Update README.md

README.md

@@ -13,7 +13,7 @@ tags:
 - Deberta-v2
 ---
 
-# Deberta for Financial Sentiment Analysis
+# Deberta for Financial Sentiment Classification
 
 I use a Deberta model trained on over 1 million reviews from Amazon's multi-reviews dataset and finetune it on 4 finance datasets that are categorized with  Sentiment labels.
 The datasets I use are 
@@ -41,7 +41,7 @@ def get_sentiment(sentences):
         bert_dict['pos'] = round(prob[2].item(), 3)
         print (bert_dict)
 
-MODEL_NAME = 'RashidNLP/Finance_Multi_Sentiment'
+MODEL_NAME = 'RashidNLP/Finance-Sentiment-Classification'
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 bert_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels = 3).to(device)