Update README.md

README.md

@@ -16,19 +16,14 @@ More technical details on `FinBERT`: [Click Link](https://github.com/yya518/FinB
 
 This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.
 
-If you use the model in your academic work, please cite the following paper:
-
-Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).
-
-
 # How to use 
 You can use this model with Transformers pipeline for sentiment analysis.
 ```python
 from transformers import BertTokenizer, BertForSequenceClassification
 from transformers import pipeline
 
-finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
-tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')
+finbert = BertForSequenceClassification.from_pretrained('rpratap2102/The_Misfits',num_labels=3)
+tokenizer = BertTokenizer.from_pretrained('rpratap2102/The_Misfits')
 
 nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)