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Module base


class BaseRanker(BaseComponent)


| timing(fn, attr_name)

Wrapper method used to time functions.


| eval(label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label", top_k: int = 10, open_domain: bool = False, return_preds: bool = False) -> dict

Performs evaluation of the Ranker. Ranker is evaluated in the same way as a Retriever based on whether it finds the correct document given the query string and at which position in the ranking of documents the correct document is.

| Returns a dict containing the following metrics:

- "recall": Proportion of questions for which correct document is among retrieved documents
- "mrr": Mean of reciprocal rank. Rewards retrievers that give relevant documents a higher rank.
  Only considers the highest ranked relevant document.
- "map": Mean of average precision for each question. Rewards retrievers that give relevant
  documents a higher rank. Considers all retrieved relevant documents. If ``open_domain=True``,
  average precision is normalized by the number of retrieved relevant documents per query.
  If ``open_domain=False``, average precision is normalized by the number of all relevant documents
  per query.


  • label_index: Index/Table in DocumentStore where labeled questions are stored
  • doc_index: Index/Table in DocumentStore where documents that are used for evaluation are stored
  • top_k: How many documents to return per query
  • open_domain: If True, retrieval will be evaluated by checking if the answer string to a question is contained in the retrieved docs (common approach in open-domain QA). If False, retrieval uses a stricter evaluation that checks if the retrieved document ids are within ids explicitly stated in the labels.
  • return_preds: Whether to add predictions in the returned dictionary. If True, the returned dictionary contains the keys "predictions" and "metrics".

Module sentence_transformers


class SentenceTransformersRanker(BaseRanker)

Sentence Transformer based pre-trained Cross-Encoder model for Document Re-ranking (https://huggingface.co/cross-encoder). Re-Ranking can be used on top of a retriever to boost the performance for document search. This is particularly useful if the retriever has a high recall but is bad in sorting the documents by relevance.

SentenceTransformerRanker handles Cross-Encoder models - use a single logit as similarity score e.g. cross-encoder/ms-marco-MiniLM-L-12-v2 - use two output logits (no_answer, has_answer) e.g. deepset/gbert-base-germandpr-reranking https://www.sbert.net/docs/pretrained-models/ce-msmarco.html#usage-with-transformers

| With a SentenceTransformersRanker, you can:

  • directly get predictions via predict()

Usage example: ... retriever = ElasticsearchRetriever(document_store=document_store) ranker = SentenceTransformersRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-12-v2") p = Pipeline() p.add_node(component=retriever, name="ESRetriever", inputs=["Query"]) p.add_node(component=ranker, name="Ranker", inputs=["ESRetriever"])


| __init__(model_name_or_path: Union[str, Path], model_version: Optional[str] = None, top_k: int = 10, use_gpu: bool = True, devices: Optional[List[Union[int, str, torch.device]]] = None)


  • model_name_or_path: Directory of a saved model or the name of a public model e.g. 'cross-encoder/ms-marco-MiniLM-L-12-v2'. See https://huggingface.co/cross-encoder for full list of available models
  • model_version: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
  • top_k: The maximum number of documents to return
  • use_gpu: Whether to use all available GPUs or the CPU. Falls back on CPU if no GPU is available.
  • devices: List of GPU devices to limit inference to certain GPUs and not use all available ones (e.g. ["cuda:0"]).


| predict_batch(query_doc_list: List[dict], top_k: int = None, batch_size: int = None)

Use loaded Ranker model to, for a list of queries, rank each query's supplied list of Document.

Returns list of dictionary of query and list of document sorted by (desc.) similarity with query


  • query_doc_list: List of dictionaries containing queries with their retrieved documents
  • top_k: The maximum number of answers to return for each query
  • batch_size: Number of samples the model receives in one batch for inference


List of dictionaries containing query and ranked list of Document


| predict(query: str, documents: List[Document], top_k: Optional[int] = None) -> List[Document]

Use loaded ranker model to re-rank the supplied list of Document.

Returns list of Document sorted by (desc.) similarity with the query.


  • query: Query string
  • documents: List of Document to be re-ranked
  • top_k: The maximum number of documents to return


List of Document