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metadata
configs:
  - config_name: ConditionalQA-corpus
    data_files:
      - split: test
        path: ConditionalQA/corpus/*
  - config_name: ConditionalQA-docs
    data_files:
      - split: test
        path: ConditionalQA/docs/*
  - config_name: ConditionalQA-corpus_coref
    data_files:
      - split: test
        path: ConditionalQA/corpus_coref/*
  - config_name: ConditionalQA-queries
    data_files:
      - split: train
        path: ConditionalQA/queries/train.parquet
      - split: dev
        path: ConditionalQA/queries/dev.parquet
      - split: test
        path: ConditionalQA/queries/test.parquet
  - config_name: ConditionalQA-qrels
    data_files:
      - split: train
        path: ConditionalQA/qrels/train.parquet
      - split: dev
        path: ConditionalQA/qrels/dev.parquet
      - split: test
        path: ConditionalQA/qrels/test.parquet
  - config_name: ConditionalQA-keyphrases
    data_files:
      - split: test
        path: ConditionalQA/keyphrases/*
  - config_name: NaturalQuestions-corpus
    data_files:
      - split: test
        path: NaturalQuestions/corpus/*
  - config_name: NaturalQuestions-docs
    data_files:
      - split: test
        path: NaturalQuestions/docs/*
  - config_name: NaturalQuestions-corpus_coref
    data_files:
      - split: test
        path: NaturalQuestions/corpus_coref/*
  - config_name: nq-hard
    data_files:
      - split: test
        path: NaturalQuestions/nq-hard/*
  - config_name: NaturalQuestions-queries
    data_files:
      - split: train
        path: NaturalQuestions/queries/train.parquet
      - split: dev
        path: NaturalQuestions/queries/dev.parquet
      - split: test
        path: NaturalQuestions/queries/test.parquet
  - config_name: NaturalQuestions-qrels
    data_files:
      - split: train
        path: NaturalQuestions/qrels/train.parquet
      - split: dev
        path: NaturalQuestions/qrels/dev.parquet
      - split: test
        path: NaturalQuestions/qrels/test.parquet
  - config_name: NaturalQuestions-keyphrases
    data_files:
      - split: test
        path: NaturalQuestions/keyphrases/*
  - config_name: Genomics-corpus
    data_files:
      - split: test
        path: Genomics/corpus/*
  - config_name: Genomics-docs
    data_files:
      - split: test
        path: Genomics/docs/*
  - config_name: Genomics-corpus_coref
    data_files:
      - split: test
        path: Genomics/corpus_coref/*
  - config_name: Genomics-queries
    data_files:
      - split: test
        path: Genomics/queries/test.parquet
  - config_name: Genomics-qrels
    data_files:
      - split: test
        path: Genomics/qrels/test.parquet
  - config_name: Genomics-keyphrases
    data_files:
      - split: test
        path: Genomics/keyphrases/*
  - config_name: MSMARCO-corpus
    data_files:
      - split: test
        path: MSMARCO/corpus/*
  - config_name: MSMARCO-docs
    data_files:
      - split: test
        path: MSMARCO/docs/*
  - config_name: MSMARCO-corpus_coref
    data_files:
      - split: test
        path: MSMARCO/corpus_coref/*
  - config_name: MSMARCO-queries
    data_files:
      - split: train
        path: MSMARCO/queries/train.parquet
      - split: dev
        path: MSMARCO/queries/dev.parquet
      - split: test
        path: MSMARCO/queries/test.parquet
  - config_name: MSMARCO-qrels
    data_files:
      - split: train
        path: MSMARCO/qrels/train.parquet
      - split: dev
        path: MSMARCO/qrels/dev.parquet
      - split: test
        path: MSMARCO/qrels/test.parquet
  - config_name: MSMARCO-keyphrases
    data_files:
      - split: test
        path: MSMARCO/keyphrases/*
  - config_name: MIRACL-corpus
    data_files:
      - split: test
        path: MIRACL/corpus/*
  - config_name: MIRACL-docs
    data_files:
      - split: test
        path: MIRACL/docs/*
  - config_name: MIRACL-corpus_coref
    data_files:
      - split: test
        path: MIRACL/corpus_coref/*
  - config_name: MIRACL-queries
    data_files:
      - split: train
        path: MIRACL/queries/train.parquet
      - split: dev
        path: MIRACL/queries/dev.parquet
      - split: test
        path: MIRACL/queries/test.parquet
  - config_name: MIRACL-qrels
    data_files:
      - split: train
        path: MIRACL/qrels/train.parquet
      - split: dev
        path: MIRACL/qrels/dev.parquet
      - split: test
        path: MIRACL/qrels/test.parquet
  - config_name: MIRACL-keyphrases
    data_files:
      - split: test
        path: MIRACL/keyphrases/*

DAPR: Document-Aware Passage Retrieval

This datasets repo contains the queries, passages/documents and judgements for the data used in the DAPR paper.

Overview

For the DAPR benchmark, it contains 5 datasets:

Dataset #Queries (test) #Documents #Passages
MS MARCO 2,722 1,359,163 2,383,023*
Natural Questions 3,610 108,626 2,682,017
MIRACL 799 5,758,285 32,893,221
Genomics 62 162,259 12,641,127
ConditionalQA 271 652 69,199

And additionally, NQ-hard, the hard subset of queries from Natural Questions is also included (516 in total). These queries are hard because understanding the document context (e.g. coreference, main topic, multi-hop reasoning, and acronym) is necessary for retrieving the relevant passages.

Notes: for MS MARCO, its documents do not provide the gold paragraph segmentation and we only segment the document by keeping the judged passages (from the MS MARCO Passage Ranking task) standing out while leaving the rest parts surrounding these passages. These passages are marked by is_candidate==true.

Load the dataset

Loading the passages

One can load the passages like this:

from datasets import load_dataset

dataset_name = "ConditionalQA"
passages = load_dataset("kwang2049/dapr", f"{dataset_name}-corpus", split="test")
for passage in passages:
    passage["_id"]  # passage id
    passage["text"]  # passage text
    passage["title"]  # doc title
    passage["doc_id"]
    passage["paragraph_no"]  # the paragraph number within the document
    passage["total_paragraphs"]  # how many paragraphs/passages in total in the document
    passage["is_candidate"]  # is this passage a candidate for retrieval

Or strem the dataset without downloading it beforehand:

from datasets import load_dataset

dataset_name = "ConditionalQA"
passages = load_dataset(
    "kwang2049/dapr", f"{dataset_name}-corpus", split="test", streaming=True
)
for passage in passages:
    passage["_id"]  # passage id
    passage["text"]  # passage text
    passage["title"]  # doc title
    passage["doc_id"]
    passage["paragraph_no"]  # the paragraph number within the document
    passage["total_paragraphs"]  # how many paragraphs/passages in total in the document
    passage["is_candidate"]  # is this passage a candidate for retrieval

Loading the qrels

The qrels split contains the query relevance annotation, i.e., it contains the relevance score for (query, passage) pairs.

from datasets import load_dataset

dataset_name = "ConditionalQA"
qrels = load_dataset("kwang2049/dapr", f"{dataset_name}-qrels", split="test")
for qrel in qrels:
    qrel["query_id"]  # query id (the text is available in ConditionalQA-queries)
    qrel["corpus_id"]  # passage id
    qrel["score"]  # gold judgement

We present the NQ-hard dataset in an extended format of the normal qrels with additional columns:

from datasets import load_dataset

qrels = load_dataset("kwang2049/dapr", "nq-hard", split="test")
for qrel in qrels:
    qrel["query_id"]  # query id (the text is available in ConditionalQA-queries)
    qrel["corpus_id"]  # passage id
    qrel["score"]  # gold judgement

    # Additional columns:
    qrel["query"]  # query text
    qrel["text"]  # passage text
    qrel["title"]  # doc title
    qrel["doc_id"]
    qrel["categories"]  # list of categories about this query-passage pair
    qrel["url"]  # url to the document in Wikipedia

Note

This dataset was created with datasets==2.15.0. Make sure to use this or a newer version of the datasets library.