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DMetaSoul/sbert-chinese-general-v2

此模型基于 bert-base-chinese 版本 BERT 模型,在百万级语义相似数据集 SimCLUE 上进行训练,适用于通用语义匹配场景,从效果来看该模型在各种任务上泛化能力更好。

注:此模型的轻量化版本,也已经开源啦!

Usage

1. Sentence-Transformers

通过 sentence-transformers 框架来使用该模型,首先进行安装:

pip install -U sentence-transformers

然后使用下面的代码来载入该模型并进行文本表征向量的提取:

from sentence_transformers import SentenceTransformer
sentences = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"]

model = SentenceTransformer('DMetaSoul/sbert-chinese-general-v2')
embeddings = model.encode(sentences)
print(embeddings)

2. HuggingFace Transformers

如果不想使用 sentence-transformers 的话,也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取:

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ["我的儿子!他猛然间喊道,我的儿子在哪儿?", "我的儿子呢!他突然喊道,我的儿子在哪里?"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-general-v2')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v2')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

Evaluation

该模型在公开的几个语义匹配数据集上进行了评测,计算了向量相似度跟真实标签之间的相关性系数:

csts_dev csts_test afqmc lcqmc bqcorpus pawsx xiaobu
sbert-chinese-general-v1 84.54% 82.17% 23.80% 65.94% 45.52% 11.52% 48.51%
sbert-chinese-general-v2 77.20% 72.60% 36.80% 76.92% 49.63% 16.24% 63.16%

这里对比了本模型跟之前我们发布 sbert-chinese-general-v1 之间的差异,可以看到本模型在多个任务上的泛化能力更好。

Citing & Authors

E-mail: xiaowenbin@dmetasoul.com

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