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README.md

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+# 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("context comes here", return_tensors="pt")
+question_embedding = question_encoder(**data).pooler_output  # embedding vector for question
+```