| 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('Forturne/Finbert_PB',num_labels=3) | |
| tokenizer = BertTokenizer.from_pretrained('Forturne/Finbert_PB') | |
| 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 | |