For this hands-on tutorial on fine-tuning a Phi-2 model, we are going to deal with a sentiment analysis on financial and economic information. Sentiment analysis on financial and economic information is highly relevant for businesses for several key reasons, ranging from market insights (gain valuable insights into market trends, investor confidence, and consumer behavior) to risk management (identifying potential reputational risks) to investment decisions (gauging the sentiment of stakeholders, investors, and the general public businesses can assess the potential success of various investment opportunities).
Before the technicalities of fine-tuning a large language model like Phi-2, we had to find the correct dataset to demonstrate the potentialities of fine-tuning.
Particularly within the realm of finance and economic texts, annotated datasets are notably rare, with many being exclusively reserved for proprietary purposes. To address the issue of insufficient training data, scholars from the Aalto University School of Business introduced in 2014 a set of approximately 5000 sentences. This collection aimed to establish human-annotated benchmarks, serving as a standard for evaluating alternative modeling techniques. The involved annotators (16 people with adequate background knowledge on financial markets) were instructed to assess the sentences solely from the perspective of an investor, evaluating whether the news potentially holds a positive, negative, or neutral impact on the stock price.
The FinancialPhraseBank dataset is a comprehensive collection that captures the sentiments of financial news headlines from the viewpoint of a retail investor. Comprising two key columns, namely "Sentiment" and "News Headline," the dataset effectively classifies sentiments as either negative, neutral, or positive. This structured dataset serves as a valuable resource for analyzing and understanding the complex dynamics of sentiment in the domain of financial news. It has been used in various studies and research initiatives, since its inception in the work by Malo, P., Sinha, A., Korhonen, P., Wallenius, J., and Takala, P. "Good debt or bad debt: Detecting semantic orientations in economic texts.", published in the Journal of the Association for Information Science and Technology in 2014.