90 lines
3.4 KiB
Markdown
90 lines
3.4 KiB
Markdown
---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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- semantic-search
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- chinese
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---
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# DMetaSoul/sbert-chinese-qmc-domain-v1
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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：
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- 洗澡用什么香皂好？vs. 洗澡用什么香皂好
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- 大连哪里拍婚纱照好点？ vs. 大连哪里拍婚纱照比较好
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- 银行卡怎样挂失？vs. 银行卡丢了怎么挂失啊？
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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！
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# Usage
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## 1. Sentence-Transformers
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通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
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```
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pip install -U sentence-transformers
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```
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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：
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```python
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from sentence_transformers import SentenceTransformer
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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？"]
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model = SentenceTransformer('DMetaSoul/sbert-chinese-qmc-domain-v1')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## 2. HuggingFace Transformers
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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：
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# Sentences we want sentence embeddings for
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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？"]
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1')
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model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, mean pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation
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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：
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| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** |
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| ------------------------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- |
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| **sbert-chinese-qmc-domain-v1** | 80.90% | 76.63% | 34.51% | 77.06% | 52.96% | 12.98% | 59.48% |
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## Citing & Authors
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E-mail: xiaowenbin@dmetasoul.com |