README.md

---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-translated
license: apache-2.0
multilinguality:
- monolingual
pretty_name: Query Classification
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- text-classification
- query-classification
- intent-detection
- search-queries
task_categories:
- text-classification
task_ids:
- multi-class-classification
- intent-classification
---

# Dataset Card for Query Classification

## Dataset Description

- **Homepage:** [Pankaj8922/query-classification](https://huggingface.co/datasets/Pankaj8922/query-classification)
- **Repository:** [Pankaj8922/query-classification](https://huggingface.co/datasets/Pankaj8922/query-classification)
- **Point of Contact:** Pankaj8922

### Dataset Summary

Query Classification is a dataset of 21,627 English queries categorized into 19 distinct classes. The dataset was created by translating Chinese search queries into English using machine translation, making it suitable for training and evaluating text classification models for query intent detection.

### Supported Tasks and Leaderboards

- **Text Classification:** Classify queries into one of 19 categories.
- **Intent Detection:** Identify the user's intent behind a search query.

### Languages

The dataset is in English (translated from Chinese).

## Dataset Structure

### Data Instances

Each instance consists of a query and its corresponding label:

```json
{
  "Query": "How to promote hair growth",
  "Label": "Medical care"
}
```

### Data Fields

- `Query` (string): The English query text
- `Label` (string): The category label (one of 19 classes)

### Data Splits

| Split | Size |
|-------|------|
| Train | 21,627 |

### Label Distribution

| Label | Count |
|-------|-------|
| Education and Training | 4,273 |
| Medical care | 2,656 |
| Daily Life and Welfare | 2,369 |
| Books and novels | 1,668 |
| Products | 1,342 |
| Movies, TV shows, and anime | 1,311 |
| Characters | 1,222 |
| Software tools | 919 |
| Transportation and Tourism | 908 |
| Social Sciences and Technology | 867 |
| News | 863 |
| Game | 705 |
| Government Affairs | 502 |
| Production and manufacturing | 487 |
| Finance | 436 |
| Audio and performances | 345 |
| Companies and corporate hiring | 267 |
| Real estate decoration | 258 |
| Law | 229 |

## Dataset Creation

### Curation Rationale

This dataset was created to provide a multi-class query classification benchmark for natural language processing tasks. The queries span diverse topics, making it useful for training models to understand user intent in search and conversational AI applications.

### Source Data

The original data consisted of Chinese search queries that were translated to English using machine translation (Tencent/Hy-MT2-1.8B model).

#### Data Collection and Processing

1. Chinese queries and labels were collected from a filtered query classification dataset
2. Labels were translated once using batched machine translation
3. Queries were translated in batches of 96 using machine translation
4. Quality control: Queries where the translation still contained Chinese characters were flagged and excluded from the final dataset
5. Only successfully translated queries with consistent English labels were retained

#### Who are the source language producers?

The original queries were produced by Chinese-speaking users submitting search queries.

### Annotations

The labels were originally in Chinese and translated to English using machine translation. Label consistency was verified to ensure no duplicate or variant translations existed for the same category.

#### Annotation process

- Labels were translated using machine translation (Tencent/Hy-MT2-1.8B)
- Translation quality was verified by checking for remaining Chinese characters
- Label consistency was manually verified to ensure all 19 categories have unique, consistent names

#### Who are the annotators?

Machine translation was performed using the Tencent/Hy-MT2-1.8B model.

### Personal and Sensitive Information

The dataset contains search queries that may include names of public figures, but no private personal information.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset can help improve query classification systems, enabling better search experiences and intent understanding in various applications.

### Discussion of Biases

The dataset reflects the interests and search behaviors of Chinese-speaking users, which may introduce cultural and topical biases. The machine translation process may also introduce subtle semantic shifts from the original queries.

### Other Known Limitations

- Machine translation quality may vary across query types
- Some nuanced queries may have lost subtle meaning during translation
- The dataset contains only one split (train) without validation or test sets

### Licensing Information

This dataset is licensed under the Apache License 2.0.