---
title: Cross Encoder Reranker
sidebarTitle: "Cross Encoder"
description: Implement semantic search reranking in LanceDB using Cross Encoder models. Features configurable model selection, device optimization, and comprehensive scoring options for all search types.

---

import { PyRerankingCrossEncoderUsage } from '/snippets/integrations.mdx';

# Cross Encoder Reranker

This reranker uses Cross Encoder models from sentence-transformers to rerank the search results. You can use this reranker by passing `CrossEncoderReranker()` to the `rerank()` method. 
> **Note:** Supported query types – Hybrid, Vector, and FTS.


<CodeGroup>
  <CodeBlock filename="Python" language="Python" icon="python">
    {PyRerankingCrossEncoderUsage}
  </CodeBlock>
</CodeGroup>

## Accepted Arguments {#accepted-arguments}
| Argument | Type | Default | Description |
| --- | --- | --- | --- |
| `model_name` | `str` | `"cross-encoder/ms-marco-TinyBERT-L-6"` | The name of the reranker model to use.|
| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
| `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". |
| `trust_remote_code` | `bool` | `True` | Passed to Sentence Transformers model loading. Set this to `False` when you only want to load models that do not require custom repository code. |
| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", returns only `_relevance_score`. If "all" is supported, returns relevance score along with the vector and/or FTS scores depending on query type. |

The reranker loads the model locally through `sentence-transformers`, so install the local model
runtime dependencies you need, such as PyTorch and any device-specific acceleration packages.

## Supported Scores for each query type {#supported-scores-for-each-query-type}
You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:

### Hybrid Search {#hybrid-search}
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |

### Vector Search {#vector-search}
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |

### FTS Search {#fts-search}
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |