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Update README.md

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  1. README.md +3 -3
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@@ -32,7 +32,7 @@ Then you can use the model like this:
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  from sentence_transformers import SentenceTransformer
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  sentences = ["こんにちは、世界!", "文埋め込み最高!文埋め込み最高と叫びなさい", "極度乾燥しなさい"]
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- model = SentenceTransformer("sup-simcse-ja-base")
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
@@ -53,8 +53,8 @@ def cls_pooling(model_output, attention_mask):
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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- tokenizer = AutoTokenizer.from_pretrained("sup-simcse-ja-base")
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- model = AutoModel.from_pretrained("sup-simcse-ja-base")
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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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  from sentence_transformers import SentenceTransformer
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  sentences = ["こんにちは、世界!", "文埋め込み最高!文埋め込み最高と叫びなさい", "極度乾燥しなさい"]
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+ model = SentenceTransformer("cl-nagoya/sup-simcse-ja-base")
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
 
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained("cl-nagoya/sup-simcse-ja-base")
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+ model = AutoModel.from_pretrained("cl-nagoya/sup-simcse-ja-base")
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')