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@@ -115,13 +115,12 @@ Train data should be a json file, where each line is a dict like this:
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  {"query": str, "pos": List[str], "neg": List[str]}
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  ```
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- `query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts, `prompt` indicates the
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- relationship between query and texts. If you have no negative texts for a query, you can random sample some from the
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- entire corpus as the negatives.
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  ## Performance
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- In the following table, we provide various pre-trained Cross-Encoders together with their performance on
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  the [MS MMarco Passage Reranking - Vi - Dev](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset.
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  | Model-Name | NDCG@3 | MRR@3 | NDCG@5 | MRR@5 | NDCG@10 | MRR@10 | Docs / Sec |
 
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  {"query": str, "pos": List[str], "neg": List[str]}
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  ```
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+ `query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts. If you have no negative
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+ texts for a query, you can random sample some from the entire corpus as the negatives.
 
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  ## Performance
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+ Below is a comparision table of the results we achieved compared to some other pre-trained Cross-Encoders on
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  the [MS MMarco Passage Reranking - Vi - Dev](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset.
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  | Model-Name | NDCG@3 | MRR@3 | NDCG@5 | MRR@5 | NDCG@10 | MRR@10 | Docs / Sec |