Update README.md

README.md

@@ -14,12 +14,8 @@ TODO
 Pre-trained models can be used like this:
 ```python
 from sentence_transformers import CrossEncoder
-model = CrossEncoder('cross-encoder/nli-deberta-v3-large')
+model = CrossEncoder('vectara/hallucination_evaluation_model')
 scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
-
-#Convert scores to labels
-label_mapping = ['contradiction', 'entailment', 'neutral']
-labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
 ```
 
 ## Usage with Transformers AutoModel
@@ -28,15 +24,12 @@ You can use the model also directly with Transformers library (without SentenceT
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-large')
-tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-large')
+model = AutoModelForSequenceClassification.from_pretrained('vectara/hallucination_evaluation_model')
+tokenizer = AutoTokenizer.from_pretrained('vectara/hallucination_evaluation_model')
 
 features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'],  padding=True, truncation=True, return_tensors="pt")
 
 model.eval()
 with torch.no_grad():
-    scores = model(**features).logits
-    label_mapping = ['contradiction', 'entailment', 'neutral']
-    labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
-    print(labels)
+    scores = model(**features)
 ```