yya518/FinBERT
A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097
This project helps financial analysts and researchers understand the context and sentiment of financial text. You can feed it earnings call transcripts, corporate reports, or analyst reports, and it will output classifications for sentiment (positive, negative, neutral), ESG factors, or identify forward-looking statements. It's designed for anyone working with financial communications who needs to quickly extract meaning and insights from large volumes of text.
647 stars. No commits in the last 6 months.
Use this if you need to analyze large volumes of financial text to automatically determine sentiment, identify ESG-related information, or pinpoint forward-looking statements.
Not ideal if your primary need is general-purpose text analysis outside of financial documents, as its specialization is in financial language.
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647
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Jul 23, 2023
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