Transformers
PyTorch
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dit
jakewa commited on
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1 Parent(s): bcd125f

Add upstream DiT MIT license information

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The 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.

Files changed (2) hide show
  1. LICENSE +21 -0
  2. README.md +6 -1
LICENSE ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ The MIT License (MIT)
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+
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+ Copyright (c) Microsoft Corporation
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+
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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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+
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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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+
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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.
README.md CHANGED
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  ---
 
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  tags:
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  - dit
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  inference: false
@@ -10,6 +11,10 @@ Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 20
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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.
@@ -54,4 +59,4 @@ loss, logits = outputs.loss, outputs.logits
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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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+
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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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+
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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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+ ```