PromCSE(sup)

Model List

The evaluation dataset is in Chinese, and we used the same language model RoBERTa base on different methods.

Model	STS-B(w-avg)	ATEC	BQ	LCQMC	PAWSX	Avg.
BERT-Whitening	65.27	-	-	-	-	-
SimBERT	70.01	-	-	-	-	-
SBERT-Whitening	71.75	-	-	-	-	-
BAAI/bge-base-zh	78.61	-	-	-	-	-
hellonlp/simcse-base-zh	80.96	-	-	-	-	-
hellonlp/promcse-base-zh	81.57	-	-	-	-	-

Uses

To use the tool, first install the promcse package from PyPI

pip install promcse

After installing the package, you can load our model by two lines of code

from promcse import PromCSE
model = PromCSE("hellonlp/promcse-bert-base-zh", "cls", 10)

Then you can use our model for encoding sentences into embeddings

embeddings = model.encode("武汉是一个美丽的城市。")
print(embeddings.shape)
#torch.Size([768])

Compute the cosine similarities between two groups of sentences

sentences_a = ['你好吗']
sentences_b = ['你怎么样','我吃了一个苹果','你过的好吗','你还好吗','你',
               '你好不好','你好不好呢','我不开心','我好开心啊', '你吃饭了吗',
               '你好吗','你现在好吗','你好个鬼']
similarities = model.similarity(sentences_a, sentences_b)
print(similarities)
#[[0.7818036 , 0.0754933 , 0.751326  , 0.83766925, 0.6286671 ,
#  0.917025  , 0.8861941 , 0.20904644, 0.41348672, 0.5587336 ,
#  1.0000001 , 0.7798723 , 0.70388055]]