# Entity Co-occurrence
**Objective:**
Entity Co-occurrence measures the frequency with which specific entities (e.g., names, places, events) appear together within a dataset. It is used to evaluate the relationship modeling of NER systems or entity extraction models. High entity co-occurrence implies that the model successfully identifies relevant pairings of entities that often appear together, important for downstream tasks like relation extraction or knowledge graph construction.
**Required Columns in Dataset:**
`Prompt`, `Retrieved Entities`, `Expected Entities`
**Interpretation:**
* **High Entity Co-occurrence**: Suggests that the model correctly identifies and links related entities, reflecting an understanding of their relationships in the dataset.
* **Low Entity Co-occurrence**: Indicates that the model fails to detect frequent co-occurring entities, potentially missing important connections.
**Execution via UI:**
**Execution via SDK:**
Entity Co-occurrence doesn't require an LLM for computation.
```python
metrics=[
{"name": "Entity Cooccurrence", "column_name": "your-text", "schema_mapping": schema_mapping}
]
```
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