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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, BeitForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("microsoft/dit-base") model = BeitForMaskedImageModeling.from_pretrained("microsoft/dit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add upstream DiT MIT license information
Browse filesThe model card currently omits license metadata and the repository has no license file. This adds the MIT license information from the upstream [Microsoft UniLM DiT project](https://github.com/microsoft/unilm/tree/31c5b904ca1bf2afb4c234a6675c683a4e5fc7cd/dit).
- Add `license: mit` to the model-card metadata.
- Add the upstream MIT `LICENSE`, preserving Microsoft's copyright notice and the full license text (line endings normalized to LF).
- Add a model-card License section linking to the local license and its pinned upstream source.
Source: [UniLM LICENSE](https://github.com/microsoft/unilm/blob/31c5b904ca1bf2afb4c234a6675c683a4e5fc7cd/LICENSE). The [upstream README](https://github.com/microsoft/unilm/blob/31c5b904ca1bf2afb4c234a6675c683a4e5fc7cd/README.md#license) identifies this root license as the project license, and DiT has no separate license file.
Validation: Hugging Face model-card validation passed; license content matches the pinned upstream file after normalizing line endings; `git diff --check` passed.
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The MIT License (MIT)
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Copyright (c) Microsoft Corporation
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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---
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tags:
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- dit
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inference: false
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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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## Model description
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The Document Image Transformer (DiT) is a transformer encoder model (BERT-like) pre-trained on a large collection of images in a self-supervised fashion. The pre-training objective for the model is to predict visual tokens from the encoder of a discrete VAE (dVAE), based on masked patches.
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journal={Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval},
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year={2006}
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}
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```
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---
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license: mit
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tags:
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- dit
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inference: false
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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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## License
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DiT is released under the [MIT License](LICENSE), as provided in the [upstream UniLM repository](https://github.com/microsoft/unilm/blob/31c5b904ca1bf2afb4c234a6675c683a4e5fc7cd/LICENSE). Copyright (c) Microsoft Corporation.
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## Model description
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The Document Image Transformer (DiT) is a transformer encoder model (BERT-like) pre-trained on a large collection of images in a self-supervised fashion. The pre-training objective for the model is to predict visual tokens from the encoder of a discrete VAE (dVAE), based on masked patches.
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journal={Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval},
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year={2006}
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}
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```
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