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financial-roberta-large-sentiment

Text Classification Transformers PyTorch English roberta Sentiment RoBERTa Financial Statements Accounting Finance Business ESG CSR Reports Financial News Earnings Call Transcripts Sustainability Corporate governance Inference Endpoints
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Financial-RoBERTa

Financial-RoBERTa is a pre-trained NLP model to analyze sentiment of financial text including:

  • Financial Statements,
  • Earnings Announcements,
  • Earnings Call Transcripts,
  • Corporate Social Responsibility (CSR) Reports,
  • Environmental, Social, and Governance (ESG) News,
  • Financial News,
  • Etc.

Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text.

The model will give softmax outputs for three labels: Positive, Negative or Neutral.

How to perform sentiment analysis:

The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:

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

I provide an example script via Google Colab. You can load your data to a Google Drive and run the script for free on a Colab.

Citation and contact:

Please cite this paper when you use the model. Feel free to reach out to mohammad.soleimanian@concordia.ca with any questions or feedback you may have.

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