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---
license: apache-2.0
configs:
- config_name: corpus
data_files:
- split: train
path: corpus/train-*
- config_name: question_answers
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- split: train
path: question_answers/train-*
- split: test
path: question_answers/test-*
dataset_info:
- config_name: corpus
features:
- name: doc_id
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- name: url
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- name: title
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- name: document
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- name: md_document
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dataset_size: 10625185
- config_name: question_answers
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dtype: string
- name: question
dtype: string
- name: correct_answer
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---
---
# 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](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html)).
- **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](https://arxiv.org/pdf/2401.14367).
---
## 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:
- Email: [[email protected]](mailto:[email protected])
- Or, open an [pull request/discussion](https://huggingface.co/datasets/ibm-research/watsonxDocsQA/discussions/new) in this repository.
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