Datasets:

Parssky
/

industrial-instruction-dataset

pretty_name: Industrial-Instruction Dataset
language:
  - en
license: other
task_categories:
  - question-answering
  - text-generation
tags:
  - industrial
  - rag
  - benchmark
  - multiple-choice
configs:
  - config_name: panasonic_qa_v1
    data_files:
      - split: train
        path: panasonic_qa_v1/train-*
      - split: test
        path: panasonic_qa_v1/test-*
  - config_name: panasonic_qa_claude_v1
    data_files:
      - split: train
        path: panasonic_qa_claude_v1/train-*
      - split: test
        path: panasonic_qa_claude_v1/test-*
  - config_name: corpus_panasonic_md_v0_1
    data_files:
      - split: train
        path: corpus_panasonic_md_v0_1/train-*
  - config_name: panasonic_v0_0
    data_files:
      - split: train
        path: panasonic_v0_0/train-*
dataset_info:
  - config_name: panasonic_qa_claude_v1
    features:
      - name: question
        dtype: string
      - name: answer
        list: string
      - name: documents
        list: string
    splits:
      - name: train
        num_bytes: 168144704
        num_examples: 25252
      - name: test
        num_bytes: 6607933
        num_examples: 1000
    download_size: 162307467
    dataset_size: 174752637
  - config_name: panasonic_v0_0
    features:
      - name: page_no
        dtype: int64
      - name: input_height
        dtype: int64
      - name: input_width
        dtype: int64
      - name: layout_info_path
        dtype: string
      - name: layout_image_path
        dtype: string
      - name: md_content_path
        dtype: string
      - name: filtered
        dtype: bool
      - name: file_path
        dtype: string
      - name: md_content
        dtype: string
    splits:
      - name: train
        num_bytes: 993352721
        num_examples: 7525
    download_size: 986131439
    dataset_size: 993352721

Industrial-Instruction Dataset

Industrial-Instruction provides benchmark and training-ready QA instances derived from industrial technical reports, designed to evaluate robustness under realistic retrieval conditions. Samples are grounded in retrieved evidence and include irrelevant retrieval, single-/multi-document support, and single-/multi-document answer settings.

Paper

Industrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and Benchmark Datasets from Industrial Technical Reports

arXiv: https://arxiv.org/abs/2608.22817

Configs

Config	Records	Description
`panasonic_qa_v1`	12,557 train / 1,000 test	QA data generated with the open-weight Qwen3-30B-A3B-Instruct model.
`panasonic_qa_claude_v1`	25,252 train / 1,000 test	QA data generated with Claude-Opus-4.6, same pipeline and prompts.
`corpus_panasonic_md_v0_1`	—	Retrieval corpus: layout-preserved Markdown extracted from the source PDFs.
`panasonic_v0_0`	7,525	Raw, unfiltered page-level extractions before quality filtering.

Each QA record is grounded in five query–document scenarios (r0–r4): irrelevant retrieval, single-/multi-document support, and single-/multi-document answer.

Usage

from datasets import load_dataset

# QA data generated with the open-weight Qwen3-30B-A3B-Instruct model
qa = load_dataset("Parssky/industrial-instruction-dataset", "panasonic_qa_v1")
# DatasetDict: train (12,557) / test (1,000)

# QA data generated with Claude-Opus-4.6
qa_claude = load_dataset("Parssky/industrial-instruction-dataset", "panasonic_qa_claude_v1")
# DatasetDict: train (25,252) / test (1,000)

# Retrieval corpus (layout-preserved Markdown pages)
corpus = load_dataset("Parssky/industrial-instruction-dataset", "corpus_panasonic_md_v0_1")

# Raw, unfiltered page-level extractions
raw = load_dataset("Parssky/industrial-instruction-dataset", "panasonic_v0_0")

Record format

Each QA record has three fields:

Field	Type	Description
`question`	`string`	The question, with its five answer options (A–E) inline.
`answer`	`list[string]`	Correct option letter(s), e.g. `["A"]` or `["B", "C"]`. Some questions have multiple correct answers.
`documents`	`list[string]`	Source passages retrieved from the Panasonic corpus when the item was generated.

ex = qa["test"][0]
print(ex["question"])    # "... which performance characteristic should be prioritized ...
                         #  A Thermal shock resistance  B Solderability  ..."
print(ex["answer"])      # ["A"]
print(ex["documents"])   # ["Current Sensing Resistors, Metal Plate Type\n\n## Performance ..."]

Evaluation

Answers are sets, not ordered strings, so exact-match scoring is misleading (["A","B"] vs ["B","A"]). The paper scores with Set-Match Accuracy, F1 and Jaccard similarity. Benchmark scripts: package_benchmark_panasonic.

Models trained on this dataset

Parssky/industrial-instruction-qwen4b — trained on panasonic_qa_v1
Parssky/industrial-instruction-qwen4b-claude — trained on panasonic_qa_claude_v1

Intended Use

This dataset is for research on industrial retrieval-augmented generation (RAG), evidence integration, and instruction tuning for technical-domain QA.

Notes

Derived from publicly available industrial technical documentation published by Panasonic Corporation.
Use should follow source-document terms and applicable data-use restrictions.

Source Code

GitHub repository: https://github.com/parssky/industrial-instruction

Citation

@misc{parsa_bakhtiari_2026,
    author       = { Parsa Bakhtiari and Hassan Bashiri and Alireza Khalilipour and Masoud Nasiripour and Moharram Challenger },
    title        = { industrial-instruction-dataset (Revision 7eadea0) },
    year         = 2026,
    url          = { https://huggingface.co/datasets/Parssky/industrial-instruction-dataset },
    doi          = { 10.57967/hf/10098 },
    publisher    = { Hugging Face }
}