| # Spider-TriviaQA: Context Encoder | |
| This is the context encoder of the model fine-tuned on TriviaQA (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-trivia-ctx-encoder") | |
| model = DPRContextEncoder.from_pretrained("NAACL2022/spider-trivia-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) | |
| ``` | |