finbert

Introduction

FinBERT is a pre-trained NLP model designed for analyzing the sentiment of financial text. It extends the BERT language model to the finance domain by using a large financial corpus for further training and fine-tuning. The model is specifically fine-tuned using the Financial PhraseBank by Malo et al. (2014) and is detailed in the paper "FinBERT: Financial Sentiment Analysis with Pre-trained Language Models." It categorizes financial text sentiment into three labels: positive, negative, or neutral.

Architecture

FinBERT is based on the BERT architecture, which is a transformer-based model. It has been fine-tuned on a financial corpus to enhance its ability to perform sentiment analysis specifically in the financial sector. The model outputs sentiment predictions using a softmax layer to classify text into positive, negative, or neutral categories.

Training

The training for FinBERT involved using the Financial PhraseBank dataset, which provides a rich set of annotated financial texts. The model underwent additional training on this domain-specific data to adjust the BERT architecture for financial sentiment analysis tasks, thus improving its accuracy and relevance for financial text.

Guide: Running Locally

1. Prerequisites: Ensure you have Python and the necessary libraries such as PyTorch or TensorFlow installed. Hugging Face's Transformers library is also required.
2. Installation: Use pip to install the Transformers library:

   pip install transformers
   

3. Load the Model: Use the Transformers library to load FinBERT:

   from transformers import BertTokenizer, BertForSequenceClassification
   tokenizer = BertTokenizer.from_pretrained('ProsusAI/finbert')
   model = BertForSequenceClassification.from_pretrained('ProsusAI/finbert')
   

4. Inference: Tokenize your input text and pass it through the model to obtain sentiment predictions.
5. Cloud GPUs: For faster processing, consider using cloud GPU services such as AWS, Google Cloud, or Microsoft Azure.

License

The documentation does not specify a license. For further details, please refer to the official Hugging Face model page or contact the authors.