| --- |
| license: apache-2.0 |
| language: |
| - eng |
| tags: |
| - text-classification |
| - Forward-Looking Statements |
| - FLS |
| - RoBERTa |
| - Financial Statements |
| - Accounting |
| - Finance |
| - Business |
| - ESG |
| - CSR Reports |
| - Financial News |
| - Earnings Call Transcripts |
| - Sustainability |
| - Corporate governance |
| --- |
| <!DOCTYPE html> |
| <html> |
| <body> |
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| <h1><b>FLS-RoBERTa</b></h1> |
| <p><b>FLS-RoBERTa</b> is a pre-trained NLP model to classify forward-looking statements (FLS) in 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>FLS-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus of 10-K, 10-Q, 8-K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text, labeled at the sentence level as forward-looking or non-forward-looking.</p> |
| <p>The model gives softmax outputs for two labels: <b>FLS</b> (Forward-Looking Statement) and <b>Non-FLS</b> (Non-Forward-Looking Statement).</p> |
| <p><b>How to classify text:</b></p> |
| <p>The easiest way to use the model for single predictions is Hugging Face's text classification pipeline, which only needs a couple lines of code as shown in the following example:</p> |
| <pre> |
| <code> |
| from transformers import pipeline |
| fls_classifier = pipeline("text-classification", model="soleimanian/fls-roberta-large") |
| print(fls_classifier("We expect revenue to grow by approximately 15% over the next fiscal year as we expand into new markets.")) |
| </code> |
| </pre> |
| <p>I provide an example script via <a href="https://colab.research.google.com/drive/1WQqMH-FiC9MJlUYRA2aI7FWCUeoYuCs-?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="#" 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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| </body> |
| </html> |
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