Document Visual Question Answering Training Set

DocVQA Train is a dataset for visual question answering on document images. It was uploaded by Raagul04 to Hugging Face in July 2022. The dataset is intended for training models to answer questions based on visual content within documents.

Use Cases

• Train models to answer questions about text content visible in document images.
• Fine-tune vision-language models to locate and extract information from scanned forms or reports.
• Benchmark the performance of document understanding systems on visual question answering tasks.
• Develop pre-training tasks for models that combine optical character recognition with natural language understanding.

Strengths

• Dataset is hosted on Hugging Face, a major platform for AI datasets and models.
• Specifically designed for the Document Visual Question Answering (DocVQA) task, a recognized benchmark.

Limitations

• The number of samples, specific document types, and annotation details are unknown.
• The dataset's license, original source, and collection methodology are not specified.
• The last update was in July 2022, which may limit its relevance for evaluating against the latest model architectures.

Provenance

Source

Raagul04 on Hugging Face.

Freshness

Last updated on 2022-07-15.