---
datasets:
- BAAI/Infinity-Instruct
- opencsg/chinese-fineweb-edu
language:
- zh
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: mit
---
## Introduction

This model was trained by [richinfoai](https://www.richinfo.cn/).
Followed [Stella and Jasper models](https://arxiv.org/pdf/2412.19048), we do distillation training from
[lier007/xiaobu-embedding-v2](https://huggingface.co/lier007/xiaobu-embedding-v2),
[dunzhang/stella-large-zh-v3-1792d](https://huggingface.co/dunzhang/stella-large-zh-v3-1792d)
and [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2).
Thanks to their outstanding performance, our model has achieved excellent results on MTEB(cmn, v1).

We believe this model once again demonstrates the effectiveness of distillation learning.
In the future, we will train more bilingual vector models based on various excellent vector training methods.

## Methods

### Stage1

We use [BAAI/Infinity-Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct)
and [opencsg/chinese-fineweb-edu](https://huggingface.co/datasets/opencsg/chinese-fineweb-edu)
as training data to do a distillation from the above three models.
In this stage, we only use cosine-loss.

### Stage2

The objective of stage2 is reducing dimensions.
We use the same training data as the stage1 with `similarity loss`. After stage2, the dimensions of our model is 1792.

## Usage

This model does not need instructions and you can use it in `SentenceTransformer`:

```python
import os

os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
from sentence_transformers import SentenceTransformer

text_encoder = SentenceTransformer("richinfoai/ritrieve_zh_v1")
texts = [
    "什么是人工智能",
    "介绍一下主流的LLM",
    "人工智能（AI）是模拟人类智能的计算机系统，能够执行学习、推理和决策等任务。它通过算法和大数据实现自动化，广泛应用于各行各业。"
]
vectors = text_encoder.encode(texts, normalize_embeddings=True)
print(vectors @ vectors.T)
# [[0.9999999  0.67707014 0.91421044]
#  [0.67707014 0.9999998  0.6353945 ]
#  [0.91421044 0.6353945  1.0000001 ]]

```