初始化项目，由ModelHub XC社区提供模型

Model: DMetaSoul/sbert-chinese-qmc-domain-v1
Source: Original Platform

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- semantic-search
+- chinese
+---
+
+# 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