This repository contains a fine-tuned version of the distilbert-base-uncased model, designed for sentiment analysis of tweets. The model is trained to classify the sentiment of a sentence into two categories: positive (label 0) and negative (label 1).

The fine-tuned model utilizes the distilbert-base-uncased architecture, trained on a dataset of GPT-3.5-generated tweets. It is designed to input a sentence and output a binary sentiment label, 0 for positive and 1 for negative.

The model was trained on a dataset consisting of tweets generated and labeled with sentiments by GPT-3.5. Each tweet in the training set was labeled as either positive or negative to provide ground truth for training.