L-12 -> L12

README.md

@@ -9,7 +9,7 @@ tags:
 pipeline_tag: sentence-similarity
 ---
 
-# sentence-transformers/msmarco-MiniLM-L-12-v3
+# sentence-transformers/msmarco-MiniLM-L12-v3
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -29,7 +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('sentence-transformers/msmarco-MiniLM-L-12-v3')
+model = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L12-v3')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -55,8 +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('sentence-transformers/msmarco-MiniLM-L-12-v3')
-model = AutoModel.from_pretrained('sentence-transformers/msmarco-MiniLM-L-12-v3')
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/msmarco-MiniLM-L12-v3')
+model = AutoModel.from_pretrained('sentence-transformers/msmarco-MiniLM-L12-v3')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -78,7 +78,7 @@ print(sentence_embeddings)
 
 
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-MiniLM-L-12-v3)
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-MiniLM-L12-v3)