FinBERT: Financial Sentiment Analysis with BERT

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⚠️ IMPORTANT LICENSING NOTICE

Effective Date: 12 October 2026

What the Apache-2.0 License Covers

The Apache-2.0 license in this repository applies ONLY to the training code, scripts, and notebooks contained in this repository.

The Apache-2.0 license does NOT cover:

• The pre-trained model weights (hosted on Hugging Face)
• The training dataset (Financial PhraseBank)

Licensor

The code in this repository is licensed by MIH AI B.V. under the Apache License, Version 2.0.

Model Weights

The pre-trained FinBERT model weights are available on Hugging Face. Please review the model card on Hugging Face for licensing terms and restrictions that apply to the model weights.

Training Data

This model was trained using the Financial PhraseBank dataset, which has its own licensing terms. Users are solely responsible for ensuring compliance with any third-party data licenses.

No Warranty

This project is provided "AS IS" without warranty of any kind. This is not an official Prosus product.

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FinBERT sentiment analysis model is now available on Hugging Face model hub. You can get the model here.

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. For the details, please see FinBERT: Financial Sentiment Analysis with Pre-trained Language Models.

Installing

Install the dependencies by creating the Conda environment finbert from the given environment.yml file and activating it.

conda env create -f environment.yml
conda activate finbert

Models

Pre-trained Model Weights

Pre-trained FinBERT model weights are available on Hugging Face:

🔗 https://huggingface.co/ProsusAI/finbert

Please review the model card for licensing terms and usage restrictions before downloading or using the model weights.

The code in this repository loads the model directly from Hugging Face by its id:

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained('ProsusAI/finbert')

To use a model you trained yourself, pass its local directory instead of 'ProsusAI/finbert'.

Datasets

There are two datasets used for FinBERT. The language model further training is done on a subset of Reuters TRC2 dataset. This dataset is not public, but researchers can apply for access here.

For the sentiment analysis, we used Financial PhraseBank from Malo et al. (2014). The dataset can be downloaded from this link. If you want to train the model on the same dataset, after downloading it, you should create three files under the data/sentiment_data folder as train.csv, validation.csv, test.csv. To create these files, do the following steps:

• Download the Financial PhraseBank from the above link.
• Get the path of Sentences_50Agree.txt file in the FinancialPhraseBank-v1.0 zip.
• Run the datasets script: python scripts/datasets.py --data_path <path to Sentences_50Agree.txt>

Training the model

Training is done in finbert_training.ipynb notebook. Training starts from bert-base-uncased by default; you can set bm in the notebook to your own further pre-trained language model instead. The trained model will be saved to models/classifier_model/finbert-sentiment. You can find the training parameters in the notebook as follows:

config = Config(   data_dir=cl_data_path,
                   bert_model=bertmodel,
                   num_train_epochs=4.0,
                   model_dir=cl_path,
                   max_seq_length = 64,
                   train_batch_size = 32,
                   learning_rate = 2e-5,
                   output_mode='classification',
                   warm_up_proportion=0.2,
                   local_rank=-1,
                   discriminate=True,
                   gradual_unfreeze=True )

The last two parameters discriminate and gradual_unfreeze determine whether to apply the corresponding technique against catastrophic forgetting.

Getting predictions

We provide a script to quickly get sentiment predictions using FinBERT. Given a .txt file, predict.py produces a .csv file including the sentences in the text, corresponding softmax probabilities for three labels, actual prediction and sentiment score (which is calculated with: probability of positive - probability of negative).

Here's an example with the provided example text: test.txt. From the command line, simply run:

python scripts/predict.py --text_path test.txt --output_dir output/

By default this uses the ProsusAI/finbert model from Hugging Face. To use your own trained model, add --model_path models/classifier_model/finbert-sentiment.

Disclaimer

This is not an official Prosus product. It is the outcome of an intern research project in Prosus AI team.

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.