Financial B E R T Sentiment Analysis

Introduction

FinancialBERT is a BERT-based model specifically pre-trained on financial texts to improve the performance of sentiment analysis within the financial domain. This model is intended to assist financial researchers and practitioners by providing robust NLP capabilities without the need for extensive computational resources.

Architecture

FinancialBERT builds upon the BERT architecture and has been fine-tuned for sentiment analysis tasks using the Financial PhraseBank dataset. This fine-tuning allows it to outperform general BERT models as well as other models specific to the financial sector.

Training

The model was fine-tuned using the Financial PhraseBank dataset, which contains 4,840 categorized financial news articles. The training employed the following hyperparameters:

• Learning Rate: 2e-5
• Batch Size: 32
• Max Sequence Length: 512
• Number of Training Epochs: 5

Evaluation Metrics: The model was evaluated using precision, recall, and F1-score, achieving high scores across negative, neutral, and positive classes.

Guide: Running Locally

To use FinancialBERT for sentiment analysis, follow these steps:

1. Install Transformers: Ensure the transformers library is installed via pip:

   pip install transformers
   

2. Load the Model: Use the following code to load the model and tokenizer:

   from transformers import BertTokenizer, BertForSequenceClassification, pipeline
   
   model = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis", num_labels=3)
   tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis")
   
   nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
   

3. Perform Sentiment Analysis: Analyze text data using the pipeline:

   sentences = ["Your financial text here."]
   results = nlp(sentences)
   print(results)
   

Cloud GPUs: For large-scale analysis or faster processing, consider using cloud-based GPUs from providers like AWS, Google Cloud, or Azure.

License

Details on the licensing and usage permissions for FinancialBERT are typically provided on the model's Hugging Face page. Refer to the model's documentation for specific licensing information.