|
--- |
|
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-large-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')] |
|
``` |
|
|
|
|
