Update README.md

README.md

@@ -12,7 +12,7 @@ tags:
 
 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.
 
-The model is based on the sentence-transformers/all-MiniLM-L6-v2 model and was fine-tuned with the eclass-dataset (https://huggingface.co/datasets/gart-labor/eclassTrainST).
+The model is based on the sentence-transformers/all-MiniLM-L6-v2 model and was fine-tuned with the [eclass-dataset](https://huggingface.co/datasets/gart-labor/eclassTrainST).
 
 ## Usage (Sentence-Transformers)
 
@@ -28,7 +28,7 @@ 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('{MODEL_NAME}')
+model = SentenceTransformer('gart-labor/paraphrase-MiniLM-L6-v2-eclass')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -54,8 +54,8 @@ 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('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('gart-labor/paraphrase-MiniLM-L6-v2-eclass')
+model = AutoModel.from_pretrained('gart-labor/paraphrase-MiniLM-L6-v2-eclass')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')