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.
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