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soleimanian/fls-roberta

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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<h1>FLS-RoBERTa</h1> FLS-RoBERTa is a pre-trained NLP model to classify forward-looking statements (FLS) in financial text, including: <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> 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. The model gives softmax outputs for two labels: FLS (Forward-Looking Statement) and Non-FLS (Non-Forward-Looking Statement). How to classify text: 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: <pre> <code> from transformers import pipeline flsclassifier = pipeline("text-classification", model="soleimanian/fls-roberta-large") print(flsclassifier("We expect revenue to grow by approximately 15% over the next fiscal year as we expand into new markets.")) </code> </pre> 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. Citation and contact: 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.

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