Small Appliance Troubleshooting Document Retrieval Benchmark Dataset
#document retrieval #semantic matching #troubleshooting information retrieval #small appliance troubleshooting #appliance technical support #repair document retrieval
- 5000
- 0.3GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-10-19
AI Analysis & Value Prop
This dataset pairs queries describing small appliance malfunctions or usage problems with candidate web documents, including retailer support pages, FAQs, and troubleshooting guides, and provides relevance labels for each query-document pair. It focuses on knowledge retrieval and query-document matching in small appliance retail after-sales support. Use it to evaluate how well models retrieve suitable troubleshooting resources and to build or validate document retrieval systems. The specific product coverage and dataset size depend on the records provided.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| document | object | A retailer support page, FAQ, or troubleshooting guide that may help answer the query. |
| query_text | string | A user's description of a small appliance malfunction or usage problem. |
| relevance_label | integer | The relevance level of the candidate document to the query; the dataset annotation guidelines define the specific values. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What does the dataset contain?
- It contains queries describing small appliance malfunctions or usage problems, candidate web documents intended to answer them, and relevance labels for the query-document pairs. Candidate documents include retailer support pages, FAQs, and troubleshooting guides.
- What evaluations are supported by the relevance labels?
- The labels can help assess whether a model retrieves suitable support resources for a problem. They can also support comparisons of query-document matching methods, retrieval ranking evaluation, and the development of relevance evaluation workflows. Refer to the dataset documentation for the label values and their definitions.
- Does the dataset cover every appliance brand and failure type?
- The dataset focuses on knowledge retrieval for small appliance retail after-sales support, but the available information does not specify exhaustive brand, product category, or failure-type coverage, nor does it state the sample size. Review the provided records and field documentation to determine whether they fit your products and evaluation needs.
- How can I obtain the dataset, and what usage permissions apply?
- Use the access channel provided on the dataset product page or by its publisher, and check that source for delivery format, sample size, and update information. Dataset terms and the licenses for source web documents may differ. Confirm permitted uses before training, evaluation, or commercial deployment, and follow the source websites' terms.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Small Appliance Troubleshooting Document Retrieval Benchmark Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/8bc30fe78047688e3fed465a1176c143?dataset_scene_cate_type=1},
urldate={2026-10-10},
keywords={small appliance troubleshooting dataset, document retrieval benchmark, retail after-sales support, query-document matching, relevance labeled data, web text retrieval, appliance FAQ dataset, retrieval model evaluation},
version={1.0}
}Using this in research? Please cite us.
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