Ranker¶
Abstract class for ranking models.
Parameters¶
-
key (str)
Field identifier of each document.
-
on (Union[str, List[str]])
Fields of the documents to use for ranking.
-
encoder
Encoding function to computes embeddings of the documents.
-
normalize (bool)
Normalize the embeddings in order to measure cosine similarity if set to True, dot product if set to False.
-
batch_size (int)
-
k (Optional[int]) – defaults to
None
Methods¶
call
Rank documents according to the query.
Parameters
- q (Union[List[str], str])
- documents (Union[List[List[Dict[str, str]]], List[Dict[str, str]]])
- k (int)
- batch_size (Optional[int]) – defaults to
None - kwargs
add
Pre-compute embeddings and store them at the selected path.
Parameters
- documents (List[Dict[str, str]])
- batch_size (int) – defaults to
64
encode_rank
Encode documents and rank them according to the query.
Parameters
- embeddings_queries (numpy.ndarray)
- documents (List[List[Dict[str, str]]])
- k (int)
- batch_size (Optional[int]) – defaults to
None
rank
Rank inputs documents ordered by relevance among the top k.
Parameters
- embeddings_documents (Dict[str, numpy.ndarray])
- embeddings_queries (numpy.ndarray)
- documents (List[List[Dict[str, str]]])
- k (int)
- batch_size (Optional[int]) – defaults to
None