Update README.md

README.md

@@ -31,7 +31,7 @@ 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('dbourget/philai-tsdae-4.8e-cp')
+model = SentenceTransformer('dbourget/philai-embeddings-v1.1')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -54,8 +54,8 @@ def cls_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('dbourget/philai-tsdae-4.8e-cp')
-model = AutoModel.from_pretrained('dbourget/philai-tsdae-4.8e-cp')
+tokenizer = AutoTokenizer.from_pretrained('dbourget/philai-embeddings-v1.1')
+model = AutoModel.from_pretrained('dbourget/philai-embeddings-v1.1')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -97,7 +97,7 @@ The model was trained with the parameters:
 Parameters of the fit()-Method:
 ```
 {
-    "epochs": 6,
+    "epochs": 5,
     "evaluation_steps": 61476,
     "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
     "max_grad_norm": 1,