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Update README.md

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  1. README.md +4 -4
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@@ -31,7 +31,7 @@ Then you can use the model like this:
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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- model = SentenceTransformer('dbourget/philai-tsdae-4.8e-cp')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
@@ -54,8 +54,8 @@ def cls_pooling(model_output, attention_mask):
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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- tokenizer = AutoTokenizer.from_pretrained('dbourget/philai-tsdae-4.8e-cp')
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- model = AutoModel.from_pretrained('dbourget/philai-tsdae-4.8e-cp')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -97,7 +97,7 @@ The model was trained with the parameters:
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  Parameters of the fit()-Method:
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  ```
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  {
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- "epochs": 6,
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  "evaluation_steps": 61476,
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  "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
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  "max_grad_norm": 1,
 
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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+ model = SentenceTransformer('dbourget/philai-embeddings-v1.1')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
 
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained('dbourget/philai-embeddings-v1.1')
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+ model = AutoModel.from_pretrained('dbourget/philai-embeddings-v1.1')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
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  Parameters of the fit()-Method:
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  ```
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  {
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+ "epochs": 5,
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  "evaluation_steps": 61476,
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  "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
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  "max_grad_norm": 1,