Financial B E R T

Introduction

FinancialBERT is a BERT model specifically pre-trained for applications in the financial domain. It is designed to enhance natural language processing (NLP) research and practice within financial contexts by providing a specialized model trained on a large corpus of financial texts.

Architecture

FinancialBERT follows the architecture of BERT, a transformer-based model renowned for its application in language understanding tasks. This model is particularly adapted for financial texts, making it suitable for tasks such as masked language modeling in financial datasets.

Training

The training of FinancialBERT utilized a vast collection of financial documents, including:

• TRC2-financial: 1.8 million news articles from Reuters, published between 2008 and 2010.
• Bloomberg News: 400,000 articles from 2006 to 2013.
• Corporate Reports: 192,000 transcripts of 10-K and 10-Q reports.
• Earning Calls: 42,156 documents.

This extensive dataset equips FinancialBERT with a robust understanding of financial language, jargon, and context.

Guide: Running Locally

1. Install Dependencies: Ensure you have Python and PyTorch installed. Use a virtual environment for managing dependencies.
2. Clone Repository: Clone the model repository from Hugging Face's model hub.
3. Load Model: Use the Hugging Face Transformers library to load FinancialBERT.

   from transformers import BertTokenizer, BertForMaskedLM
   
   tokenizer = BertTokenizer.from_pretrained('ahmedrachid/FinancialBERT')
   model = BertForMaskedLM.from_pretrained('ahmedrachid/FinancialBERT')
   

4. Inference: Use the model to run inference on financial text data.
5. Cloud GPUs: For training or inference requiring significant computational resources, consider using cloud-based GPU providers like AWS, Google Cloud, or Azure.

License

The licensing details for FinancialBERT are not explicitly mentioned in the provided text. It is advisable to check the model's repository on Hugging Face or contact the creator, Ahmed Rachid Hazourli, for precise licensing information.