Update README.md

README.md

@@ -4,14 +4,15 @@ license: apache-2.0
 
 ## **Sentiment Inferencing model for stock related commments**
 
-### A project by NUS ISS students Frank Cao, Gerong Zhang, Jiaqi Yao, Sikai Ni, Yunduo Zhang
+#### *A project by NUS ISS students Frank Cao, Gerong Zhang, Jiaqi Yao, Sikai Ni, Yunduo Zhang*
 
 <br /> 
 
-### Dataset
+### Description
 
 This model is fine tuned with roberta-base model on 3200000 comments from stocktwits, with the user labeled tags 'Bullish' or 'Bearish'
-dataset link: 
+
+[code on github](https://github.com/Gitrexx/PLPPM_Sentiment_Analysis_via_Stocktwits/tree/main/SentimentEngine)
 
 <br />
 
@@ -26,6 +27,8 @@ dataset link:
 | epoch3      | 0.2360      | 0.1875           | 0.9210              |
 | epoch4      | 0.2106      | 0.1603           | 0.9343              |
 
+<br />
+
 # How to use
 ```python
 from transformers import RobertaForSequenceClassification, RobertaTokenizer
@@ -56,8 +59,8 @@ 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 = list(sentences.apply(process_text))
-sentences = list(sentences) # if input text contains https, @ or # or $ symbols, better apply preprocess to get a more accurate result
+# 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)
 print(results) # 2 labels, label 0 is bearish, label 1 is bullish