Document Visual Question Answering Dataset

Mp DocVQA is a multimodal dataset for document visual question answering, created by the lmms-lab. It contains image-text pairs where questions are posed about document images. The dataset was last updated on Hugging Face in February 2024.

Use Cases

• Train models to answer questions based on text extracted from document images.
• Benchmark the performance of multimodal vision-language models on document-based VQA tasks.
• Fine-tune models to understand the relationship between visual layout and textual content in scanned documents.

Strengths

• Dataset size is categorized as between 10,000 and 100,000 samples based on platform tags.
• Contains multimodal data (image and text) for complex reasoning tasks.
• Stored in Parquet format for efficient data handling.

Limitations

• Specific row count, column names, and document types are unknown.
• Geographic and temporal coverage of the source documents is unspecified.
• License information is unavailable, which may restrict usage.

Provenance

Source

lmms-lab on Hugging Face.

Collection Method

null

Time Range

null

Freshness

Last updated on Hugging Face in February 2024.

Geography

Platform tags suggest a US region focus, but specific coverage is unknown.