| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Provides a curated dataset of 1 trillion tokens spanning 539,406 PDF documents with aligned image-to-text pairs extracted from Common Crawl 2023-06 snapshot. The dataset uses a hierarchical indexing structure that maps document boundaries, page-level image coordinates, and corresponding OCR/text extractions, enabling efficient retrieval of multimodal training samples at scale without requiring full dataset materialization in memory.
Unique: Combines 1 trillion tokens of document text with aligned page-level images from a single Common Crawl snapshot, providing temporally-consistent multimodal pairs at unprecedented scale — most competing datasets either use synthetic image-text pairs or lack document-level coherence across modalities
vs alternatives: Larger and more document-focused than LAION-5B (which emphasizes web images) and more naturally-paired than synthetic datasets like Synthetic Docvqa, with real-world OCR challenges that improve model robustness
Implements HuggingFace Datasets streaming protocol that enables on-demand loading of document samples without downloading the full 1T token dataset upfront. The architecture uses memory-mapped file access and configurable batch sampling strategies, allowing training loops to fetch and cache only the samples needed for each epoch while maintaining deterministic shuffling across distributed workers.
Unique: Uses HuggingFace's streaming protocol with deterministic shuffling and worker-aware sharding, enabling true distributed training without pre-downloading — avoids the storage bottleneck that limits competitors like LAION-5B when used in multi-node setups
vs alternatives: More practical for large-scale training than downloading full datasets upfront, and more deterministic than ad-hoc web scraping approaches that lack reproducibility
Maintains structured metadata for each document including source URL, Common Crawl snapshot date (2023-06), document hash, page count, and extraction quality scores. This metadata is queryable and filterable within the dataset, allowing users to select subsets based on source domain, quality thresholds, or temporal characteristics without scanning the full corpus.
Unique: Embeds Common Crawl provenance (URLs, crawl dates, document hashes) directly in the dataset schema, enabling reproducible filtering and bias analysis — most competing datasets either lack this metadata or store it separately, making it harder to correlate quality with source
vs alternatives: Provides better auditability and reproducibility than datasets without source tracking, and more granular filtering than datasets with only aggregate statistics
Extracts page-level images from PDF documents and aligns them with corresponding OCR/text content using spatial layout information (bounding boxes, reading order). The extraction pipeline preserves document structure (headers, footers, tables, body text) by analyzing PDF internal structure and image coordinates, creating naturally-aligned multimodal pairs suitable for vision-language model training without requiring post-hoc alignment.
Unique: Preserves document layout structure through PDF internal coordinate systems rather than post-hoc image analysis, enabling structurally-aware alignment that captures reading order and spatial relationships — most competing datasets either discard layout information or infer it from image analysis alone
vs alternatives: More accurate layout alignment than image-only document datasets, and more scalable than manually-annotated document datasets like DocVQA
Dataset is derived from a single Common Crawl snapshot (2023-06), ensuring temporal consistency across all documents — all PDFs were crawled within a specific time window, avoiding temporal distribution shifts that occur when combining data from multiple crawl dates. The integration includes Common Crawl metadata (WARC records, crawl IDs) enabling users to trace documents back to original crawl artifacts for verification or re-extraction.
Unique: Anchors entire dataset to a single Common Crawl snapshot (2023-06) with traceable WARC references, ensuring temporal consistency and reproducibility — most competing web-derived datasets either combine multiple crawl dates or lack explicit Common Crawl integration
vs alternatives: More reproducible than datasets combining multiple crawl dates, and more verifiable than proprietary datasets without public provenance
Dataset is released under Creative Commons Attribution 4.0 (CC-BY-4.0) license, permitting commercial use, modification, and redistribution with attribution. The license is applied at the dataset level, though individual documents may have different licenses — users are responsible for verifying compliance for derived works, but the dataset itself imposes minimal legal restrictions on model training and deployment.
Unique: Explicitly licensed under CC-BY-4.0 with clear commercial use rights, reducing legal friction for commercial model training — many competing datasets either lack explicit licensing or use more restrictive licenses (e.g., non-commercial only)
vs alternatives: More commercially-friendly than datasets with non-commercial restrictions, and more legally transparent than datasets with unclear licensing
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Pinecone scores higher at 71/100 vs MINT-1T-PDF-CC-2023-06 at 22/100. MINT-1T-PDF-CC-2023-06 leads on ecosystem, while Pinecone is stronger on adoption and quality.
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Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities