Instructions to use microsoft/dit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/dit-base with Transformers:
# Load model directly from transformers import AutoImageProcessor, BeitForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("microsoft/dit-base") model = BeitForMaskedImageModeling.from_pretrained("microsoft/dit-base") - Notebooks
- Google Colab
- Kaggle
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# Document Image Transformer (base-sized model)
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Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images.
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Disclaimer: The team releasing DiT did not write a model card for this model so this model card has been written by the Hugging Face team.
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# Document Image Transformer (base-sized model)
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Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/dit). Note that DiT is identical to the architecture of [BEiT](https://huggingface.co/docs/transformers/model_doc/beit).
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Disclaimer: The team releasing DiT did not write a model card for this model so this model card has been written by the Hugging Face team.
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