Hugging Face's logo

Datasets:
ibm-research
/
watsonxDocsQA

Modalities:
Text
Formats:
parquet
Size:
1K - 10K
ArXiv:
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files Community
3
watsonxDocsQA / README.md
benjamsz's picture
benjamsz
Upload dataset
6fad96c verified
preview code
|
raw
history blame contribute delete
3.97 kB
metadata
license: apache-2.0
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: corpus/train-*
  - config_name: question_answers
    data_files:
      - split: train
        path: question_answers/train-*
      - split: test
        path: question_answers/test-*
dataset_info:
  - config_name: corpus
    features:
      - name: doc_id
        dtype: string
      - name: url
        dtype: string
      - name: title
        dtype: string
      - name: document
        dtype: string
      - name: md_document
        dtype: string
    splits:
      - name: train
        num_bytes: 10625185
        num_examples: 1144
    download_size: 3327056
    dataset_size: 10625185
  - config_name: question_answers
    features:
      - name: question_id
        dtype: string
      - name: question
        dtype: string
      - name: correct_answer
        dtype: string
      - name: correct_answer_document_ids
        dtype: string
      - name: ground_truths_contexts
        dtype: string
    splits:
      - name: train
        num_bytes: 60268
        num_examples: 45
      - name: test
        num_bytes: 33340
        num_examples: 30
    download_size: 58074
    dataset_size: 93608

watsonxDocsQA Dataset

Overview

watsonxDocsQA is a new open-source dataset and benchmark contributed by IBM. The dataset is derived from enterprise product documentation and is designed specifically for end-to-end Retrieval-Augmented Generation (RAG) evaluation. The dataset consists of two components:

  • Documents: A corpus of 1,144 text and markdown files generated by crawling enterprise documentation (main page - crawl March 2024).
  • Benchmark: A set of 75 question-answer (QA) pairs with gold document labels and answers. The QA pairs are crafted as follows:
    • 25 questions: Human-generated by two subject matter experts.
    • 50 questions: Synthetically generated using the tiiuae/falcon-180b model, then manually filtered and reviewed for quality. The methodology is detailed in Yehudai et al. 2024.

Data Description

Corpus Dataset

The corpus dataset contains the following fields:

Field Description
doc_id Unique identifier for the document
title Document title as it appears on the HTML page
document Textual representation of the content
md_document Markdown representation of the content
url Origin URL of the document

Question-Answers Dataset

The QA dataset includes these fields:

Field Description
question_id Unique identifier for the question
question Text of the question
correct_answer Ground-truth answer
ground_truths_contexts_ids List of ground-truth document IDs
ground_truths_contexts List of grounding texts on which the answer is based

Samples

Below is an example from the question_answers dataset:

  • question_id: watsonx_q_2
  • question: What foundation models have been built by IBM?
  • correct_answer:
    "Foundation models built by IBM include:
    • granite-13b-chat-v2
    • granite-13b-chat-v1
    • granite-13b-instruct-v1"
  • ground_truths_contexts_ids: B2593108FA446C4B4B0EF5ADC2CD5D9585B0B63C
  • ground_truths_contexts: Foundation models built by IBM \n\nIn IBM watsonx.ai, ...

Contact

For questions or feedback, please: