Update README.md

README.md

@@ -5,6 +5,8 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
+language:
+- en
 ---
 
 # mboth/distil-eng-quora-sentence
@@ -27,7 +29,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('mboth/distil-eng-quora-sentence')
+model = SentenceTransformer('gart-labor/eng-distilBERT-se')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -53,8 +55,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('mboth/distil-eng-quora-sentence')
-model = AutoModel.from_pretrained('mboth/distil-eng-quora-sentence')
+tokenizer = AutoTokenizer.from_pretrained('gart-labor/eng-distilBERT-se')
+model = AutoModel.from_pretrained('gart-labor/eng-distilBERT-se')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -86,8 +88,4 @@ SentenceTransformer(
   (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
 )
-```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->
+```