README.md

metadata

license: mit
language:
  - en

Laser Cleaning Query Relevance Dataset

Overview

This dataset was created for training text classification models to identify customer queries relevant to laser cleaning services. It contains a comprehensive collection of text examples labeled for relevance to laser cleaning, enabling automated triage of customer inquiries for laser cleaning businesses.

Dataset Description

• Task: Binary classification of text queries
• Classes:
  • 1 = Relevant to laser cleaning
  • 0 = Not relevant to laser cleaning
• Size: Approximately 714 examples
  • Training set: 571 examples (80%)
  • Test set: 143 examples (20%)
• Class distribution:
  • Relevant queries: ~78% (557 examples)
  • Non-relevant queries: ~22% (157 examples)
• Text length: Short to medium queries (typically 5-25 words)
• Languages: English

Files

The dataset is available in multiple formats:

• full_dataset.jsonl - Complete dataset in JSONL format
• train.jsonl - Training split in JSONL format
• test.jsonl - Testing split in JSONL format
• train.csv - Training split in CSV format
• test.csv - Testing split in CSV format

File Format

JSONL Format

{"text": "How does laser cleaning work?", "label": 1}
{"text": "Weather forecast for tomorrow", "label": 0}

CSV Format

text,label
"How does laser cleaning work?",1
"Weather forecast for tomorrow",0

Dataset Creation

This dataset was systematically generated using multiple techniques:

1. Base examples: Manually curated positive examples (relevant to laser cleaning) and negative examples (not relevant).

2. Template-based generation: Using templates with placeholders to create numerous variations:

   "Can laser clean {thing}?"
   "Laser cleaning for {thing} - price?"
   

3. Material and problem variations: Systematic combination of materials (e.g., "car parts", "metal gate", "bronze statue") with cleaning problems (e.g., "rust", "corrosion", "paint").

4. Compound questions: Multi-faceted queries combining different aspects of laser cleaning.

5. Paraphrasing: Alternative phrasings of base examples to increase linguistic diversity.

6. Ambiguous examples: Carefully labeled edge cases to help models learn boundary conditions.

Dataset Content

Relevant Query Categories (Label 1)

• Specific laser cleaning questions
• Service area questions
• Quote and pricing related
• Material-specific inquiries
• Common applications
• Edge cases that are still relevant

Examples:

• "How does laser cleaning work?"
• "Do you offer laser cleaning in Huntsville?"
• "How much does laser cleaning cost?"
• "Laser cleaning for aluminum"
• "Using laser to clean motorcycle parts"
• "Rust removal options in Huntsville"

Non-relevant Query Categories (Label 0)

• General information requests
• Other services entirely
• Random questions
• Consumer products
• Similarly worded but different domains
• Other industrial services

Examples:

• "Weather forecast for tomorrow"
• "Plumbing services in Huntsville"
• "What's the capital of France?"
• "Best laptop under $1000"
• "How to clean my computer keyboard"
• "CNC machining services"

Usage

The dataset is formatted for easy use with common ML libraries:

Loading with Pandas

import pandas as pd

# Load CSV
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')

# Access data
X_train = train_df['text'].values
y_train = train_df['label'].values

Loading with PyTorch and Transformers

from torch.utils.data import Dataset, DataLoader
from transformers import BertTokenizer

class LaserCleaningDataset(Dataset):
    def __init__(self, texts, labels, tokenizer, max_length=128):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_length = max_length
        
    def __len__(self):
        return len(self.texts)
    
    def __getitem__(self, idx):
        text = str(self.texts[idx])
        label = self.labels[idx]
        
        # Tokenize the text
        encoding = self.tokenizer(
            text,
            add_special_tokens=True,
            max_length=self.max_length,
            return_token_type_ids=False,
            padding='max_length',
            truncation=True,
            return_attention_mask=True,
            return_tensors='pt'
        )
        
        return {
            'input_ids': encoding['input_ids'].flatten(),
            'attention_mask': encoding['attention_mask'].flatten(),
            'labels': torch.tensor(label, dtype=torch.long)
        }

Applications

This dataset is designed for:

1. Query triage: Automatically identifying customer inquiries related to laser cleaning
2. Chatbot development: Training chatbots to recognize laser cleaning queries
3. Customer service automation: Routing queries to appropriate service representatives
4. Search relevance: Enhancing search functionality for laser cleaning businesses
5. Marketing analysis: Identifying potential customer needs related to laser cleaning

License

This dataset is provided under the MIT License, allowing for both academic and commercial use with minimal restrictions.

Citation

If you use this dataset in your research or applications, please cite it as:

RustBusters Laser Cleaning Query Relevance Dataset (2025)
Creator: RustBusters LLC
Version: 1.0

Contact

For questions, improvements, or feedback about this dataset, please contact the RustBusters team.

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This dataset was carefully crafted to support machine learning applications in the laser cleaning industry.