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---
language:
- zh
license: mit
pipeline_tag: sentence-similarity
---
# SimCSE(sup)
## Model List
The evaluation dataset is in Chinese, and we used the same language model **RoBERTa large** on different methods.
| 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| -| -| -| -| -|
## Uses
You can use our model for encoding sentences into embeddings
```python
import torch
from transformers import BertTokenizer
from transformers import BertModel
from sklearn.metrics.pairwise import cosine_similarity
# model
simcse_sup_path = "hellonlp/simcse-roberta-large-zh"
tokenizer = BertTokenizer.from_pretrained(simcse_sup_path)
MODEL = BertModel.from_pretrained(simcse_sup_path)
def get_vector_simcse(sentence):
"""
预测simcse的语义向量。
"""
input_ids = torch.tensor(tokenizer.encode(sentence)).unsqueeze(0)
output = MODEL(input_ids)
return output.last_hidden_state[:, 0].squeeze(0)
embeddings = get_vector_simcse("武汉是一个美丽的城市。")
print(embeddings.shape)
#torch.Size([768])
```
You can also compute the cosine similarities between two sentences
```python
def get_similarity_two(sentence1, sentence2):
vec1 = get_vector_simcse(sentence1).tolist()
vec2 = get_vector_simcse(sentence2).tolist()
similarity_list = cosine_similarity([vec1], [vec2]).tolist()[0][0]
return similarity_list
sentence1 = '你好吗'
sentence2 = '你还好吗'
result = get_similarity_two(sentence1,sentence2)
print(result)
#0.848331
``` |