Add readme

README.md

@@ -0,0 +1,41 @@
+# DPRContextEncoder for TriviaQA
+
+## dpr-ctx_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 context encoder of DPR trained on TriviaQA using the [official implementation of DPR](https://github.com/facebookresearch/DPR).
+
+Disclaimer: This model is not from the authors of DPR. It is my own reproduction. The authors did not release the DPR weights for TriviaQA.
+
+## Performance
+
+The performance is 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
+
+`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 DPRContextEncoder, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("soheeyang/dpr-ctx_encoder-single-trivia-base")
+ctx_encoder = DPRContextEncoder.from_pretrained("soheeyang/dpr-ctx_encoder-single-trivia-base")
+
+data = tokenizer("context comes here", return_tensors="pt")
+ctx_embedding = ctx_encoder(**data).pooler_output  # embedding vector for context
+```