Sentence Similarity
sentence-transformers
PyTorch
Safetensors
bert
mteb
feature-extraction
Eval Results (legacy)
text-embeddings-inference
Instructions to use aspire/acge_text_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aspire/acge_text_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aspire/acge_text_embedding") 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] - Notebooks
- Google Colab
- Kaggle
acge可以只在hf里使用吗?
#6
by rangehow - opened
我注意到示例中只提到了sentence-transformer的使用,同时仓库里存在类似1_Pooling、2_Dense等似乎不是常规hf模型的部件,因此我好奇,是否采用hf使用该模型会忽略掉这些特殊的部分?另外这些类似的文件我在其他的句向量模型中似乎也有发现,可以请教一下它们的来源吗?(比如这是出自一个公共库训练的保存习惯?)