Update README.md

README.md

@@ -7,11 +7,11 @@ tags:
 - transformers
 ---
 
-# {MODEL_NAME}
+# Gold-only BERT STSb
 
 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.
 
-<!--- Describe your model here -->
+It is used as a demo model within the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp), for the chapter on [In-domain Data Augmentation with BERT](https://www.pinecone.io/learn/data-augmentation/).
 
 ## Usage (Sentence-Transformers)
 
@@ -27,7 +27,7 @@ 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('bert-stsb-gold')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -53,8 +53,8 @@ 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('bert-stsb-gold')
+model = AutoModel.from_pretrained('bert-stsb-gold')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -70,15 +70,6 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
-
-
-## Evaluation Results
-
-<!--- 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})
-
-
 ## Training
 The model was trained with the parameters:
 
@@ -119,7 +110,3 @@ SentenceTransformer(
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
 )
 ```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->