---
title: nlp_structbert_sentence-similarity_chinese-base
canonical_url: "https://www.modelscope.cn/models/damo/nlp_structbert_sentence-similarity_chinese-base"
md_url: "https://www.modelscope.cn/models/damo/nlp_structbert_sentence-similarity_chinese-base.md"
repository: damo/nlp_structbert_sentence-similarity_chinese-base
chinese_name: "StructBERT文本相似度-中文-通用-base"
last_updated: 2022-11-14
license: "Apache License 2.0"
pipeline_tag: sentence-similarity
tasks:
  - sentence-similarity
model_type:
  - sbert
library_name:
  - pytorch
frameworks:
  - pytorch
language:
  - cn
domain:
  - nlp
downloads: 470610
stars: 70
tags:
  - transformer
  - Alibaba
  - sentence-similarity
  - "文本相似度"
  - "文本对匹配"
---

# nlp_structbert_sentence-similarity_chinese-base

> nlp_structbert_sentence-similarity_chinese-base - damo 在 ModelScope 开源的模型。StructBERT文本相似度-中文-通用-base是在structbert-base-chinese预训练模型的基础上，用atec、bq_corpus、chineseSTS、lcqmc、paws-x-zh五个数据集（52.5w条数据，正负比例0.48:0.52）训练出来的相似度匹配模型。由于license权限问题，目前只上传了BQ_Corpus、chineseSTS…

damo/nlp_structbert_sentence-similarity_chinese-base 是 ModelScope 魔搭社区上的sentence-similarity模型，采用 Apache License 2.0 许可。

- **Repository**: damo/nlp_structbert_sentence-similarity_chinese-base
- **License**: Apache License 2.0
- **Tasks**: sentence-similarity
- **Tags**: transformer, Alibaba, sentence-similarity, 文本相似度, 文本对匹配
- **Downloads**: 470610
- **Stars**: 70
- **Last updated**: 2022-11-14

Source: https://www.modelscope.cn/models/damo/nlp_structbert_sentence-similarity_chinese-base

---

# StructBERT中文文本相似度模型介绍

StructBERT中文文本相似度模型是在structbert-base-chinese预训练模型的基础上，用atec、bq_corpus、chineseSTS、lcqmc、paws-x-zh五个数据集（52.5w条数据，正负比例0.48:0.52）训练出来的相似度匹配模型。由于license权限问题，目前只上传了BQ_Corpus、chineseSTS、LCQMC这三个数据集。

其他数据集：
- ATEC：https://dc.cloud.alipay.com/index#/topic/intro?id=3 
- paws-x-zh：https://aistudio.baidu.com/aistudio/competition/detail/45/0/task-definition

## 模型描述

模型基于Structbert-base-chinese，按照BERT文本对分类的方式，在atec、bq_corpus、chineseSTS、lcqmc、paws-x-zh五个数据集（52.5w条数据）上进行微调。

![模型结构](model.jpg)

## 期望模型使用方式以及适用范围
你可以使用StructBERT中文文本相似度模型，对通用领域的文本相似度任务进行推理。
输入形如（文本A，文本B）的文本对数据，模型会给出该文本对的是否相似的标签（不相似, 相似）以及相应的概率。

### 如何使用

#### 环境安装
在安装完成ModelScope-lib，请参考  [modelscope环境安装](https://modelscope.cn/docs/%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85) 。

#### 推理代码范例
```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

similarity_pipeline = pipeline(Tasks.sentence_similarity, 'damo/nlp_structbert_sentence-similarity_chinese-base', model_revision='master')
similarity_pipeline(input=('商务职业学院和财经职业学院哪个好？', '商务职业学院商务管理在哪个校区？'))

```

#### Finetune/训练代码范例
```python
import os.path as osp
from modelscope.trainers import build_trainer
from modelscope.msdatasets import MsDataset
from modelscope.utils.hub import read_config


model_id = 'damo/nlp_structbert_sentence-similarity_chinese-base'
dataset_id = 'BQ_Corpus'

WORK_DIR = 'workspace'

cfg = read_config(model_id)
cfg.train.max_epochs = 2
cfg.train.work_dir = WORK_DIR
cfg.train.hooks = cfg.train.hooks = [{
        'type': 'TextLoggerHook',
        'interval': 100
    }]
cfg_file = osp.join(WORK_DIR, 'train_config.json')
cfg.dump(cfg_file)

train_dataset = MsDataset.load(dataset_id, namespace='DAMO_NLP', split='train').to_hf_dataset()
eval_dataset = MsDataset.load(dataset_id, namespace='DAMO_NLP', split='validation').to_hf_dataset()

# map float to index
def map_labels(examples):
    map_dict = {0: "不相似", 1: "相似"}
    examples['label'] = map_dict[int(examples['label'])]
    return examples

train_dataset = train_dataset.map(map_labels)
eval_dataset = eval_dataset.map(map_labels)

kwargs = dict(
    model=model_id,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    cfg_file=cfg_file)


trainer = build_trainer(name='nlp-base-trainer',default_args=kwargs)

print('===============================================================')
print('pre-trained model loaded, training started:')
print('===============================================================')

trainer.train()

print('===============================================================')
print('train success.')
print('===============================================================')

for i in range(cfg.train.max_epochs):
    eval_results = trainer.evaluate(f'{WORK_DIR}/epoch_{i+1}.pth')
    print(f'epoch {i} evaluation result:')
    print(eval_results)


print('===============================================================')
print('evaluate success')
print('===============================================================')
```

### 模型局限性以及可能的偏差
模型训练数据有限，不能包含所有行业，因此在特定行业数据上，效果可能存在一定偏差。

## 训练数据介绍

1. ATEC：蚂蚁金服比赛数据集。
2. BQ_Corpus：请参考关联数据集DAMO_NLP/BQ_Corpus
3. ChineseSTS：请参考关联数据集DAMO_NLP/ChineseSTS
4. LCQMC：请参考关联数据集DAMO_NLP/LCQMC
5. paws-x-zh：谷歌发布的包含7种语言释义对的数据集，请参考 https://aistudio.baidu.com/aistudio/competition/detail/45/0/task-definition

## 数据评估及结果

| 数据集   | Avg    | ATEC  | bq_corpus | ChineseSTS | LCQMC | paws-x-zh |
| -------- | ------ | ----- | --------- | ---------- | ----- | --------- |
| Accuracy | 0.8831 | 0.8662 | 0.8668     | 0.9736      | 0.8959 | 0.8629     |

### 相关论文以及引用信息

```bib
@article{wang2019structbert,
  title={Structbert: Incorporating language structures into pre-training for deep language understanding},
  author={Wang, Wei and Bi, Bin and Yan, Ming and Wu, Chen and Bao, Zuyi and Xia, Jiangnan and Peng, Liwei and Si, Luo},
  journal={arXiv preprint arXiv:1908.04577},
  year={2019}
}
```