Update README.md

README.md

@@ -3,6 +3,7 @@ language:
 - en
 base_model:
 - google-t5/t5-large
+library_name: transformers
 ---
 Model: t5-DistillingSbS-ABSA
 
@@ -39,4 +40,22 @@ Learning Rate: 1e-4
 
 Epochs: 5
 
-Max Sequence Length: 512
+Max Sequence Length: 512
+
+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']}
+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."'
+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."'