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针对短问题匹配长文本（每段约1000字），目前SOTA的模型是什么？从哪里能看到所有的模型列表和相应的说明
我看semantic_search_example.py中query和passage都是使用的rocketqa-zh-nano-query-encoder。 在DensePassageRetriever中默认的参数是 query_embedding_model: Union[Path, str] = "rocketqa-zh-dureader-query-encoder", passage_embedding_model: Union[Path, str] = "rocketqa-zh-dureader-para-encoder", 针对query和document是否要使用不同的模型？
query_embedding_model: Union[Path, str] = "rocketqa-zh-dureader-query-encoder", passage_embedding_model: Union[Path, str] = "rocketqa-zh-dureader-para-encoder",
The text was updated successfully, but these errors were encountered:
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针对短问题匹配长文本（每段约1000字），目前SOTA的模型是什么？从哪里能看到所有的模型列表和相应的说明
我看semantic_search_example.py中query和passage都是使用的rocketqa-zh-nano-query-encoder。
在DensePassageRetriever中默认的参数是
query_embedding_model: Union[Path, str] = "rocketqa-zh-dureader-query-encoder", passage_embedding_model: Union[Path, str] = "rocketqa-zh-dureader-para-encoder",针对query和document是否要使用不同的模型？
The text was updated successfully, but these errors were encountered: