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--- |
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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-general-v2 |
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此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在百万级语义相似数据集 [SimCLUE](https://github.com/CLUEbenchmark/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**。 |
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注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2-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-general-v2') |
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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-general-v2') |
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model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v2') |
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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-general-v1** | **84.54%** | **82.17%** | 23.80% | 65.94% | 45.52% | 11.52% | 48.51% | |
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| **sbert-chinese-general-v2** | 77.20% | 72.60% | **36.80%** | **76.92%** | **49.63%** | **16.24%** | **63.16%** | |
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这里对比了本模型跟之前我们发布 [sbert-chinese-general-v1](https://huggingface.co/DMetaSoul/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。 |
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## Citing & Authors |
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E-mail: xiaowenbin@dmetasoul.com |
