DMetaSoul/sbert-chinese-qmc-domain-v1
此模型基于 bert-base-chinese 版本 BERT 模型,在百度知道问题匹配数据集(LCQMC)上进行训练调优,适用于开放领域的问题匹配场景,比如:
- 洗澡用什么香皂好?vs. 洗澡用什么香皂好
- 大连哪里拍婚纱照好点? vs. 大连哪里拍婚纱照比较好
- 银行卡怎样挂失?vs. 银行卡丢了怎么挂失啊?
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-qmc-domain-v1')
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-qmc-domain-v1')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1')
# 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-qmc-domain-v1 | 80.90% | 76.63% | 34.51% | 77.06% | 52.96% | 12.98% | 59.48% |
Citing & Authors
E-mail: xiaowenbin@dmetasoul.com
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