stella-mrl-large-zh-v3.5-1792d

dunzhang/stella-mrl-large-zh-v3.5-1792d

stella-mrl-large-zh-v3.5-1792d is a sentence similarity model from dunzhang. Use it when you need a score for how close two texts are. It is set up for sentence-transformers. The card lists the license as mit.

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At a glance

Task: Sentence Similarity
Library: sentence-transformers
License: mit
Model type: bert
Access: Public
Created: Feb 27, 2024
Updated: Nov 6, 2024
SHA: 17bb1c32

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From the Hugging Face model README

新闻 | News

[2024-04-06] 开源puff系列模型，专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语。

[2024-02-27] 开源stella-mrl-large-zh-v3.5-1792d模型，支持向量可变维度。

[2024-02-17] 开源stella v3系列、dialogue编码模型和相关训练数据。

[2023-10-19] 开源stella-base-en-v2 使用简单，不需要任何前缀文本。

[2023-10-12] 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，不需要任何前缀文本。

[2023-09-11] 开源stella-base-zh和stella-large-zh

欢迎去本人主页查看最新模型，并提出您的宝贵意见！

1 开源模型

本次开源stella-mrl-large-zh-v3.5-1792d模型，本模型是在stella-large-zh-v3-1792d的基础上使用MRL方法训练而成。其主要特点是可变的向量维度。

2 使用方法

from sentence_transformers import SentenceTransformer
from sklearn.preprocessing import normalize

model = SentenceTransformer("infgrad/stella-mrl-large-zh-v3.5-1792d")
# 注意先不要normalize! 选取前n维后再normalize
vectors = model.encode(["text1", "text2"], normalize_embeddings=False)
print(vectors.shape)  # shape is [2,1792]
# n_dims越大效果越好，但是时空消耗就越大。建议维度选取128的倍数，因为是这么训练的
n_dims = 768
cut_vecs = normalize(vectors[:, :n_dims])

3 不同向量维度的CMTEB得分

stella-mrl-large-zh-v3.5-1792d_1024 代表取前1024维。整体趋势是维度越大效果越好。

Model	Retrieval	STS	PairClassification	Classification	Reranking	Clustering	CMTEB-Score
stella-mrl-large-zh-v3.5-1792d_128	70.01	62.17	87.99	70.67	66.77	53.55	67.16
stella-mrl-large-zh-v3.5-1792d_256	72.19	62.41	88.09	71.22	68.32	53.38	68.02
stella-mrl-large-zh-v3.5-1792d_384	72.77	62.43	88.26	71.34	68.31	53.87	68.25
stella-mrl-large-zh-v3.5-1792d_512	73.11	62.45	88.16	71.46	68.32	53.28	68.29
stella-mrl-large-zh-v3.5-1792d_640	73.27	62.49	88.21	71.46	68.69	53.63	68.42
stella-mrl-large-zh-v3.5-1792d_768	73.38	62.5	88.19	71.49	68.64	53.77	68.47
stella-mrl-large-zh-v3.5-1792d_896	73.37	62.5	88.14	71.51	68.44	54.13	68.49
stella-mrl-large-zh-v3.5-1792d_1024	73.43	62.51	88.16	71.52	68.59	53.43	68.44
stella-mrl-large-zh-v3.5-1792d_1152	73.46	62.49	88.16	71.57	68.55	53.67	68.49
stella-mrl-large-zh-v3.5-1792d_1280	73.48	62.51	88.12	71.55	68.44	53.74	68.48
stella-mrl-large-zh-v3.5-1792d_1408	73.48	62.51	88.14	71.58	68.46	53.69	68.48
stella-mrl-large-zh-v3.5-1792d_1536	73.49	62.5	88.11	71.55	68.5	54.06	68.52
stella-mrl-large-zh-v3.5-1792d_1664	73.56	62.49	88.06	71.56	68.47	54.28	68.56
stella-mrl-large-zh-v3.5-1792d_1792	73.51	62.48	88.09	71.56	68.45	54.39	68.56

上述表格中stella-mrl-large-zh-v3.5-1792d_1792的得分为68.56和榜单68.55得分不一致，原因和权重类型有关，小差异请忽略不计。

Task: Sentence Similarity
Library: sentence-transformers
Type: bert
License: mit
Created: Feb 27, 2024
Updated: Nov 6, 2024