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Finbert Pretrain
FinBERT is a BERT-based model pretrained on financial texts, aimed at advancing research and practice in financial natural language processing.
Downloads 9,645
Release Time : 3/2/2022
Model Overview
FinBERT is a financial domain pretrained language model based on the BERT architecture, specifically designed for processing financial texts and supporting downstream tasks such as sentiment analysis and ESG classification.
Model Features
Financial Domain Pretraining
Pretrained on a corpus of 4.9 billion tokens of financial texts (including corporate reports, earnings call transcripts, and analyst reports), ensuring domain adaptability.
Multi-Task Support
Supports various downstream tasks such as financial sentiment analysis, ESG classification, and forward-looking statement classification, with the ability to fine-tune for specific tasks.
Academic Research Support
Provides clear academic citation guidelines, facilitating researchers in citing the model in their academic work.
Model Capabilities
Financial Text Understanding
Sentiment Analysis
ESG Classification
Forward-Looking Statement Identification
Use Cases
Financial Analysis
Earnings Report Sentiment Analysis
Analyzes the sentiment tendencies in corporate earnings reports to assist investment decisions.
ESG Evaluation
Automatically classifies ESG-related content in corporate reports to support sustainable investing.
Market Research
Forward-Looking Statement Detection
Identifies forward-looking statements in earnings call transcripts to analyze corporate outlooks.
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