Microsoft's Dit Base is a transformer encoder model pre-trained on 42 million document images in a self-supervised fashion, learning an inner representation of images for downstream tasks like document image classification and layout analysis. It is genuinely best at encoding document images into a vector space, allowing for fine-tuning on specific tasks such as table detection.
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The creator's other models in the catalog, with their context, size and license where published.
Put this model beside its alternatives on the same evidence, or go back to the full catalog.
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Microsoft has not published a context window for Dit Base, so the catalog shows it as not available rather than estimating one.
Yes. Dit Base is an open-weight model released under the CC BY NC license, so the weights can be downloaded and self-hosted within that license's terms.
Dit Base accepts Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Dit Base is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Dit Base is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
Microsoft has not published a parameter count for Dit Base, so the catalog cannot estimate its memory footprint. The capacity planner can size it from a parameter count you supply.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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