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DRAGON+ is a BERT-base sized dense retriever initialized from [RetroMAE](https://huggingface.co/Shitao/RetroMAE) and further trained on the data augmented from MS MARCO corpus, following the approach described in [How to Train Your DRAGON:
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Diverse Augmentation Towards Generalizable Dense Retrieval](https://arxiv.org/abs/2302.07452).
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Model | Initialization | Query Encoder Path | Context Encoder Path
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DRAGON+ | Shitao/RetroMAE| [facebook/dragon-plus-query-encoder](https://huggingface.co/facebook/dragon-plus-query-encoder) | [facebook/dragon-plus-context-encoder](https://huggingface.co/facebook/dragon-plus-context-encoder)
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## Usage (HuggingFace Transformers)
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Using the model directly available in HuggingFace transformers .
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DRAGON+ is a BERT-base sized dense retriever initialized from [RetroMAE](https://huggingface.co/Shitao/RetroMAE) and further trained on the data augmented from MS MARCO corpus, following the approach described in [How to Train Your DRAGON:
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Diverse Augmentation Towards Generalizable Dense Retrieval](https://arxiv.org/abs/2302.07452).
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<p align="center">
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<img src="https://raw.githubusercontent.com/facebookresearch/dpr-scale/main/dragon/images/teaser.png" width="600">
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</p>
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The associated GitHub repository is available here https://github.com/facebookresearch/dpr-scale/tree/main/dragon. We use asymmetric dual encoder, with two distinctly parameterized encoders. The following models are also available:
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Model | Initialization | MARCO Dev | BEIR | Query Encoder Path | Context Encoder Path
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DRAGON+ | Shitao/RetroMAE| 39.0 | 47.4 | [facebook/dragon-plus-query-encoder](https://huggingface.co/facebook/dragon-plus-query-encoder) | [facebook/dragon-plus-context-encoder](https://huggingface.co/facebook/dragon-plus-context-encoder)
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DRAGON-RoBERTa | RoBERTa-base | 39.4 | 47.2 | [facebook/dragon-roberta-query-encoder](https://huggingface.co/facebook/dragon-roberta-query-encoder) | [facebook/dragon-roberta-context-encoder](https://huggingface.co/facebook/dragon-roberta-context-encoder)
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## Usage (HuggingFace Transformers)
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Using the model directly available in HuggingFace transformers .
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