FinancialBERT-Sentiment-Analysis
FinancialBERT is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model.
Params
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Context
512
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Likes
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Specifications
- Architecture
- BertForSequenceClassification
- Context length
- 512
- Vocabulary
- 30,873
- Layers / heads
- 12 / 12
- First seen on the Hub
- 2022-03-02
- Training datasets
- financial_phrasebank
- Added to our catalog
- 2026-07-28
Compare with any text-classification model