transformers pytorch bert sentence-similarity zh license:mit endpoints_compatible text-embeddings-inference region:us
---
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')]
```

