Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
text-generation
mteb
Qwen2
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Alibaba-NLP/gte-Qwen2-7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gte-Qwen2-7B-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Alibaba-NLP/gte-Qwen2-7B-instruct with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
测试效果bad case
#40
by jwww123 - opened
query: ['动脉瘤是什么']
documents: ['腘窝囊肿是什么', '动脉瘤是什么?']
score: [[83.64187622070312, 48.1422119140625]]
为啥「腘窝囊肿是什么」的匹配度更高呢?
query: ['动脉瘤是什么?']
documents: ['腘窝囊肿是什么', '动脉瘤是什么?']
[[39.49958801269531, 99.9999771118164]]
query加了个问号后结果就正常了
使用的就是例子的代码,是使用姿势不对还是需要微调呢?
发现是后缀相同的文本,相似度会高很多,gte-Qwen2-1.5B-instruct也有同样的问题
请给一下完整计算相似度的脚
zyznull changed discussion status to closed
zyznull changed discussion status to open