Finbert

--- language: "en" license: cc-by-nc-sa-4.0 tags: - financial-sentiment-analysis - sentiment-analysis - finance widget: - text: "Stocks rallied and the British pound gained." --- # FinBERT FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063) and our related [blog post](https://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101) on Medium. The model will give softmax outputs for three labels: positive, negative or neutral. ## How to use ```python from transformers import pipeline classifier = pipeline("text-classification", model="ProsusAI/finbert") classifier("Stocks rallied and the British pound gained.") # [{'label': 'positive', 'score': 0.898...}] ``` To get the scores for all three labels, pass `top_k=None` to the classifier. ## Model details - **Architecture**: BERT base (12 layers, hidden size 768, 12 attention heads) with a sequence classification head - **Labels**: `0` positive, `1` negative, `2` neutral - **Language**: English - **Frameworks**: weights are available for PyTorch, TensorFlow and JAX/Flax - **Code**: training and prediction code is on [GitHub](https://github.com/ProsusAI/finBERT) ## Training data ### Training Data Attribution This model was trained using the **Financial PhraseBank** dataset: > Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. > (2014). Good debt or bad debt: Detecting semantic orientations in > economic texts. *Journal of the Association for Information Science > and Technology*, 65(4), 782-796. The Financial PhraseBank dataset is licensed under **CC BY-NC-SA 3.0**. The underlying language model was further pre-trained on a subset of the Reuters TRC2 corpus, which is distributed by NIST under its own access terms. ## Citation ```bibtex @article{araci2019finbert, title={FinBERT: Financial Sentiment Analysis with Pre-tra...

--- language: "en" tags: - financial-sentiment-analysis - sentiment-analysis widget: - text: "Stocks rallied and the British pound gained." --- FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063) and our related [blog post](https://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101) on Medium. The model will give softmax outputs for three labels: positive, negative or neutral. --- About Prosus Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies that empower people and enrich communities. For more information, please visit www.prosus.com. Contact information Please contact Dogu Araci dogu.araci[at]prosus[dot]com and Zulkuf Genc zulkuf.genc[at]prosus[dot]com about any FinBERT related issues and questions.

Source context: 3873198 downloads · 1291 likes · Pipeline text-classification · Library transformers · Repo ProsusAI/finbert