Update README.md

README.md

@@ -42,16 +42,21 @@ As of December 2021, mDeBERTa-base is the best performing multilingual transform
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+
 model_name = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
 premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
 hypothesis = "Emmanuel Macron is the President of France"
+
 input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
 output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
 prediction = torch.softmax(output["logits"][0], -1).tolist()
+
 label_names = ["entailment", "neutral", "contradiction"]
 prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
+
 print(prediction)
 ```