Update README.md

README.md

@@ -30,15 +30,16 @@ The datasets I use are
 import torch
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-def get_sentiment(sentence):
+def get_sentiment(sentences):
     bert_dict = {}
-    vectors = tokenizer(sentence, return_tensors='pt').to(device)
+    vectors = tokenizer(sentences, padding = True, max_length = 65, 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
+    probs = torch.nn.functional.softmax(outputs, dim = 1)
+    for prob in probs:
+        bert_dict['neg'] = round(prob[0].item(), 3)
+        bert_dict['neu'] = round(prob[1].item(), 3)
+        bert_dict['pos'] = round(prob[2].item(), 3)
+        print (bert_dict)
 
 MODEL_NAME = 'RashidNLP/Finance_Multi_Sentiment'
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -46,6 +47,6 @@ 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")
+get_sentiment(["The stock market will struggle until debt ceiling is increased", "ChatGPT is boosting Microsoft's search engine market share"])
 
 ```