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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ }
README.md ADDED
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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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+
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+ # DMetaSoul/sbert-chinese-general-v2
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+
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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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+
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+ # Usage
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+
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+ ## 1. Sentence-Transformers
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+
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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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+ ```
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+ pip install -U sentence-transformers
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+ ```
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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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+
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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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+
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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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+
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+ ## 2. HuggingFace Transformers
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+
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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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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModel
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+ import torch
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+
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+
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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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+
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ print("Sentence embeddings:")
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+ print(sentence_embeddings)
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+ ```
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+
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+ ## Evaluation
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+
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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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+
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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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+
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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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+
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+ ## Citing & Authors
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+
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+ xiaowenbin@[元灵数智](https://www.dmetasoul.com/)
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