
# DMetaSoul/sbert-chinese-qmc-domain-v1

此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在百度知道问题匹配数据集（[LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html)）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](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-domain-v1-distill)，也已经开源啦！

# Usage

## 1. Sentence-Transformers

通过  [sentence-transformers](https://www.SBERT.net) 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：

```python
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](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：

```python
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
{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch bert feature-extraction sentence-similarity transformers semantic-search chinese endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us 

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：


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：


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：


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：



Citing & Authors
----------------


E-mail: xiaowenbin@URL



TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us 

passage: TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us 
