Update README.md

README.md

@@ -11,4 +11,41 @@ tags:
 - Sentiment Classification
 - Finance
 - Deberta-v2
----
+---
+
+# Deberta for Financial Sentiment Analysis
+
+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 
+
+1) financial_phrasebank
+2) chiapudding/kaggle-financial-sentiment
+3) zeroshot/twitter-financial-news-sentiment
+4) FinanceInc/auditor_sentiment
+
+
+## How to use the model
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+def get_sentiment(sentence):
+    bert_dict = {}
+    vectors = tokenizer(sentence, return_tensors='pt').to(device)
+    outputs = bert_model(**vectors).logits
+    probs = torch.nn.functional.softmax(outputs, dim = 1)[0]
+    bert_dict['neg'] = round(probs[0].item(), 3)
+    bert_dict['neu'] = round(probs[1].item(), 3)
+    bert_dict['pos'] = round(probs[2].item(), 3)
+    return bert_dict
+
+MODEL_NAME = 'RashidNLP/Finance_Multi_Sentiment'
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+bert_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels = 3).to(device)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+get_sentiment("The stock market will struggle to rally until debt ceiling is increased")
+
+```