---
license: unknown
dataset_info:
- config_name: corpus
  features:
  - name: query_id
    dtype: string
  - name: snippets
    dtype: string
  - name: air_date
    dtype: string
  - name: category
    dtype: string
  - name: value
    dtype: string
  - name: round
    dtype: string
  - name: show_number
    dtype: int32
  - name: doc_id
    dtype: string
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 6252715344
    num_examples: 14120776
  download_size: 3271155810
  dataset_size: 6252715344
- config_name: qa_data
  features:
  - name: query_id
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: search_results
    struct:
    - name: related_links
      sequence: string
    - name: snippets
      sequence: string
    - name: titles
      sequence: string
    - name: urls
      sequence: string
  - name: doc_id
    sequence: string
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 6503932619
    num_examples: 173397
  - name: test
    num_bytes: 1830028629
    num_examples: 43350
  download_size: 5008413626
  dataset_size: 8333961248
configs:
- config_name: corpus
  data_files:
  - split: train
    path: corpus/train-*
- config_name: qa_data
  data_files:
  - split: train
    path: qa_data/train-*
  - split: test
    path: qa_data/test-*
---
# preprocessed_SearchQA

The SearchQA question-answer pairs originate from J! Archive2, which comprehensively archives all question-answer pairs
from the renowned television show Jeopardy! The passages, sourced from Google search web page snippets.
We offer passage metadata, encompassing details like 'air_date,' 'category,' 'value,' 'round,' and 'show_number,'
enabling you to enhance retrieval performance at your discretion.
Should you require further details about SearchQA, please refer to below links.

[Github](https://github.com/nyu-dl/dl4ir-searchQA)<br>
[Paper](https://arxiv.org/abs/1704.05179)<br>

The dataset is derived from [searhQA](https://huggingface.co/datasets/search_qa).<br>
This preprocessed dataset is for RAG. For more information about our task, visit our [repository](https://github.com/NomaDamas/RAGchain)!<br>

Preprocess SearchQA dataset code for RAG benchmark. <br>
More information, refer to this link! [huggingface](https://huggingface.co/datasets/NomaDamas/search_qa_split)