📊 Showing 5 results | 📏 Metric: F1-Score
| Rank | Model | Paper | F1-Score | Date | Code |
|---|---|---|---|---|---|
| 1 | GPT-4_10_example_values_&_10_demonstrations | Using LLMs for the Extraction and Normalization of Product Attribute Values | 90.54 | 2024-03-04 | 📦 wbsg-uni-mannheim/wdc-pave |
| 2 | GPT-3.5_10_example_values_&_10_demonstrations | Using LLMs for the Extraction and Normalization of Product Attribute Values | 88.02 | 2024-03-04 | 📦 wbsg-uni-mannheim/wdc-pave |
| 3 | AVEQA | Using LLMs for the Extraction and Normalization of Product Attribute Values | 80.83 | 2024-03-04 | 📦 wbsg-uni-mannheim/wdc-pave |
| 4 | MAVEQA | Using LLMs for the Extraction and Normalization of Product Attribute Values | 65.10 | 2024-03-04 | 📦 wbsg-uni-mannheim/wdc-pave |
| 5 | SU-OpenTag | Using LLMs for the Extraction and Normalization of Product Attribute Values | 60.44 | 2024-03-04 | 📦 wbsg-uni-mannheim/wdc-pave |