|
--- |
|
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 |
|
--- |
|
<!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="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/> |
|
|
|
</body> |
|
</html> |
|
|
