File size: 5,734 Bytes
ca3bcff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | ---
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
```json
{"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
```python
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
```python
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](https://opensource.org/licenses/MIT), 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.
---
*This dataset was carefully crafted to support machine learning applications in the laser cleaning industry.* |