Update README.md

README.md

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----
-license: mit
----
+---
+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.*