From Medium:

An efficient approach to text classification using OpenAI’s GPT-3.5-turbo model eliminates the need for extensive labeled data. By utilizing the OpenAI API and Python, we can quickly classify SMS messages as spam or not. With only $0.007 spent, the model achieved an impressive 94% accuracy rate in classifying messages.

The process starts by creating an OpenAI account to access the API key required for utilizing the models. Limits are set at 3 requests per minute and 200 per day to avoid Rate Limit Errors in the free version. The GPT-3.5-turbo model analyzes messages and provides a probability score between 0 to 1 for spam classification.

Using the SMS Spam Collection dataset, we tested the model’s prediction accuracy by examining 50 instances containing both spam and non-spam messages. The model correctly categorized 47 out of 50 instances, demonstrating high sensitivity in detecting spam messages with room for precision improvement based on the three misclassifications observed.

The model’s reasoning for misclassifying non-spam messages as spam included language and grammar irregularities commonly found in spam messages. Additionally, suggestive or inappropriate content and spelling errors were factors influencing the model’s classification decisions. Further exploration of different prompts and OpenAI models may enhance performance.

Overall, the use of pre-trained language models offers a streamlined approach to text classification tasks, enabling accurate predictions without the need for extensive training datasets. Leveraging the capabilities of the OpenAI API and GPT-3.5-turbo model for spam detection showcases the efficiency and effectiveness of utilizing advanced AI technologies for text analysis.



Read more at Medium: Utilizing the OpenAI API for Detection of SMS Spam Messages | by Amanda Iglesias Moreno | Mar, 2024