Update README.md

README.md

@@ -13,7 +13,7 @@ should probably proofread and complete it, then remove this comment. -->
 # bert-uncased-base
 
 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an Reddit-dialogue dataset.
-This model can be used for Text Classification: Given two sentences，see if they are related.
+This model can be used for Text Classification: Given two sentences, see if they are related.
 It achieves the following results on the evaluation set:
 - Loss: 0.2297
 - Accuracy: 0.9267
@@ -54,7 +54,7 @@ label_list = ['matched', 'unmatched']
 tokenizer = AutoTokenizer.from_pretrained("Fan-s/reddit-tc-bert", use_fast=True)
 model = AutoModelForSequenceClassification.from_pretrained("Fan-s/reddit-tc-bert") 
 
-# set the input
+# Set the input
 post = "don't make gravy with asbestos."
 response = "i'd expect someone with a culinary background to know that. since we're talking about school dinner ladies, they need to learn this pronto."
 
@@ -69,6 +69,6 @@ def predict(post, response, max_seq_length=128):
         return predict_label, logits
 
 # predict whether the two sentences match                 
-predict_label = predict(post, response)
+predict_label, logits = predict(post, response)
 print("predict_label:", predict_label)
 ```