| # DPRQuestionEncoder for TriviaQA | |
| ## dpr-question_encoder-single-trivia-base | |
| Dense Passage Retrieval (`DPR`) | |
| 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. | |
| 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). | |
| 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. | |
| ## Performance | |
| The following is the answer recall rate measured using PyTorch 1.4.0 and transformers 4.5.0. | |
| The values in parentheses are those reported in the paper. | |
| | Top-k Passages | TriviaQA Dev | TriviaQA Test | | |
| |----------------|--------------|---------------| | |
| | 1 | 54.27 | 54.41 | | |
| | 5 | 71.11 | 70.99 | | |
| | 20 | 79.53 | 79.31 (79.4) | | |
| | 50 | 82.72 | 82.99 | | |
| | 100 | 85.07 | 84.99 (85.0) | | |
| ## How to Use | |
| Using `AutoModel` does not properly detect whether the checkpoint is for `DPRContextEncoder` or `DPRQuestionEncoder`. | |
| Therefore, please specify the exact class to use the model. | |
| ```python | |
| from transformers import DPRQuestionEncoder, AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("soheeyang/dpr-question_encoder-single-trivia-base") | |
| question_encoder = DPRQuestionEncoder.from_pretrained("soheeyang/dpr-question_encoder-single-trivia-base") | |
| data = tokenizer("question comes here", return_tensors="pt") | |
| question_embedding = question_encoder(**data).pooler_output # embedding vector for question | |
| ``` | |