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---
license: mit
language:
- zh
pipeline_tag: sentence-similarity
---
## Model List
The evaluation dataset is in Chinese, and we used the same language model **RoBERTa base** on different methods. In addition, considering that the test set of some datasets is small, which may lead to a large deviation in evaluation accuracy, the evaluation data here uses train, valid and test at the same time, and the final evaluation result adopts the **weighted average (w-avg)** method.
| 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-v1.0](https://huggingface.co/hellonlp/promcse-bert-base-zh) | **81.57**| -| -| -| -| -|
| [hellonlp/promcse-base-zh-v1.1](https://huggingface.co/hellonlp/promcse-bert-base-zh) | **82.02**| -| -| -| -| -|
## 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-v1.1", "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)
# [(1.0, '你好吗'),
# (0.9029, '你好不好'),
# (0.8945, '你好不好呢'),
# (0.8478, '你还好吗'),
# (0.7746, '你现在好吗'),
# (0.7607, '你过的好吗'),
# (0.7399, '你怎么样'),
# (0.5967, '你'),
# (0.5395, '你好个鬼'),
# (0.5262, '你吃饭了吗'),
# (0.3608, '我好开心啊'),
# (0.2308, '我不开心'),
# (0.0626, '我吃了一个苹果')]
```
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