatives

More flexible than fixed-size domain datasets (e.g., ArXiv papers, legal documents) by allowing dynamic filtering from larger corpus; more efficient than downloading full dataset for subset access.

document structure and layout preservation in extraction

Medium confidence

Solves for

Best for

Researchers building layout-aware document understanding models

Teams developing document structure analysis systems

Organizations building document-to-structured-data extraction pipelines

Requires

PDF parsing libraries with layout support (e.g., pdfplumber, PyPDF2)

Model architectures supporting spatial/layout information (vision transformers, layout-aware LLMs)

Python 3.8+

Limitations

Layout preservation adds complexity to data representation — requires specialized handling in model architectures

Scanned PDFs may have inconsistent or degraded layout information affecting structure preservation

Layout-aware training requires models with spatial reasoning capabilities — not compatible with simple text-only architectures

What makes it unique

vs alternatives

Enables layout-aware training unlike text-only corpora (C4, The Pile) while providing larger scale than manually-annotated layout datasets (DocVQA, RVL-CDIP).

common crawl pdf snapshot integration and versioning

Medium confidence

Solves for

Best for

Researchers requiring reproducible datasets for published work

Teams building models with strict data provenance requirements

Organizations comparing model performance across dataset versions

Requires

Hugging Face Datasets library

Knowledge of Common Crawl structure and metadata

Python 3.8+

Limitations

Static snapshot from 2023 — does not include documents added to Common Crawl after snapshot date

Common Crawl PDF quality varies — includes spam, corrupted files, and low-quality documents without filtering

No automatic updates — requires manual re-processing for new Common Crawl snapshots

What makes it unique

vs alternatives

More reproducible than direct Common Crawl access (which changes over time) while providing pre-processed documents unlike raw Common Crawl snapshots.

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

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

Capabilities6 decomposed

multimodal document-to-text extraction at scale

paired image-text dataset construction for vision-language training

large-scale text corpus for language model pretraining

document-domain dataset sampling and filtering

document structure and layout preservation in extraction

common crawl pdf snapshot integration and versioning

Related Artifactssharing capabilities

GLM-OCR

ShareGPT4V

LAION-5B

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

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

MINT-1T-PDF-CC-2023-50

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

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Data Sources

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

Capabilities6 decomposed

multimodal document-to-text extraction at scale

paired image-text dataset construction for vision-language training

large-scale text corpus for language model pretraining

document-domain dataset sampling and filtering

document structure and layout preservation in extraction

common crawl pdf snapshot integration and versioning

Related Artifactssharing capabilities

GLM-OCR

ShareGPT4V

LAION-5B

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

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

MINT-1T-PDF-CC-2023-50

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

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

Are you the builder of MINT-1T-PDF-CC-2023-40?

Get the weekly brief

Data Sources