split_search_qa / README.md
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metadata
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
Paper

The dataset is derived from searhQA.
This preprocessed dataset is for RAG. For more information about our task, visit our repository!

Preprocess SearchQA dataset code for RAG benchmark.
More information, refer to this link! huggingface