---
license: mit
language:
- zh
pipeline_tag: sentence-similarity
---


# 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](https://huggingface.co/BAAI/bge-base-zh)  |  78.61| -| -| -| -| -|
|  [hellonlp/simcse-base-zh](https://huggingface.co/hellonlp/simcse-roberta-base-zh)  |  80.96| -| -| -| -| -|
|  [hellonlp/promcse-base-zh](https://huggingface.co/hellonlp/promcse-bert-base-zh)  |  **81.57**| -| -| -| -| -|



## Data List
The following datasets are all in Chinese.
|          Data          | size(train) | size(valid) | size(test) |
|:-----------------------:|:------------:|:------------:|:------------:|
|   [STS-B](https://link.zhihu.com/?target=https%3A//pan.baidu.com/s/10yfKfTtcmLQ70-jzHIln1A%3Fpwd%3Dgf8y)|  5231|  1458|  1361|
|   [ATEC](https://link.zhihu.com/?target=https%3A//pan.baidu.com/s/1gmnyz9emqOXwaHhSM9CCUA%3Fpwd%3Db17c)| 62477|  20000|  20000|
|   [BQ](https://link.zhihu.com/?target=https%3A//pan.baidu.com/s/1M-e01yyy5NacVPrph9fbaQ%3Fpwd%3Dtis9)|  100000|  10000|  10000|
|   [LCQMC](https://pan.baidu.com/s/16DfE7fHrCkk4e8a2j3SYUg?pwd=bc8w )|  238766| 8802| 12500|
|   [PAWSX](https://link.zhihu.com/?target=https%3A//pan.baidu.com/s/1ox0tJY3ZNbevHDeAqDBOPQ%3Fpwd%3Dmgjn)| 49401| 2000| 2000|
|   [SNLI](https://link.zhihu.com/?target=https%3A//pan.baidu.com/s/1NOgA7JwWghiauwGAUvcm7w%3Fpwd%3Ds75v)| 146828| 2699| 2618|
|   [MNLI](https://link.zhihu.com/?target=https%3A//pan.baidu.com/s/1xjZKtWk3MAbJ6HX4pvXJ-A%3Fpwd%3D2kte)| 122547| 2932| 2397|



## Uses
To use the tool, first install the `promcse` package from [PyPI](https://pypi.org/project/promcse/)
```bash
pip install promcse
```

After installing the package, you can load our model by two lines of code
```python
from promcse import PromCSE
model = PromCSE("hellonlp/promcse-bert-base-zh", "cls", 10)
```

Then you can use our model for encoding sentences into embeddings
```python
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
```python
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]]
```