Use HF papers (#38184)
* Use hf papers * Hugging Face papers * doi to hf papers * style
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@@ -30,14 +30,14 @@ The [`EncoderDecoderModel`] can be used to initialize a sequence-to-sequence mod
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pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
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The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks
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was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by
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was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) by
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Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
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After such an [`EncoderDecoderModel`] has been trained/fine-tuned, it can be saved/loaded just like
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any other models (see the examples for more information).
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An application of this architecture could be to leverage two pretrained [`BertModel`] as the encoder
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and decoder for a summarization model as was shown in: [Text Summarization with Pretrained Encoders](https://arxiv.org/abs/1908.08345) by Yang Liu and Mirella Lapata.
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and decoder for a summarization model as was shown in: [Text Summarization with Pretrained Encoders](https://huggingface.co/papers/1908.08345) by Yang Liu and Mirella Lapata.
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## Randomly initializing `EncoderDecoderModel` from model configurations.
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