Create README.md

README.md

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+# Spider-NQ: Context Encoder
+
+This is the context encoder of the model fine-tuned on Natural Questions (and initialized from Spider) discussed in our paper [Learning to Retrieve Passages without Supervision](https://arxiv.org/abs/2112.07708).
+
+## Usage
+
+We used weight sharing for the query encoder and passage encoder, so the same model should be applied for both.
+
+**Note**! We format the passages similar to DPR, i.e. the title and the text are separated by a `[SEP]` token, but token 
+type ids are all 0-s. 
+
+An example usage:
+
+```python
+from transformers import AutoTokenizer, DPRContextEncoder
+
+tokenizer = AutoTokenizer.from_pretrained("NAACL2022/spider-nq-ctx-encoder")
+model = DPRContextEncoder.from_pretrained("NAACL2022/spider-nq-ctx-encoder")
+
+title = "Sauron"
+context = "Sauron is the title character and main antagonist of J. R. R. Tolkien's \"The Lord of the Rings\"."
+
+input_dict = tokenizer(title, context, return_tensors="pt")
+del input_dict["token_type_ids"]
+
+outputs = model(**input_dict)
+```