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--- |
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language: |
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- en |
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license: mit |
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size_categories: |
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- n<1K |
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task_categories: |
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- text-generation |
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tags: |
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- geo |
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- seo |
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- search-engine-optimization |
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- query-generation |
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--- |
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# GEO Agent Dataset with Queries |
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Dataset with generated train/test queries for Generative Engine Optimization (GEO) research. |
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## Dataset Description |
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This dataset contains 208 web documents with automatically generated search queries for training and evaluation. |
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## Features |
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| Column | Description | |
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|--------|-------------| |
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| doc_id | Unique document identifier | |
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| url | Source URL | |
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| raw_html | Original HTML content | |
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| cleaned_text | Parsed plain text content | |
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| cleaned_text_length | Character count of cleaned text | |
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| tags | Topic classification tags | |
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| primary_topic | Main topic category | |
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| data_source | Original data source | |
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| query | Original search query | |
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| train_queries | Generated training queries (~20 per doc) | |
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| test_queries | Generated test queries (~36 per doc) | |
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## Statistics |
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- **Total documents**: 208 |
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- **Avg train queries**: 19.7 per document |
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- **Avg test queries**: 35.9 per document |
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- **Total train queries**: 4096 |
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- **Total test queries**: 7457 |
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## Usage |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("erv1n/GEO_Agent_with_queries") |
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# Access queries |
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for example in ds["train"]: |
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print(f"Doc: {example['doc_id']}") |
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print(f"Train queries: {example['train_queries'][:3]}") |
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print(f"Test queries: {example['test_queries'][:3]}") |
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break |
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``` |
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## Related Datasets |
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- [erv1n/GEO_Agent](https://huggingface.co/datasets/erv1n/GEO_Agent) - Base dataset without queries |
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