Simcse Roberta Base Zh

🚀 SimCSE(sup)

SimCSE(sup) is a model for sentence similarity tasks, which can encode sentences into embeddings and compute the cosine similarities between sentences.

🚀 Quick Start

You can use the provided code examples to encode sentences into embeddings and compute the cosine similarities between sentences.

✨ Features

• Support encoding sentences into embeddings.
• Compute the cosine similarities between two sentences.

📦 Data List

The following datasets are all in Chinese:

📦 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.

💻 Usage Examples

Basic Usage

import torch
from transformers import BertTokenizer
from transformers import BertModel
from sklearn.metrics.pairwise import cosine_similarity

# model
simcse_sup_path = "hellonlp/simcse-roberta-base-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])

Advanced Usage

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) #0.7996
#(1.0, '你好吗')
#(0.8247, '你好不好')
#(0.8217, '你现在好吗')
#(0.7976, '你还好吗')
#(0.7918, '你好不好呢')
#(0.712, '你过的好吗')
#(0.6986, '你怎么样')
#(0.6693, '你')
#(0.5442, '你好个鬼')
#(0.4516, '你吃饭了吗')
#(0.4, '我好开心啊')
#(0.29, '我不开心')
#(0.2782, '我吃了一个苹果')

📄 License

This project is licensed under the MIT license.