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+ # DPRQuestionEncoder for TriviaQA
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+ ## dpr-question_encoder-single-trivia-base
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+ Dense Passage Retrieval (`DPR`)
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+ Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih, [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906), EMNLP 2020.
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+ This model is the question encoder of DPR trained solely on TriviaQA (single-trivia) using the [official implementation of DPR](https://github.com/facebookresearch/DPR).
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+ Disclaimer: This model is not from the authors of DPR, but my reproduction. The authors did not release the DPR weights trained solely on TriviaQA. I hope this model checkpoint can be helpful for those who want to use DPR trained only on TriviaQA.
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+ ## Performance
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+ The following is the answer recall rate measured using PyTorch 1.4.0 and transformers 4.5.0.
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+ The values in parentheses are those reported in the paper.
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+ | Top-k Passages | TriviaQA Dev | TriviaQA Test |
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+ |----------------|--------------|---------------|
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+ | 1 | 54.27 | 54.41 |
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+ | 5 | 71.11 | 70.99 |
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+ | 20 | 79.53 | 79.31 (79.4) |
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+ | 50 | 82.72 | 82.99 |
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+ | 100 | 85.07 | 84.99 (85.0) |
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+
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+ ## How to Use
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+ Using `AutoModel` does not properly detect whether the checkpoint is for `DPRContextEncoder` or `DPRQuestionEncoder`.
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+ Therefore, please specify the exact class to use the model.
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+ ```python
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+ from transformers import DPRQuestionEncoder, AutoTokenizer
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+ tokenizer = AutoTokenizer.from_pretrained("soheeyang/dpr-question_encoder-single-trivia-base")
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+ question_encoder = DPRQuestionEncoder.from_pretrained("soheeyang/dpr-question_encoder-single-trivia-base")
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+ data = tokenizer("context comes here", return_tensors="pt")
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+ question_embedding = question_encoder(**data).pooler_output # embedding vector for question
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+ ```