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    Models/Document Question Answering/sahil-everlign/layoutlmv2-base-uncased_finetuned_docvqa_on_1200
    Document Question Answeringtransformerscc-by-nc-sa-4.0

    layoutlmv2-base-uncased_finetuned_docvqa_on_1200

    by sahil-everlign

    Identifier
    Model ID
    sahil-everlign/layoutlmv2-base-uncased_finetuned_docvqa_on_1200

    Tags

    transformerstensorboardsafetensorslayoutlmv2document-question-answeringgenerated_from_trainerbase_model:microsoft/layoutlmv2-base-uncasedbase_model:finetune:microsoft/layoutlmv2-base-uncasedlicense:cc-by-nc-sa-4.0endpoints_compatibleregion:us

    Use layoutlmv2-base-uncased_finetuned_docvqa_on_1200 on Mixpeek

    Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

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    How It Runs on Mixpeek

    On Mixpeek, layoutlmv2-base-uncased_finetuned_docvqa_on_1200 runs as a managed extractor inside a processing pipeline. Point a bucket of document question answering data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

    Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.