---
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](https://arxiv.org/abs/2305.13915) 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:
```python
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:
```python
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.
```python
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:
```python
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.