readme: ---
  language: ja
  license: cc-by-sa-4.0
  tags:
  - sentence-transformers
  - sentence-bert
  - feature-extraction
  - sentence-similarity
  ---
  
  This is a Japanese sentence-BERT model.
  
  日本語用Sentence-BERTモデル（バージョン1）です。
  
  ※: 精度が1.5ポイントほど向上した[バージョン2モデル](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens-v2)もあります。
  
  
  # 解説
  
  https://qiita.com/sonoisa/items/1df94d0a98cd4f209051
  
  
  # 使い方
  
  ```python
  from transformers import BertJapaneseTokenizer, BertModel
  import torch
  
  
  class SentenceBertJapanese:
      def __init__(self, model_name_or_path, device=None):
          self.tokenizer = BertJapaneseTokenizer.from_pretrained(model_name_or_path)
          self.model = BertModel.from_pretrained(model_name_or_path)
          self.model.eval()
  
          if device is None:
              device = "cuda" if torch.cuda.is_available() else "cpu"
          self.device = torch.device(device)
          self.model.to(device)
  
      def _mean_pooling(self, 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)
  
      @torch.no_grad()
      def encode(self, sentences, batch_size=8):
          all_embeddings = []
          iterator = range(0, len(sentences), batch_size)
          for batch_idx in iterator:
              batch = sentences[batch_idx:batch_idx + batch_size]
  
              encoded_input = self.tokenizer.batch_encode_plus(batch, padding="longest", 
                                             truncation=True, return_tensors="pt").to(self.device)
              model_output = self.model(**encoded_input)
              sentence_embeddings = self._mean_pooling(model_output, encoded_input["attention_mask"]).to('cpu')
  
              all_embeddings.extend(sentence_embeddings)
  
          # return torch.stack(all_embeddings).numpy()
          return torch.stack(all_embeddings)
  
  
  MODEL_NAME = "sonoisa/sentence-bert-base-ja-mean-tokens"
  model = SentenceBertJapanese(MODEL_NAME)
  
  sentences = ["暴走したAI", "暴走した人工知能"]
  sentence_embeddings = model.encode(sentences, batch_size=8)
  
  print("Sentence embeddings:", sentence_embeddings)
  ```
  
  
  