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README.md

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+# rdr-question_encoder-single-nq-base
+
+Reader-Distilled Retriever (`RDR`)
+
+Sohee Yang and Minjoon Seo, [Is Retriever Merely an Approximator of Reader?](https://arxiv.org/abs/2010.10999), arXiv 2020
+
+The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit. The model is a DPR retriever further finetuned using knowledge distillation from the DPR reader. Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k.
+
+This model is the question encoder of RDR trained solely on Natural Questions (NQ) (single-nq). This model is trained by the authors and is the official checkpoint of RDR.
+
+## Performance
+
+The following is the answer recall rate measured using PyTorch 1.4.0 and transformers 4.5.0.
+
+For the values of DPR, those in parentheses are directly taken from the paper. The values without parentheses are reported using the reproduction of DPR that consists of [this question encoder](https://huggingface.co/soheeyang/dpr-question_encoder-single-trivia-base) and [this queston encoder](https://huggingface.co/soheeyang/dpr-question_encoder-single-trivia-base).
+
+|             | Top-K Passages   | 1         | 5         | 20        | 50        | 100       |
+|-------------|------------------|-----------|-----------|-----------|-----------|-----------|
+|**TriviaQA Dev** | **DPR**              | 54.27     | 71.11     | 79.53     | 82.72     | 85.07     |
+|             | **RDR (This Model)** | **61.84** | **75.93** | **82.56** | **85.35** | **87.00** |
+|**TriviaQA Test**| **DPR**              | 54.41     | 70.99     | 79.31 (79.4)     | 82.90     | 84.99 (85.0)     |
+|             | **RDR (This Model)** | **62.56** | **75.92** | **82.52** | **85.64** | **87.26** |
+
+## How to Use
+
+RDR shares the same architecture with DPR. Therefore, It uses `DPRQuestionEncoder` as the model class.
+
+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/rdr-question_encoder-single-trivia-base")
+question_encoder = DPRQuestionEncoder.from_pretrained("soheeyang/rdr-question_encoder-single-trivia-base")
+
+data = tokenizer("question comes here", return_tensors="pt")
+question_embedding = question_encoder(**data).pooler_output  # embedding vector for question
+```