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Finbert Pretrain Yiyanghkust
FinBERT is a BERT model pretrained on financial texts, aiming to advance research and applications in financial natural language processing.
Downloads 580
Release Time : 3/2/2022
Model Overview
FinBERT is a pretrained language model based on the BERT architecture for the financial domain. It was trained on three major financial text corpora (corporate annual reports, earnings call transcripts, and analyst research reports), with a total corpus size of 4.9 billion tokens.
Model Features
Financial Domain Pretraining
Specifically pretrained on financial texts, including professional corpora such as corporate annual reports, earnings call transcripts, and analyst research reports.
Large-Scale Corpus
Training corpus size reaches 4.9 billion tokens, covering various types of financial texts.
Downstream Task Adaptation
Can serve as a base model for fine-tuning on various financial NLP tasks.
Model Capabilities
Financial Text Understanding
Financial Domain Masked Prediction
Financial Text Feature Extraction
Use Cases
Financial Text Analysis
Analyst Sentiment Classification
After fine-tuning, it can be used to analyze sentiment tendencies in financial texts.
Fine-tuned model has been released at https://huggingface.co/yiyanghkust/finbert-tone
Financial Document Information Extraction
Extract key information from documents such as annual reports and financial statements.
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