Update README.md

README.md

@@ -11,17 +11,20 @@ Thus, the vocabulary is 10% of the original, and number of parameters in the who
  
 To get the sentence embeddings, you can  use the following code:
 ```python
+import torch
 from transformers import AutoTokenizer, AutoModel
-tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/LaBSE")
-model = AutoModel.from_pretrained("sentence-transformers/LaBSE")
-sentences = ["Hello World", "Hallo Welt"]
+tokenizer = AutoTokenizer.from_pretrained("cointegrated/LaBSE-en-ru")
+model = AutoModel.from_pretrained("cointegrated/LaBSE-en-ru")
+sentences = ["Hello World", "Привет Мир"]
 encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=64, return_tensors='pt')
 with torch.no_grad():
     model_output = model(**encoded_input)
 embeddings = model_output.pooler_output
 embeddings = torch.nn.functional.normalize(embeddings)
 print(embeddings)
+```
 
 ## Reference:
 Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. [Language-agnostic BERT Sentence Embedding](https://arxiv.org/abs/2007.01852). July 2020
+
 License: [https://tfhub.dev/google/LaBSE/1](https://tfhub.dev/google/LaBSE/1)