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# Qwen3 Embedding
## 核心亮点
Qwen3 Embedding 模型系列是 Qwen 家族最新的专有模型，专为文本嵌入和排序任务设计。它基于 Qwen3 系列的稠密基础模型构建，提供了多种尺寸（0.6B、4B 和 8B）的全面文本嵌入和重排模型。该系列继承了其基础模型出色的多语言能力、长文本理解和推理技能。Qwen3 Embedding 系列在多项文本嵌入和排序任务中取得了显著进展，包括文本检索、代码检索、文本分类、文本聚类和双语文本挖掘。
**出色的通用性**：该嵌入模型在广泛的下游应用评估中取得了最先进的性能。8B 尺寸的嵌入模型在 MTEB 多语言排行榜上排名**第一**（截至 2025 年 6 月 5 日，得分为 **70.58**），而重排模型在各种文本检索场景中也表现出色。
**全面的灵活性**：Qwen3 Embedding 系列为嵌入和重排模型提供了全尺寸范围（从 0.6B 到 8B）的选择，满足兼顾效率与效果的多样化用例需求。开发者可以无缝组合这两个模块。此外，嵌入模型允许在所有维度上灵活定义向量，并且嵌入和重排模型都支持用户自定义指令，以提升特定任务、语言或场景下的性能。
**多语言能力**：得益于 Qwen3 模型的多语言能力，Qwen3 Embedding 系列支持超过 100 种语言。这包括多种编程语言，并提供了强大的多语言、跨语言和代码检索能力。
## Qwen3 Embedding 系列模型列表
| 模型类型 | 模型 | 尺寸 | 层数 | 序列长度 | 嵌入维度 | MRL 支持 | 指令感知 |
|------------------|----------------------|------|--------|-----------------|---------------------|-------------|----------------|
| 文本嵌入 | [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) | 0.6B | 28 | 32K | 1024 | 是 | 是 |
| 文本嵌入 | [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) | 4B | 36 | 32K | 2560 | 是 | 是 |
| 文本嵌入 | [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) | 8B | 36 | 32K | 4096 | 是 | 是 |
| 文本重排 | [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) | 0.6B | 28 | 32K | - | - | 是 |
| 文本重排 | [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) | 4B | 36 | 32K | - | - | 是 |
| 文本重排 | [Qwen3-Reranker-8B](https://huggingface.co/Qwen/Qwen3-Reranker-8B) | 8B | 36 | 32K | - | - | 是 |
### 多语言支持
Qwen3-Embedding 模型系列继承了 Qwen3 基础模型的多语言支持能力。
点击展开支持的语言列表
| 语系 | 语言与方言 |
|---|---|
| 印欧语系 | 英语、法语、葡萄牙语、德语、罗马尼亚语、瑞典语、丹麦语、保加利亚语、俄语、捷克语、希腊语、乌克兰语、西班牙语、荷兰语、斯洛伐克语、克罗地亚语、波兰语、立陶宛语、挪威博克马尔语、新挪威语、波斯语、斯洛文尼亚语、古吉拉特语、拉脱维亚语、意大利语、奥克语、尼泊尔语、马拉地语、白俄罗斯语、塞尔维亚语、卢森堡语、威尼斯语、阿萨姆语、威尔士语、西里西亚语、阿斯图里亚斯语、恰蒂斯加尔语、阿瓦德语、迈蒂利语、博杰普尔语、信德语、爱尔兰语、法罗语、印地语、旁遮普语、孟加拉语、奥里亚语、塔吉克语、东意第绪语、伦巴第语、利古里亚语、西西里语、弗留利语、萨丁尼亚语、加利西亚语、加泰罗尼亚语、冰岛语、托斯克阿尔巴尼亚语、林堡语、达利语、南非荷兰语、马其顿语、僧伽罗语、乌尔都语、摩揭陀语、波斯尼亚语、亚美尼亚语 |
| 汉藏语系 | 中文（简体中文、繁体中文、粤语）、缅甸语 |
| 亚非语系 | 阿拉伯语（标准语、纳季迪语、黎凡特语、埃及语、摩洛哥语、美索不达米亚语、塔伊兹-亚丁语、突尼斯语）、希伯来语、马耳他语 |
| 南岛语系 | 印度尼西亚语、马来语、他加禄语、宿务语、爪哇语、巽他语、米南加保语、巴厘语、班加尔语、邦阿西楠语、伊洛科语、瓦瑞语（菲律宾） |
| 达罗毗荼语系 | 泰米尔语、泰卢固语、卡纳达语、马拉雅拉姆语 |
| 突厥语系 | 土耳其语、北阿塞拜疆语、北乌兹别克语、哈萨克语、巴什基尔语、鞑靼语 |
| 壮侗语系 | 泰语、老挝语 |
| 乌拉尔语系 | 芬兰语、爱沙尼亚语、匈牙利语 |
| 南亚语系 | 越南语、高棉语 |
| 其他 | 日语、韩语、格鲁吉亚语、巴斯克语、海地语、帕皮阿门托语、卡布佛得鲁语、巴布亚皮钦语、斯瓦希里语 |
## 用法
如果使用早于 4.51.0 版本的 Transformers，您可能会遇到以下错误：
```
KeyError: 'qwen3'
```
### 嵌入模型
#### Transformers 用法
```
# 要求 transformers>=4.51.0
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery:{query}'
# 每个 query 必须附带一句描述任务的 instruction
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'What is the capital of China?'),
get_detailed_instruct(task, 'Explain gravity')
]
# 无需为检索文档添加 instruction
documents = [
"The capital of China is Beijing.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-0.6B', padding_side='left')
model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-0.6B')
# 我们建议启用 flash_attention_2 以获得更好的加速和节省内存。
# model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-0.6B', attn_implementation="flash_attention_2", torch_dtype=torch.float16).cuda()
max_length = 8192
# 对输入文本进行 tokenize
batch_dict = tokenizer(
input_texts,
padding=True,
truncation=True,
max_length=max_length,
return_tensors="pt",
)
batch_dict.to(model.device)
with torch.no_grad():
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())
# [[0.7645568251609802, 0.14142508804798126], [0.13549736142158508, 0.5999549627304077]]
```
#### vLLM 用法
```
# 要求 vllm>=0.8.5
import torch
import vllm
from vllm import LLM
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery:{query}'
# 每个 query 必须附带一句描述任务的 instruction
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'What is the capital of China?'),
get_detailed_instruct(task, 'Explain gravity')
]
# 无需为检索文档添加 instruction
documents = [
"The capital of China is Beijing.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
]
input_texts = queries + documents
model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
outputs = model.embed(input_texts)
embeddings = torch.tensor([o.outputs.embedding for o in outputs])
scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())
# [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
```
#### Sentence Transformers 用法
```
# 要求 transformers>=4.51.0
# 要求 sentence-transformers>=2.7.0
from sentence_transformers import SentenceTransformer
# 加载模型
model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B")
# 我们建议启用 flash_attention_2 以获得更好的加速和节省内存，
# 同时将 `padding_side` 设置为 "left"：
# model = SentenceTransformer(
# "Qwen/Qwen3-Embedding-0.6B",
# model_kwargs={"attn_implementation": "flash_attention_2", "device_map": "auto"},
# tokenizer_kwargs={"padding_side": "left"},
# )
# 要 embed 的 query 和文档
queries = [
"What is the capital of China?",
"Explain gravity",
]
documents = [
"The capital of China is Beijing.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
]
with torch.no_grad():
# Encode the queries and documents. Note that queries benefit from using a prompt
# Here we use the prompt called "query" stored under `model.prompts`, but you can
# also pass your own prompt via the `prompt` argument
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
# Compute the (cosine) similarity between the query and document embeddings
similarity = model.similarity(query_embeddings, document_embeddings)
print(similarity)
# tensor([[0.7646, 0.1414], [0.1355, 0.6000]])
```
### 重排模型
#### Transformers 用法
```
# 要求 transformers>=4.51.0
import torch
from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM
def format_instruction(instruction, query, doc):
if instruction is None:
instruction = 'Given a web search query, retrieve relevant passages that answer the query'
output = "
: {instruction}\n: {query}\n: {doc}".format(instruction=instruction,query=query, doc=doc)
return output
def process_inputs(pairs):
inputs = tokenizer(
pairs, padding=False, truncation='longest_first',
return_attention_mask=False, max_length=max_length - len(prefix_tokens) - len(suffix_tokens)
)
for i, ele in enumerate(inputs['input_ids']):
inputs['input_ids'][i] = prefix_tokens + ele + suffix_tokens
inputs = tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=max_length)
for key in inputs:
inputs[key] = inputs[key].to(model.device)
return inputs
@torch.no_grad()
def compute_logits(inputs, **kwargs):
batch_scores = model(**inputs).logits[:, -1, :]
true_vector = batch_scores[:, token_true_id]
false_vector = batch_scores[:, token_false_id]
batch_scores = torch.stack([false_vector, true_vector], dim=1)
batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
scores = batch_scores[:, 1].exp().tolist()
return scores
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Reranker-0.6B", padding_side='left')
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Reranker-0.6B").eval()
# 我们建议启用 flash_attention_2 以获得更好的加速和节省内存。
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Reranker-0.6B", torch_dtype=torch.float16, attn_implementation="flash_attention_2").cuda().eval()
token_false_id = tokenizer.convert_tokens_to_ids("no")
token_true_id = tokenizer.convert_tokens_to_ids("yes")
max_length = 8192
prefix = "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n"
suffix = "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n"
prefix_tokens = tokenizer.encode(prefix, add_special_tokens=False)
suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = ["What is the capital of China?",
"Explain gravity",
]
documents = [
"The capital of China is Beijing.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
]
pairs = [format_instruction(task, query, doc) for query, doc in zip(queries, documents)]
# 对输入文本进行 tokenize
inputs = process_inputs(pairs)
scores = compute_logits(inputs)
print("scores: ", scores)
```
#### vLLM 用法
```
# 要求 vllm>=0.8.5
import logging
from typing import Dict, Optional, List
import json
import logging
import torch
from transformers import AutoTokenizer, is_torch_npu_available
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import destroy_model_parallel
import gc
import math
from vllm.inputs.data import TokensPrompt
def format_instruction(instruction, query, doc):
text = [
{"role": "system", "content": "Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\"."},
{"role": "user", "content": f": {instruction}\n\n: {query}\n\n: {doc}"}
]
return text
def process_inputs(pairs, instruction, max_length, suffix_tokens):
messages = [format_instruction(instruction, query, doc) for query, doc in pairs]
messages = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=False, enable_thinking=False
)
messages = [ele[:max_length] + suffix_tokens for ele in messages]
messages = [TokensPrompt(prompt_token_ids=ele) for ele in messages]
return messages
def compute_logits(model, messages, sampling_params, true_token, false_token):
outputs = model.generate(messages, sampling_params, use_tqdm=False)
scores = []
for i in range(len(outputs)):
final_logits = outputs[i].outputs[0].logprobs[-1]
token_count = len(outputs[i].outputs[0].token_ids)
if true_token not in final_logits:
true_logit = -10
else:
true_logit = final_logits[true_token].logprob
if false_token not in final_logits:
false_logit = -10
else:
false_logit = final_logits[false_token].logprob
true_score = math.exp(true_logit)
false_score = math.exp(false_logit)
score = true_score / (true_score + false_score)
scores.append(score)
return scores
number_of_gpu = torch.cuda.device_count()
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Reranking-4B')
model = LLM(model='Qwen/Qwen3-Reranking-0.6B', tensor_parallel_size=number_of_gpu, max_model_len=10000, enable_prefix_caching=True, gpu_memory_utilization=0.8)
tokenizer.padding_side = "left"
tokenizer.pad_token = tokenizer.eos_token
suffix = "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n"
max_length=8192
suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)
true_token = tokenizer("yes", add_special_tokens=False).input_ids[0]
false_token = tokenizer("no", add_special_tokens=False).input_ids[0]
sampling_params = SamplingParams(temperature=0,
max_tokens=1,
logprobs=20,
allowed_token_ids=[true_token, false_token],
)
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = ["What is the capital of China?",
"Explain gravity",
]
documents = [
"The capital of China is Beijing.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
]
pairs = list(zip(queries, documents))
inputs = process_inputs(pairs, task, max_length-len(suffix_tokens), suffix_tokens)
scores = compute_logits(model, inputs, sampling_params, true_token, false_token)
print('scores', scores)
destroy_model_parallel()
```
📌 **提示**：我们建议开发者根据其特定的场景、任务和语言来自定义 `instruct`。我们的测试表明，在大多数检索场景中，如果不在查询端使用 `instruct`，检索性能可能会下降约 1% 到 5%。
有关更多用法示例，请参阅 [示例]() 部分的代码。
## 训练
训练 Qwen3-Embedding 模型的代码和说明可以在 [训练文档](docs/training) 中找到。
## 评估
用于复现以下结果的代码可在 [评估]() 部分找到。
### MTEB（多语言）
| 模型 | 尺寸 | 平均分（任务） | 平均分（类型） | 双语文本挖掘 | 分类 | 聚类 | 指令检索 | 多类分类 | 成对分类 | 重排 | 检索 | STS |
|----------------------------------|:-------:|:-------------:|:-------------:|:--------------:|:--------:|:--------:|:--------------:|:---------------:|:--------------:|:--------:|:--------:|:------:|
| NV-Embed-v2 | 7B | 56.29 | 49.58 | 57.84 | 57.29 | 40.80 | 1.04 | 18.63 | 78.94 | 63.82 | 56.72 | 71.10|
| GritLM-7B | 7B | 60.92 | 53.74 | 70.53 | 61.83 | 49.75 | 3.45 | 22.77 | 79.94 | 63.78 | 58.31 | 73.33|
| BGE-M3 | 0.6B | 59.56 | 52.18 | 79.11 | 60.35 | 40.88 | -3.11 | 20.1 | 80.76 | 62.79 | 54.60 | 74.12|
| multilingual-e5-large-instruct | 0.6B | 63.22 | 55.08 | 80.13 | 64.94 | 50.75 | -0.40 | 22.91 | 80.86 | 62.61 | 57.12 | 76.81|
| gte-Qwen2-1.5B-instruct | 1.5B | 59.45 | 52.69 | 62.51 | 58.32 | 52.05 | 0.74 | 24.02 | 81.58 | 62.58 | 60.78 | 71.61|
| gte-Qwen2-7b-Instruct | 7B | 62.51 | 55.93 | 73.92 | 61.55 | 52.77 | 4.94 | 25.48 | 85.13 | 65.55 | 60.08 | 73.98|
| text-embedding-3-large | - | 58.93 | 51.41 | 62.17 | 60.27 | 46.89 | -2.68 | 22.03 | 79.17 | 63.89 | 59.27 | 71.68|
| Cohere-embed-multilingual-v3.0 | - | 61.12 | 53.23 | 70.50 | 62.95 | 46.89 | -1.89 | 22.74 | 79.88 | 64.07 | 59.16 | 74.80|
| gemini-embedding-exp-03-07 | - | 68.37 | 59.59 | 79.28 | 71.82 | 54.59 | 5.18 | **29.16** | 83.63 | 65.58 | 67.71 | 79.40|
| **Qwen3-Embedding-0.6B** | 0.6B | 64.33 | 56.00 | 72.22 | 66.83 | 52.33 | 5.09 | 24.59 | 80.83 | 61.41 | 64.64 | 76.17|
| **Qwen3-Embedding-4B** | 4B | 69.45 | 60.86 | 79.36 | 72.33 | 57.15 | **11.56** | 26.77 | 85.05 | 65.08 | 69.60 | 80.86|
| **Qwen3-Embedding-8B** | 8B | **70.58** | **61.69** | **80.89** | **74.00** | **57.65** | 10.06 | 28.66 | **86.40** | **65.63** | **70.88** | **81.08** |
### MTEB（英文 v2）
| MTEB 英文 / 模型 | 参数量 | 平均分（任务） | 平均分（类型） | 分类 | 聚类 | 成对分类 | 重排 | 检索 | STS | 摘要 |
|--------------------------------|:--------:|:------------:|:------------:|:--------:|:--------:|:-------------:|:---------:|:--------:|:-------:|:-------:|
| multilingual-e5-large-instruct | 0.6B | 65.53 | 61.21 | 75.54 | 49.89 | 86.24 | 48.74 | 53.47 | 84.72 | 29.89 |
| NV-Embed-v2 | 7.8B | 69.81 | 65.00 | 87.19 | 47.66 | 88.69 | 49.61 | 62.84 | 83.82 | 35.21 |
| GritLM-7B | 7.2B | 67.07 | 63.22 | 81.25 | 50.82 | 87.29 | 49.59 | 54.95 | 83.03 | 35.65 |
| gte-Qwen2-1.5B-instruct | 1.5B | 67.20 | 63.26 | 85.84 | 53.54 | 87.52 | 49.25 | 50.25 | 82.51 | 33.94 |
| stella_en_1.5B_v5 | 1.5B | 69.43 | 65.32 | 89.38 | 57.06 | 88.02 | 50.19 | 52.42 | 83.27 | 36.91 |
| gte-Qwen2-7B-instruct | 7.6B | 70.72 | 65.77 | 88.52 | 58.97 | 85.9 | 50.47 | 58.09 | 82.69 | 35.74 |
| gemini-embedding-exp-03-07 | - | 73.3 | 67.67 | 90.05 | **59.39** | **87.7** | 48.59 | 64.35 | 85.29 | **38.28** |
| **Qwen3-Embedding-0.6B** | 0.6B | 70.70 | 64.88 | 85.76 | 54.05 | 84.37 | 48.18 | 61.83 | 86.57 | 33.43 |
| **Qwen3-Embedding-4B** | 4B | 74.60 | 68.10 | 89.84 | 57.51 | 87.01 | 50.76 | 68.46 | **88.72** | 34.39 |
| **Qwen3-Embedding-8B** | 8B | **75.22** | **68.71** | **90.43** | 58.57 | 87.52 | **51.56** | **69.44** | 88.58 | 34.83 |
### C-MTEB（MTEB 中文）
| C-MTEB | 参数量 | 平均分（任务） | 平均分（类型） | 分类 | 聚类 | 成对分类 | 重排 | 检索 | STS |
|------------------|--------|------------|------------|--------|--------|-------------|---------|-------|-------|
| multilingual-e5-large-instruct | 0.6B | 58.08 | 58.24 | 69.80 | 48.23 | 64.52 | 57.45 | 63.65 | 45.81 |
| bge-multilingual-gemma2 | 9B | 67.64 |68.52 | 75.31 | 59.30 | 86.67 | 68.28 | 73.73 | 55.19 |
| gte-Qwen2-1.5B-instruct | 1.5B | 67.12 | 67.79 | 72.53 | 54.61 | 79.5 | 68.21 | 71.86 | 60.05 |
| gte-Qwen2-7B-instruct | 7.6B | 71.62 | 72.19 | 75.77 | 66.06 | 81.16 | 69.24 | 75.70 | 65.20 |
| ritrieve_zh_v1 | 0.3B | 72.71 | 73.85 | 76.88 | 66.5 | **85.98** | **72.86** | 76.97 | **63.92** |
| **Qwen3-Embedding-0.6B** | 0.6B | 66.33 | 67.45 | 71.40 | 68.74 | 76.42 | 62.58 | 71.03 | 54.52 |
| **Qwen3-Embedding-4B** | 4B | 72.27 | 73.51 | 75.46 | 77.89 | 83.34 | 66.05 | 77.03 | 61.26 |
| **Qwen3-Embedding-8B** | 8B | **73.84** | **75.00** | **76.97** | **80.08** | 84.23 | 66.99 | **78.21** | 63.53 |
### 重排模型
| 模型 | 参数量 | MTEB-R | CMTEB-R | MMTEB-R | MLDR | MTEB-Code | FollowIR |
|------------------------------------|--------|---------|---------|---------|--------|-----------|----------|
| **Qwen3-Embedding-0.6B** | 0.6B | 61.82 | 71.02 | 64.64 | 50.26 | 75.41 | 5.09 |
| Jina-multilingual-reranker-v2-base | 0.3B | 58.22 | 63.37 | 63.73 | 39.66 | 58.98 | -0.68 |
| gte-multilingual-reranker-base | 0.3B | 59.51 | 74.08 | 59.44 | 66.33 | 54.18 | -1.64 |
| BGE-reranker-v2-m3 | 0.6B | 57.03 | 72.16 | 58.36 | 59.51 | 41.38 | -0.01 |
| **Qwen3-Reranker-0.6B** | 0.6B | 65.80 | 71.31 | 66.36 | 67.28 | 73.42 | 5.41 |
| **Qwen3-Reranker-4B** | 4B | **69.76** | 75.94 | 72.74 | 69.97 | 81.20 | **14.84** |
| **Qwen3-Reranker-8B** | 8B | 69.02 | **77.45** | **72.94** | **70.19** | **81.22** | 8.05 |
## 引用
如果您觉得我们的工作有帮助，欢迎引用我们。
```
@article{qwen3embedding,
title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
journal={arXiv preprint arXiv:2506.05176},
year={2025}
}
```