minor changes in model names thru out the text

README.md

@@ -16,7 +16,7 @@ widget:
 
 # SBERT_PQ
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 312 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps texts & questions to a 312 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
 <!--- Describe your model here -->
 
@@ -32,9 +32,9 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["Кошка ловит мышку.", "Чем занята кошка?"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('inkoziev/sbert_pq')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -60,8 +60,8 @@ 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('inkoziev/sbert_pq')
+model = AutoModel.from_pretrained('inkoziev/sbert_pq')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -133,4 +133,12 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+```
+@MISC{rugpt_chitchat,
+    author  = {Ilya Koziev},
+    title   = {Texts & Questions Relevancy Model},
+    url     = {https://huggingface.co/inkoziev/sbert_pq},
+    year    = 2022
+}
+```
+