Add Usage

README.md

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+---
+language: "ja"
+license: "cc-by-sa-3.0"
+tags:
+- sentence-transformers
+- sentence-bert
+- feature-extraction
+- sentence-similarity
+---
+
+This is a Japanese sentence-BERT model.
+
+日本語用Sentence-BERTモデルです。
+
+# 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)
+
+
+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)
+```
+
+