Finbert Pretrain



FinBERT is a masked language model trained on the finance domain. It has been pre-trained on a large corpus of financial text data and can be fine-tuned for specific downstream tasks in the finance domain, such as sentiment analysis or financial document classification. The model is specifically designed to understand financial concepts and terminology, and can be used to generate predictions and insights for a range of financial applications.

Use cases

This pretrained FinBERT model holds immense potential for the finance industry. It can be utilized in various ways to enhance financial analysis and decision-making processes. One possible use case is sentiment analysis, where the model can be fine-tuned to analyze financial statements or news articles and determine the sentiment towards a particular company or market. This can assist investors in making informed decisions. Another application could be financial document classification, where the model could be trained to automatically categorize documents such as annual reports or SEC filings, saving time and effort in manual classification tasks. Furthermore, the model's understanding of financial concepts and terminology can be leveraged to generate predictions and insights, such as forecasting stock prices or identifying potential market trends. Overall, the FinBERT model opens up possibilities for creating products and services that provide intelligent analysis, risk assessment, and financial predictions to streamline decision-making processes in the finance domain.



Cost per run
Avg run time

Creator Models

Finbert Tone$?1,154,055
Finbert Esg 9 Categories$?3,110
Finbert Fls$?8,330
Finbert Esg$?108,147

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Summary of this model and related resources.

Model NameFinbert Pretrain
Platform did not provide a description for this model.
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided


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