Update README.md

README.md

@@ -4,8 +4,8 @@
 import torch
 from transformers.models.bert import BertTokenizer, BertForSequenceClassification
 
-tokenizer = BertTokenizer.from_pretrained('chujiezheng/roberta-base-cdconv')
-model = BertForSequenceClassification.from_pretrained('chujiezheng/roberta-base-cdconv')
+tokenizer = BertTokenizer.from_pretrained('thu-coai/roberta-base-cdconv')
+model = BertForSequenceClassification.from_pretrained('thu-coai/roberta-base-cdconv')
 model.eval()
 
 turn1 = [
@@ -22,7 +22,7 @@ text2 = turn2[1]
 model_input = tokenizer(text1, text2, return_tensors='pt', return_token_type_ids=True, return_attention_mask=True)
 model_output = model(**model_input, return_dict=False)
 prediction = torch.argmax(model_output[0].cpu(), dim=-1)[0].item()
-print(prediction) # 0 for non-contradiction, 1 for contradiction
+print(prediction) # output 1. 0 for non-contradiction, 1 for contradiction
 ```
 
 This fine-tuned model obtains 75.7 accuracy and 72.3 macro-F1 on the test set.