Update README.md

README.md

@@ -45,17 +45,29 @@ Max Sequence Length: 512
 Example usage:
 
 base_model_name = 't5-large'
+
 tokenizer = T5Tokenizer.from_pretrained(base_model_name, model_max_length=512)
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 model = T5ForConditionalGeneration.from_pretrained('trichter/t5-DistillingSbS-ABSA')
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 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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 model.to(device)
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 examples = {'appName': ['Google Chrome', 'Google Chrome'], 'review': ['This app is great, the speed is unmatched', 'Bad app, crashes constantly']}
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 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'
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 tokenizer.decode(outputs[0], skip_special_tokens=True) # prints '"speed": "positive"'
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 tokenizer.decode(outputs[1], skip_special_tokens=True) # prints '"crashes": "negative"'
 
+
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 outputs = generate(model, model_inputs, return_type = 'rationales')
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 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."'
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 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."'