---
title: promcse-bert-base-zh
canonical_url: "https://www.modelscope.cn/models/danieldong/promcse-bert-base-zh"
md_url: "https://www.modelscope.cn/models/danieldong/promcse-bert-base-zh.md"
repository: danieldong/promcse-bert-base-zh
chinese_name: promcse
last_updated: 2026-01-07
license: mit
pipeline_tag: sentence-similarity
tasks:
  - sentence-similarity
model_type:
  - bert
architectures:
  - BertForCL
library_name:
  - pytorch
frameworks:
  - Pytorch
language:
  - zh
downloads: 17
stars: 0
---

# promcse-bert-base-zh

> promcse-bert-base-zh - danieldong 在 ModelScope 开源的模型。原模型地址为：https://huggingface.co/hellonlp/promcse-bert-base-zh

danieldong/promcse-bert-base-zh 是 ModelScope 魔搭社区上的sentence-similarity模型，采用 mit 许可。

- **Repository**: danieldong/promcse-bert-base-zh
- **License**: mit
- **Tasks**: sentence-similarity
- **Downloads**: 17
- **Stars**: 0
- **Last updated**: 2026-01-07

Source: https://www.modelscope.cn/models/danieldong/promcse-bert-base-zh

---

# 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 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](https://huggingface.co/hellonlp/promcse-bert-base-zh)  |  **81.57**| -| -| -| -| -|




## 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)
# [(1.0, '你好吗'),
#  (0.9167, '你好不好'),
#  (0.8956, '你好不好呢'),
#  (0.8431, '你还好吗'),
#  (0.7919, '你怎么样'),
#  (0.7649, '你现在好吗'),
#  (0.7458, '你过的好吗'),
#  (0.6844, '你好个鬼'),
#  (0.6177, '你'),
#  (0.5654, '你吃饭了吗'),
#  (0.3612, '我好开心啊'),
#  (0.1875, '我不开心'),
#  (0.0866, '我吃了一个苹果')]
```