Update README.md

README.md

@@ -27,8 +27,8 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." # Input for Summarization/ Dialogue / Paraphrase
 hypothesis = "The movie was not good."   # Output for Summarization/ Dialogue / Paraphrase
-input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
-output = model(input["input_ids"].to(device))[:,0]  # device = "cuda:0" or "cpu"
+input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt", truncation_strategy="only_first", max_length=512)
+output = model(input["input_ids"].to(device))['logits'][:,0]  # device = "cuda:0" or "cpu"
 prediction = torch.sigmoid(output).tolist()
 print(prediction)
 ```