Update README.md

README.md

@@ -47,24 +47,16 @@ Example usage:
 base_model_name = 't5-large'  
 tokenizer = T5Tokenizer.from_pretrained(base_model_name, model_max_length=512)  
 model = T5ForConditionalGeneration.from_pretrained('trichter/t5-DistillingSbS-ABSA')  
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-model.to(device)
-
-examples = {'appName': ['Google Chrome', 'Google Chrome'], 'review': ['This app is great, the speed is unmatched', 'Bad app, crashes constantly']}
-
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
+model.to(device)  
+examples = {'appName': ['Google Chrome', 'Google Chrome'], 'review': ['This app is great, the speed is unmatched', 'Bad app, crashes constantly']}  
 model_inputs = tokenize_function(examples) # assuming example has the fields 'appName' and 'review'. tokenize_function is in the GitHub repo in data_utils.py.
 
-outputs = generate(model, model_inputs, return_type = 'labels') # generate() is in the github repo and generates either labels or rationales depening on return_type. Default is 'labels' but can be changed to 'rationales'
-
-tokenizer.decode(outputs[0], skip_special_tokens=True) # prints '"speed": "positive"'
-
-tokenizer.decode(outputs[1], skip_special_tokens=True) # prints '"crashes": "negative"'
-
-
-
-outputs = generate(model, model_inputs, return_type = 'rationales')
-
-tokenizer.decode(outputs[0], skip_special_tokens=True) # prints '"speed": "the review explicitly mentions that the speed of the app is unmatched, indicating satisfaction with its performance in terms of speed."'
+outputs = generate(model, model_inputs, return_type = 'labels') # generate() is in the github repo and generates either labels or rationales depening on return_type. Default is 'labels' but can be changed to 'rationales'  
+tokenizer.decode(outputs[0], skip_special_tokens=True) # prints '"speed": "positive"'  
+tokenizer.decode(outputs[1], skip_special_tokens=True) # prints '"crashes": "negative"'  
 
+  
+outputs = generate(model, model_inputs, return_type = 'rationales')  
+tokenizer.decode(outputs[0], skip_special_tokens=True) # prints '"speed": "the review explicitly mentions that the speed of the app is unmatched, indicating satisfaction with its performance in terms of speed."'  
 tokenizer.decode(outputs[1], skip_special_tokens=True) # prints '"crashes": "the app crashing constantly is explicitly mentioned as a major issue, indicating dissatisfaction with its stability."'