Build a sequence from the two sentences, with the correct model-specific separators, token type ids and attention
   masks (which will be created automatically by the tokenizer).
Pass this sequence through the model so that it is classified in one of the two available classes: 0 (not a
   paraphrase) and 1 (is a paraphrase).
Compute the softmax of the result to get probabilities over the classes.
Print the results.
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased-finetuned-mrpc")
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased-finetuned-mrpc")
classes = ["not paraphrase", "is paraphrase"]
sequence_0 = "The company HuggingFace is based in New York City"
sequence_1 = "Apples are especially bad for your health"
