Update README.md

README.md

@@ -20,7 +20,7 @@ This model is fine tuned with roberta-base model on 3200000 comments from stockt
 - batch size 32
 - learning rate 2e-5
 
-| Syntax      | Train loss  | Validation loss  | Validation accuracy |
+|              | Train loss  | Validation loss  | Validation accuracy |
 | ----------- | ----------- | ---------------- | ------------------- |
 | epoch1      | 0.3495      | 0.2956           | 0.8679              |
 | epoch2      | 0.2717      | 0.2235           | 0.9021              |
@@ -58,7 +58,9 @@ model_loaded = RobertaForSequenceClassification.from_pretrained('zhayunduo/rober
 
 nlp = pipeline("text-classification", model=model_loaded, tokenizer=tokenizer_loaded)
 
-sentences = pd.Series(['just buy','just sell it','entity rocket to the sky!','go down','even though it is going up, I still think it will not keep this trend in the near future'])
+sentences = pd.Series(['just buy','just sell it',
+                      'entity rocket to the sky!',
+                      'go down','even though it is going up, I still think it will not keep this trend in the near future'])
 # sentences = list(sentences.apply(process_text))  # if input text contains https, @ or # or $ symbols, better apply preprocess to get a more accurate result
 sentences = list(sentences)
 results = nlp(sentences)