readme: ---
  license: apache-2.0
  language: 
    - eng
  tags:
  - text-classification
  - Sentiment
  - RoBERTa
  - Financial Statements
  - Accounting
  - Finance
  - Business
  - ESG
  - CSR Reports
  - Financial News
  - Earnings Call Transcripts
  - Sustainability
  - Corporate governance
  ---
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  <h1><b>Financial-RoBERTa</b></h1>
  <p><b>Financial-RoBERTa</b> is a pre-trained NLP model to analyze sentiment of financial text including:</p>
  <ul style="PADDING-LEFT: 40px">
    <li>Financial Statements,</li>
    <li>Earnings Announcements,</li>
    <li>Earnings Call Transcripts,</li>
    <li>Corporate Social Responsibility (CSR) Reports,</li>
    <li>Environmental, Social, and Governance (ESG) News,</li>
    <li>Financial News,</li>
    <li>Etc.</li>
  </ul>
  <p>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.</p>
  <p>The model will give softmax outputs for three labels: <b>Positive</b>, <b>Negative</b> or <b>Neutral</b>.</p>
  <p><b>How to perform sentiment analysis:</b></p>
  <p>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:</p>
  <pre>
    <code>
  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."))
    </code>
  </pre>
  <p>I provide an example script via <a href="https://colab.research.google.com/drive/11RGWU3UDtxnjan8Ug6dyX82m9fBV6CGo?usp=sharing" target="_blank">Google Colab</a>. You can load your data to a Google Drive and run the script for free on a Colab.
  <p><b>Citation and contact:</b></p>
  <p>Please cite <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4115943" target="_blank">this paper</a> when you use the model. Feel free to reach out to mohammad.soleimanian@concordia.ca with any questions or feedback you may have.<p/>
  
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