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---
license: mit
language:
- zh
pipeline_tag: sentence-similarity
---
# PromCSE(sup)
## Data List
The following datasets are all in Chinese.
| Data | size(train) | size(valid) | size(test) |
|:----------------------:|:----------:|:----------:|:----------:|
| [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|
| [STS-B](https://link.zhihu.com/?target=https%3A//pan.baidu.com/s/10yfKfTtcmLQ70-jzHIln1A%3Fpwd%3Dgf8y) | 5231| 1458| 1361|
| [*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|
## Model List
The evaluation dataset is in Chinese, and we used the same language model **RoBERTa Large** 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. |
|:-----------------------:|:------------:|:-----------:|:----------|:----------|:----------:|:----------:|
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 78.61| -| -| -| -| -|
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 79.07| -| -| -| -| -|
| [hellonlp/simcse-large-zh](https://huggingface.co/hellonlp/simcse-roberta-large-zh) | 81.32| -| -| -| -| -|
| [hellonlp/promcse-large-zh](https://huggingface.co/hellonlp/promcse-bert-large-zh) | 81.63| -| -| -| -| -|
## 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([1024])
```
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'),
# (0.9324, '你好不好'),
# (0.8945, '你好不好呢'),
# (0.8845, '你还好吗'),
# (0.8382, '你现在好吗'),
# (0.8072, '你过的好吗'),
# (0.7648, '你怎么样'),
# (0.6736, '你'),
# (0.5706, '你吃饭了吗'),
# (0.5417, '你好个鬼'),
# (0.3747, '我好开心啊'),
# (0.0777, '我不开心'),
# (0.0624, '我吃了一个苹果')]
```
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