Update README.md

README.md

@@ -4,32 +4,29 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
+- transformers
 
 ---
 
-# {MODEL_NAME}
+# Setfit Classification Model ON Conversion Dataset With mpnet sbert Model as Base
 
-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.
+This is a Setfit Model with the L6 model as a Base for classification.
 
 <!--- Describe your model here -->
 
-## Usage (Sentence-Transformers)
+## Usage (Setfit)
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
 ```
-pip install -U sentence-transformers
+pip install setfit
 ```
 
 Then you can use the model like this:
 
 ```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
+from setfit import SetFitModel
+model = SetFitModel.from_pretrained("nayan06/binary-classifier-conversion-intent-1.1-mpnet")
+prediction = model(['i want to buy thing'])
 ```
 
 
@@ -77,12 +74,15 @@ Parameters of the fit()-Method:
 ## Full Model Architecture
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
-  (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})
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
   (2): Normalize()
 )
 ```
 
+## Dataset Used
+https://huggingface.co/datasets/nayan06/conversion1.0
+
 ## Citing & Authors
 
 <!--- Describe where people can find more information -->