---
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-trained Language Models},
  author={Araci, Dogu},
  journal={arXiv preprint arXiv:1908.10063},
  year={2019}
}
```

## 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

For questions about this research project:
- Dogu Araci: dogu.araci[at]prosus.com
- Zulkuf Genc: zulkuf.genc[at]prosus.com

**Note**: Questions about commercial licensing or legal permissions 
cannot be addressed by the research team. See the License section below.

---

## 📜 License and Usage Terms

**Copyright © MIH AI B.V.**

### Model Weights License
The FinBERT model weights are made available under **CC BY-NC-SA 4.0** 
(Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International).

**This license restricts commercial use.** You may use this model for 
non-commercial purposes only, and any adaptations must be shared under 
the same license.

## ⚠️ User Responsibilities

**You are solely responsible for**:
- Ensuring your use complies with the CC BY-NC-SA 4.0 license
- Obtaining any necessary rights clearances for your specific use case
- Determining whether your intended use qualifies as "non-commercial"
- Compliance with any applicable laws and regulations

**Prosus makes no representation** that any particular use is permitted 
and does not grant rights in third-party training data.

## 🚫 Disclaimer

**NO WARRANTY**: This model is provided "AS IS" without warranty of any 
kind, express or implied, including but not limited to warranties of 
merchantability, fitness for a particular purpose, or non-infringement.

**NOT AN OFFICIAL PRODUCT**: This is not an official Prosus product. 
It was created as part of a research project.

**NO SUPPORT**: Prosus does not provide support, maintenance, or updates 
for this model.

**LIABILITY**: In no event shall MIH AI B.V. or Prosus be liable for any 
claim, damages, or other liability arising from the use of this model.