| # Spider | |
| This is the unsupervised pretrained model 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("tau/spider") | |
| model = DPRContextEncoder.from_pretrained("tau/spider") | |
| input_dict = tokenizer("title", "text", return_tensors="pt") | |
| del input_dict["token_type_ids"] | |
| outputs = model(**input_dict) | |
| ``` | |