stella_en_400M_v5 EU AI Act Compliance Profile

Updates

We released a Jasper and Stella model technology report and code.(2025.1)

Report: https://arxiv.org/abs/2412.19048

Codes: https://github.com/NovaSearch-Team/RAG-Retrieval

Introduction

The models are trained based on Alibaba-NLP/gte-large-en-v1.5 and Alibaba-NLP/gte-Qwen2-1.5B-instruct. Thanks for their contributions!

We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.

Prompt of s2p task(e.g. retrieve task):

Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: {query}

Prompt of s2s task(e.g. semantic textual similarity task):

Instruct: Retrieve semantically similar text.\nQuery: {query}

The models are finally trained by MRL, so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192.

The higher the dimension, the better the performance. Generally speaking, 1024d is good enough. The MTEB score of 1024d is only 0.001 lower than 8192d.

Model directory structure

The model directory structure is very simple, it is a standard SentenceTransformer directory with a series of 2_Dense_{dims} folders, where dims represents the final vector dimension.

For example, the 2_Dense_256 folder stores Linear weights that convert vector dimensions to 256 dimensions. Please refer to the following chapters for specific instructions on how to use them.

Usage

You can use SentenceTransformers or transformers library to encode text.

Sentence Transformers

from sentence_transformers import SentenceTransformer

# This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively.
# They are defined in `config_sentence_transformers.json`
query_prompt_name = "s2p_query"
queries = [
    "What are some ways to reduce stress?",
    "What are the benefits of drinking green tea?",
]

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