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# DPRContextEncoder for TriviaQA |
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## dpr-ctx_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 context 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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## 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 DPRContextEncoder, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("soheeyang/dpr-ctx_encoder-single-trivia-base") |
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ctx_encoder = DPRContextEncoder.from_pretrained("soheeyang/dpr-ctx_encoder-single-trivia-base") |
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data = tokenizer("context comes here", return_tensors="pt") |
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ctx_embedding = ctx_encoder(**data).pooler_output # embedding vector for context |
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``` |
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