hpprc commited on
Commit
9865b6e
1 Parent(s): c8aa58d

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +3 -3
README.md CHANGED
@@ -33,7 +33,7 @@ Then you can use the model like this:
33
  from sentence_transformers import SentenceTransformer
34
  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"]
35
 
36
- model = SentenceTransformer("unsup-simcse-ja-base")
37
  embeddings = model.encode(sentences)
38
  print(embeddings)
39
  ```
@@ -56,8 +56,8 @@ def cls_pooling(model_output, attention_mask):
56
  sentences = ['This is an example sentence', 'Each sentence is converted']
57
 
58
  # Load model from HuggingFace Hub
59
- tokenizer = AutoTokenizer.from_pretrained("unsup-simcse-ja-base")
60
- model = AutoModel.from_pretrained("unsup-simcse-ja-base")
61
 
62
  # Tokenize sentences
63
  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
33
  from sentence_transformers import SentenceTransformer
34
  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"]
35
 
36
+ model = SentenceTransformer("cl-nagoya/unsup-simcse-ja-base")
37
  embeddings = model.encode(sentences)
38
  print(embeddings)
39
  ```
 
56
  sentences = ['This is an example sentence', 'Each sentence is converted']
57
 
58
  # Load model from HuggingFace Hub
59
+ tokenizer = AutoTokenizer.from_pretrained("cl-nagoya/unsup-simcse-ja-base")
60
+ model = AutoModel.from_pretrained("cl-nagoya/unsup-simcse-ja-base")
61
 
62
  # Tokenize sentences
63
  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')