Updates example.

README.md

@@ -38,10 +38,18 @@ import numpy as np
 model = AutoModelForSequenceClassification.from_pretrained("symanto/xlm-roberta-base-snli-mnli-anli-xnli")
 tokenizer = AutoTokenizer.from_pretrained("symanto/xlm-roberta-base-snli-mnli-anli-xnli")
 
-input_pairs = [("I like this pizza.", "The sentence is positive."), ("I like this pizza.", "The sentence is negative.")]
-inputs = tokenizer(["</s></s>".join(input_pair) for input_pair in input_pairs], return_tensors="pt")
+input_pairs = [
+               ("I like this pizza.", "The sentence is positive."),
+               ("I like this pizza.", "The sentence is negative."),
+               ("I mag diese Pizza.", "Der Satz ist positiv."),
+               ("I mag diese Pizza.", "Der Satz ist negativ."),
+               ("Me gusta esta pizza.", "Esta frase es positivo."),
+               ("Me gusta esta pizza.", "Esta frase es negativo."),
+]
+inputs = tokenizer(input_pairs, truncation="only_first", return_tensors="pt", padding=True)
 logits = model(**inputs).logits
-probs =  torch.softmax(logits, dim=1).tolist()
+probs = torch.softmax(logits, dim=1)
+probs = probs[..., [0]].tolist()
 print("probs", probs)
-np.testing.assert_almost_equal(probs, [[0.86, 0.14, 0.00], [0.16, 0.15, 0.69]], decimal=2)
+np.testing.assert_almost_equal(probs, [[0.83], [0.04], [1.00], [0.00], [1.00], [0.00]], decimal=2)
 ```