FinBERT is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.

• Corporate Reports 10-K & 10-Q: 2.5B tokens
• Earnings Call Transcripts: 1.3B tokens
• Analyst Reports: 1.1B tokens

More technical details on FinBERT: Click Link
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.
If you use the model in your academic work, please cite the following paper:
Huang, Allen H., Hui Wang, and Yi Yang. “FinBERT: A Large Language Model for Extracting Information from Financial Text.” Contemporary Accounting Research (2022).

How to use

You can use this model with Transformers pipeline for sentiment analysis.
from transformers import BertTokenizer, BertForSequenceClassification<br /> from transformers import pipeline<br /> finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)<br /> tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')<br /> nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)<br /> sentences = ["there is a shortage of capital, and we need extra financing",<br /> "growth is strong and we have plenty of liquidity",<br /> "there are doubts about our finances",<br /> "profits are flat"]<br /> results = nlp(sentences)<br /> print(results) #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative<br />