---
schema_version: "1"
id: 417224630b5a93d23451bbab57031810c72889d93ff47c73a4a9ed0966fc589f
title: SFR-v2-small Embedding Model Reference
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_search_index_sfr_v2_small.md
canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_sfr_v2_small.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 86cf34a8aa97a5833ca5ae00ad1101cacf4b9911535bd390b68d2cd64317813c
description: The SFR-v2-small embedding model offers an enhanced context window of 8,000 tokens and significantly improves user experience and model capabilities.
products:
  - Data 360
release: "264"
---
# SFR-v2-small Embedding Model Reference

The SFR-v2-small embedding model offers an enhanced context window of 8,000 tokens and significantly improves user experience and model capabilities.

### Required Editions

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small edition"><colgroup><col></colgroup><tbody><tr class=""><td class="slds-cell-wrap" style="vertical-align:top;">Available in: <strong>All Editions</strong> supported by Data Cloud. See <a href="https://help.salesforce.com/s/articleView?id=data.c360_a_dc_editions.htm&amp;language=en_US&amp;type=5" target="_blank">Data Cloud edition availability</a>.</td></tr></tbody></table>

The following provides technical information about the SFR-v2-small embedding model.

![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/images/icon_note.png)

Note If Salesforce removes this model due to regulatory changes affecting LLMs developed by Chinese entities, you'll receive the lesser of 90 days notice or the maximum time allowed by applicable law to rebuild your search index with an alternative model.

 
| MoDel Info | Details |
| --- | --- |
| Model type | Text embedding model that accepts text as input and generates 1024-dimensional continuous embedding vectors. It is trained with contrastive loss on query and document pairs. |
| Model architecture | 434M parameter language model |
| Context window | 8192 |
| Model lineage | No previous production models |
| Languages | English |
| Finetuned from model | gte-large-en-v1.5 |
| Quantized from model | N/A |
| Disclaimers and copyright attributions | Apache license 2.0 |

## Technical Specifications

- Number of layers: 24
- Embedding dimension: 1024
- Number of parameters: 434 million
- Model context size: 8,000
- Supported language: English

## Performance and Evaluation

The following captures public benchmarking scores and comparrisons to other embedding models.

- Improvement of +7.5% (15% relative) in retrieval performance over e5-large-v2
- Increased context length from 512 to 8,000 tokens, allowing more flexible knowledge processing

     
| MoDel Info | MTEB (56 tasks) | Retrieval (15 tasks) | Model Size | Embedding Dimension | Context Length |
| --- | --- | --- | --- | --- | --- |
| e5-large-v2 | 62.2 | 50.6 | 335 million | 1024 | 512 tokens |
| SFR-v2-small | 65.9 | 58.1 | 434 million | 1024 | 8192 tokens |