Enterprise AI Analysis
ModelTables: A Corpus of Tables about Models
This article introduces ModelTables, a new benchmark for AI model-related tables, offering a structured approach to model understanding, improved table search, and enhanced AI workflow management. It covers 60K models and 90K tables, demonstrating significant advancements in semantic retrieval and structured knowledge organization.
Executive Impact
ModelTables offers a unique resource for AI research and development, providing structured data for model evaluation, semantic retrieval, and a principled approach to organizing model knowledge.
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Total Models
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Total Tables
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Semantic Retrieval P@1
Deep Analysis & Enterprise Applications
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Details about ModelTables' creation, scope, and comparison to existing datasets.
Methods for finding related tables and their performance on the ModelTables benchmark.
How ModelTables can be used to analyze and integrate information about AI models.
Enterprise Process Flow
Model Card Tables
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GitHub Tables
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arXiv Tables
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Semantic Scholar Tables
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Quality Control
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Strategic Filtering
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Data Augmentation
54.8%
P@1 for Union-based Semantic Table Retrieval, showing clear room for improvement.
| Feature | ModelTables | Data Lake Benchmarks |
|---|---|---|
| Focus | AI Models | Broad Topical Coverage |
| Table Size | Smaller | Larger |
| Inter-Table Relations | Denser | Sparser |
| Ground Truth | Developer-generated | Hand-curated/LLM-generated |
Model Understanding with ModelTables
ModelTables enables a deeper understanding of AI models by linking tables to their model and publication context. This allows users to answer complex questions like "What is the best model for my task?" by integrating related tables and analyzing performance across benchmarks. It supports model card verification and generation, and helps manage private model lakes.
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Estimated Annual Savings
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Hours Reclaimed Annually
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Implementation Roadmap
Our structured approach ensures a seamless integration of AI, delivering measurable results at every stage.
Data Ingestion & Linking
Automated collection of tables from Hugging Face, GitHub, and scholarly papers, linking them to models and publications.
Quality Control & Augmentation
Refining extracted data for accuracy and consistency, applying semantic and structural augmentations for robust retrieval.
Ground Truth Generation
Constructing multi-level ground truth for table relatedness using citation, model lineage, and shared dataset signals.
Benchmark Evaluation & Application
Empirical evaluation of table search methods and exploration of advanced model understanding tasks.
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