# 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:**

<figure><img src="https://1811327582-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FYbIiNdp1QbG4avl7VShw%2Fuploads%2FdsuGD0hMknA97tBfII0j%2Fimage.png?alt=media&#x26;token=80e6ec0d-0cf1-4bf4-a4d5-923674742555" alt=""><figcaption></figcaption></figure>

**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}
]
```