---
configs:
- config_name: crag_task_1_and_2_subset_1
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_1.jsonl.bz2
- config_name: crag_task_1_and_2_subset_2
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_2.jsonl.bz2
- config_name: crag_task_1_and_2_subset_3
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_3.jsonl.bz2
- config_name: crag_task_1_and_2_subset_4
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_4.jsonl.bz2
- config_name: crag_task_1_and_2_subset_5
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_5.jsonl.bz2
- config_name: crag_task_1_and_2_subset_6
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_6.jsonl.bz2
- config_name: crag_task_1_and_2_subset_7
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_7.jsonl.bz2
- config_name: crag_task_1_and_2_subset_8
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_8.jsonl.bz2
- config_name: crag_task_1_and_2_subset_9
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_9.jsonl.bz2
- config_name: crag_task_1_and_2_subset_10
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_10.jsonl.bz2
- config_name: crag_task_1_and_2_subset_11
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_11.jsonl.bz2
- config_name: crag_task_1_and_2_subset_12
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_12.jsonl.bz2
- config_name: crag_task_1_and_2_subset_13
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_13.jsonl.bz2
- config_name: crag_task_1_and_2_subset_14
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_14.jsonl.bz2
- config_name: crag_task_1_and_2_subset_15
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_15.jsonl.bz2
- config_name: crag_task_1_and_2_subset_16
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_16.jsonl.bz2
- config_name: crag_task_1_and_2_subset_17
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_17.jsonl.bz2
- config_name: crag_task_1_and_2_subset_18
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_18.jsonl.bz2
- config_name: crag_task_1_and_2_subset_19
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_19.jsonl.bz2
- config_name: crag_task_1_and_2_subset_20
  data_files: subset/crag_task_1_and_2/crag_task_1_and_2_dev_v4_subset_20.jsonl.bz2
license: cc-by-nc-4.0
task_categories:
- question-answering
- summarization
language:
- en
tags:
- music
- finance
size_categories:
- 1K<n<10K
pretty_name: CRA
---

Datasets are taken from Facebook's [CRAG: Comprehensive RAG Benchmark](https://github.com/facebookresearch/CRAG), see their [arXiv paper](https://arxiv.org/abs/2406.04744) for details about the dataset construction.

# CRAG Sampler

We have added a simple Python tool for performing stratified sampling on CRAG data.

## Installation

### Local Development Install (Recommended)
```bash
git clone https://huggingface.co/Quivr/CRAG.git
cd CRAG
pip install -r requirements.txt  # Install dependencies
pip install -e .  # Install package in development mode
```

## Quick Start

### Running the example

```bash
python -m examples.basic_sampling
```

# CRAG dataset

CRAG (Comprehensive RAG Benchmark) is a rich and comprehensive factual question answering benchmark designed to advance research in RAG. The public version of the dataset includes:
- 2706 Question-Answer pairs
- 5 domains: Finance, Sports, Music, Movie, and Open domain
- 8 types of questions (see image below): simple, simple with condition, set, comparison, aggregation, multi-hop, post-processing heavy, and false premise

![](figs/CRAG_table_2.png)

The datasets `crag_task_1_and_2_dev_v4_subsample_*.json.bz2` have been created from the dataset [crag_task_1_and_2_dev_v4.jsonl.bz2](https://github.com/facebookresearch/CRAG/raw/refs/heads/main/data/crag_task_1_and_2_dev_v4.jsonl.bz2?download=) available on CRAG's GitHub repository. 
For an easier handling and download of the dataset, we have used our CRAG sampler to split the 2706 rows of the original file in 5 subsamples, following the procedure below: 
1. We have created a new label `answer_type`, classifying the answers in 3 categories:
      - `invalid` for any answer == "invalid question"
      - `no_answer` for any answer == "i don't know"
      - `valid` for any other answer
2. We have considered the labels `answer_type`, `domain`, `question_type` and `static_or_dynamic` and performed stratified sampling, splitting the datasets in 5 subsamples. Each subsample has thus the same statistical properties of the full dataset.

We report below the data schema as provided in [CRAG's GitHub repository](https://github.com/facebookresearch/CRAG/blob/main/docs/dataset.md).

## Data Schema

| Field Name             | Type          | Description                                                                                                                                                           |
|------------------------|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `interaction_id`       | string        | A unique identifier for each example.                                                                                                                                |
| `query_time`           | string        | Date and time when the query and the web search occurred.                                                                                                            |
| `domain`               | string        | Domain label for the query. Possible values: "finance", "music", "movie", "sports", "open". "Open" includes any factual queries not among the previous four domains. |
| `question_type`        | string        | Type label about the query. Possible values include: "simple", "simple_w_condition", "comparison", "aggregation", "set", "false_premise", "post-processing", "multi-hop".      |
| `static_or_dynamic`    | string        | Indicates whether the answer to a question changes and the expected rate of change. Possible values: "static", "slow-changing", "fast-changing", and "real-time".    |
| `query`                | string        | The question for RAG to answer.                                                                                                                                       |
| `answer`               | string        | The gold standard answer to the question.                                                                                                                             |
| `alt_ans`  | list        | Other valid gold standard answers to the question.                                                                                                                    |
| `split`                | integer       | Data split indicator, where 0 is for validation and 1 is for the public test.                                                                                         |
| `search_results`       | list of JSON  | Contains up to `k` HTML pages for each query (`k=5` for Task #1 and `k=50` for Task #3), including page name, URL, snippet, full HTML, and last modified time.         |

### Search Results Detail

| Key                  | Type   | Description                                             |
|----------------------|--------|---------------------------------------------------------|
| `page_name`          | string | The name of the webpage.                                |
| `page_url`           | string | The URL of the webpage.                                 |
| `page_snippet`       | string | A short paragraph describing the major content of the page. |
| `page_result`        | string | The full HTML of the webpage.                           |
| `page_last_modified` | string | The time when the page was last modified.               |