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 More details on `FinBERT`: [Click Link](https://github.com/yya518/FinBERT)
 
-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.
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+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.
+
+# How to use
+You can use this model with Transformers pipeline for sentiment analysis.
+```
+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)
+
+```
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