Update README.md

README.md

@@ -32,10 +32,11 @@ The training code is available here: [SBERT.net Training MS Marco](https://githu
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained('model_name')
-tokenizer = AutoTokenizer.from_pretrained('model_name')
+model = AutoModelForSequenceClassification.from_pretrained('ChangeIsKey/change-type-classifier')
+tokenizer = AutoTokenizer.from_pretrained('ChangeIsKey/change-type-classifier')
 
-features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
+
+features = tokenizer([['to quickly take something in your hand(s) and hold it firmly', 'to understand something, especially something difficult'], ['To move at a leisurely and relaxed pace, typically by foot', 'To move or travel, irrespective of the mode of transportation']],  padding=True, truncation=True, return_tensors="pt")
 
 model.eval()
 with torch.no_grad():
@@ -49,6 +50,6 @@ with torch.no_grad():
 The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
 ```python
 from sentence_transformers import CrossEncoder
-model = CrossEncoder('model_name', max_length=512)
-labels = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
+model = CrossEncoder('ChangeIsKey/change-type-classifier', max_length=512)
+labels = model.predict([('to quickly take something in your hand(s) and hold it firmly', 'to understand something, especially something difficult'), ('To move at a leisurely and relaxed pace, typically by foot', 'To move or travel, irrespective of the mode of transportation')])
 ```