Update README.md

README.md

@@ -55,7 +55,26 @@ for t in text:
 
 #### NLI use-case
 ```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
+device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+model_name = "thkkvui/mDeBERTa-v3-base-finetuned-nli-jnli"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+premise = "NY Yankees is the professional baseball team in America."
+hypothesis = "メジャーリーグのチームは、日本ではニューヨークヤンキースが有名だ。"
+
+inputs = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
+
+with torch.no_grad():
+    output = model(**inputs)
+    
+preds = torch.softmax(output["logits"][0], -1).tolist()
+label_names = ["entailment", "neutral", "contradiction"]
+result = {name: round(float(pred) * 100, 1) for pred, name in zip(preds, label_names)}
+print(result)
 ```
 
 ## Training and evaluation data