Update readme

README.md

@@ -12,15 +12,14 @@ This model is the question encoder of RDR trained solely on Natural Questions (N
 
 The following is the answer recall rate measured using PyTorch 1.4.0 and transformers 4.5.0.
 
-The values of DPR on the NQ dev set are taken from Table 1 of the paper of [RDR](https://arxiv.org/abs/2010.10999). The values of DPR on the NQ test set are taken from the [codebase of DPR](https://github.com/facebookresearch/DPR). DPR-adv is the a new DPR model released in March 2021. It is trained on the original DPR NQ train set and its version where hard negatives are mined using DPR index itself using the previous NQ checkpoint. Please refer to the [codebase of DPR](https://github.com/facebookresearch/DPR) for more details about DPR-adv-hn.
+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   |
-|---------|------------------|-------|-------|-------|-------|-------|
-| **NQ Dev**  | **DPR (approx)** | 44.2 | - | 76.9 | 81.3  | 84.2 |
-|             | **RDR (This Model)** | **54.43** | **72.17** | **81.33** | **84.8**  | **86.61** |
-| **NQ Test** | **DPR**              | 45.87 | 68.14 | 79.97 | -     | 85.87 |
-|         | **DPR-adv-hn**          | 52.47 | **72.24** | 81.33 | -     | 87.29 |
-|         | **RDR (This Model)** | **54.29** | 72.16 | **82.8**  | **86.34** | **88.2**  |
+|             | 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
 
@@ -33,8 +32,8 @@ 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-nq-base")
-question_encoder = DPRQuestionEncoder.from_pretrained("soheeyang/rdr-question_encoder-single-nq-base")
+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