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financial-roberta-large-sentiment
WebUser2/financial-roberta-large-sentiment
financial-roberta-large-sentiment is a text classification model from WebUser2. Use it when you need a label for a piece of text. The card lists the license as apache-2.0.
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From the Hugging Face model README
<!DOCTYPE html>
<html>
<body>
<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="#" 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/>
</body>
</html>
- Task
- Text Classification
- Type
- roberta
- License
- apache-2.0
- Languages
- eng
- Created
- Jul 23, 2026
- Updated
- Jul 23, 2026