Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
sentence-similarity
text-embeddings-inference
Instructions to use Qwen/Qwen3-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Qwen/Qwen3-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use Qwen/Qwen3-Embedding-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Qwen/Qwen3-Embedding-0.6B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Embedding-0.6B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Embedding-0.6B") - Inference
- Notebooks
- Google Colab
- Kaggle
关于Qwen3 Embedding系列的句子表征
#15
by Jay-v2 - opened
技术报告中的原话:“文本嵌入方面,我们使用具有因果注意力的LLM,在输入序列的末尾附加一个[EOS]标记。最终嵌 入是从与该[EOS]标记对应的最后一层的隐藏状态中得出的”
但是我实测发现,模型并没有新增标记,仅仅就是取的最后一个token的embedding作为句子表征。比如,对文本“你好”进行embedding,那么这个文本的embdding就是“好”这个token对应的embedding。
因此,请问技术报告中描述的是否有误?因为我发现gte-qwen2系列的Embedding模型,确实是取的[EOS]标记对应的最后一层的隐藏状态中得出的,但Qwen3并没有新增标记。
最新版本的模型我们更新了tokenizer.json 文件,会自动在结尾添加<|eodoftext|> token作为embedding。之前的版本需要手动添加<|eodoftext|> token。