financial roberta large sentiment

Introduction

Financial-RoBERTa is a pre-trained NLP model designed for sentiment analysis of financial texts. It processes various forms of financial documents, including financial statements, earnings announcements, and CSR reports, providing sentiment outputs for positive, negative, or neutral classifications.

Architecture

Financial-RoBERTa builds upon the RoBERTa Large language model. It is further trained and fine-tuned using a specialized corpus consisting of financial documents such as 10-K, 10-Q, 8-K filings, earnings call transcripts, CSR reports, ESG news, and other financial news articles.

Training

The model is trained to understand the nuances of financial language and provide sentiment analysis. The training corpus includes a variety of financial texts, allowing the model to output sentiment classifications with respect to the financial context.

Guide: Running Locally

1. Set Up Environment: Ensure you have Python and the Transformers library installed.
2. Install Transformers: Use the command pip install transformers to install the necessary library.
3. Load the Model: Use the Hugging Face pipeline for sentiment analysis:

   from transformers import pipeline
   sentiment_analysis = pipeline("sentiment-analysis", model="soleimanian/financial-roberta-large-sentiment")
   

4. Run Sentiment Analysis: Input your financial text to obtain sentiment predictions:

   print(sentiment_analysis("In fiscal 2021, we generated a net yield of approximately 4.19% on our investments, compared to approximately 5.10% in fiscal 2020."))
   

5. Cloud GPUs: For more extensive analysis, consider using cloud platforms like Google Colab, which provides free access to GPUs.

License

The model is released under the Apache 2.0 license, allowing for both academic and commercial use with appropriate attribution.