---
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: 60224
    num_examples: 45
  - name: test
    num_bytes: 33370
    num_examples: 30
  download_size: 58177
  dataset_size: 93594
---
---

# watsonxDocsQA Dataset

## Overview

**watsonxDocsQA**  is a new open-source dataset and benchmark contributed by IBM. The dataset is derived from enterprise product documentation and 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: [benjams@il.ibm.com](mailto:benjams@il.ibm.com)
- Or, open an [pull request/discussion](https://huggingface.co/datasets/ibm-research/watsonxDocsQA/discussions/new) in this repository.

---