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Description
Synthetic annotations for images and documents created by Facebook for the PLM model. The dataset includes generated captions for images from SA1B, OpenImages, and Object365, and question-answer pairs for documents from ArXivQA, UCSF, and PDFAcc. The dataset was last updated on April 21, 2025.
Use Cases
Training vision-language models based on synthetic image captions mentioned in the description
Fine-tuning models for visual question answering based on the synthetic QA pairs
Benchmarking model performance on generated annotations for datasets like SA1B and OpenImages
Augmenting training data for document understanding tasks using the ArXivQA and PDFAcc annotations
Strengths
Covers multiple established datasets including SA1B, OpenImages, and Object365 for image annotations
Includes annotations for document datasets like ArXivQA and PDFAcc, suggesting a multimodal scope
Last updated on 2025-04-21, indicating recent maintenance
Limitations
Column-level documentation is absent; field semantics must be inferred after download
Row count is unknown, which may limit suitability assessment
The synthetic nature of the data may introduce biases or artifacts not present in human-annotated sources
Provenance
Source
facebook
Collection Method
Synthetic generation for the PLM model, as referenced in the associated paper.
Time Range
null
Freshness
Last updated 2025-04-21 18:03:31
Geography
null
License is unknown; terms of use must be verified before application.