Create README.md

README.md

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+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')
+
+nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
+
+sentences = ["there is a shortage of capital, and we need extra financing",  
+             "growth is strong and we have plenty of liquidity", 
+             "there are doubts about our finances", 
+             "profits are flat"]
+results = nlp(sentences)
+print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative