class_docstr: BuilderConfig for BEIR datasets.
class_docstr_tok: ["BuilderConfig","for","BEIR","datasets","."]
docstr: BuilderConfig for Beir.
  
  Args:
    name: Name for the config.
    download_url: Path to zip file of corpus, quries, and qrels.
    qrel_splits: Path to splits of TSV files that contain three columns, i.e.
      the query-id, corpus-id and score in this order.
    subdir: The sub-directory to use.
    **kwargs: keyword arguments forwarded to super.
docstr_tok: ["BuilderConfig","for","Beir",".","Args",":","name",":","Name","for","the","config",".","download_url",":","Path","to","zip","file","of","corpus",",","quries",",","and","qrels",".","qrel_splits",":","Path","to","splits","of","TSV","files","that","contain","three","columns",",","i.e",".","the","query-id",",","corpus-id","and","score","in","this","order",".","subdir",":","The","sub-directory","to","use",".","*","*","kwargs",":","keyword","arguments","forwarded","to","super","."]
code: def __init__(
      self,
      name: str,
      download_url: str,
      qrel_splits: Mapping[str, str],
      subdir=None,
      **kwargs,
  ):
      """BuilderConfig for Beir.
  
      Args:
        name: Name for the config.
        download_url: Path to zip file of corpus, quries, and qrels.
        qrel_splits: Path to splits of TSV files that contain three columns, i.e.
          the query-id, corpus-id and score in this order.
        subdir: The sub-directory to use.
        **kwargs: keyword arguments forwarded to super.
      """
      super(BeirConfig, self).__init__(name=name, **kwargs)
  
      self.download_url = download_url
      self.qrel_splits = qrel_splits
      self.subdir = subdir
  
imports: from __future__ import annotations
  import functools
  import json
  import os
  from typing import Mapping
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 to create a new BeirConfig object from inherited base classes: tfds.core with example: obj = BeirConfig(name, download_url, qrel_splits, subdir)