Introduction
This model was trained by richinfoai. Followed Stella and Jasper models, we do distillation training from lier007/xiaobu-embedding-v2, dunzhang/stella-large-zh-v3-1792d and 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 and 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:
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 ]]