Update README.md

README.md

@@ -8,7 +8,7 @@ tags:
 
 ---
 
-# {MODEL_NAME}
+# BERT/MPnet base model (uncased)
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -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('abbasgolestani/ag-nli-bert-mpnet-base-uncased-sentence-similarity-v1')
 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('abbasgolestani/ag-nli-bert-mpnet-base-uncased-sentence-similarity-v1')
+model = AutoModel.from_pretrained('abbasgolestani/ag-nli-bert-mpnet-base-uncased-sentence-similarity-v1')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -77,7 +77,7 @@ print(sentence_embeddings)
 
 <!--- Describe how your model was evaluated -->
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+This model has been evaluated on a local dataset with 1000 sentence pairs. This algorithm achieved  accuracy of 82% on this dataset.
 
 
 ## Training