Add upstream DiT MIT license information
#5
by jakewa - opened
LICENSE
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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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README.md
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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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