This repository contains a sequence of Jupyter notebooks designed to perform a regression task on a set of data with the goal of predicting 'likes' on media based on captions generated from the media content. The process involves generating captions using the Blip2 model, creating instructional data from these captions, classifying the data into buckets, and then performing regression on each classified bucket.
- Caption Generation: Used the
blip2-caption-generation.ipynb
notebook to generate captions for input media data. - Instruction Data Creation: Utilized the
making-instruction-data-for-task1.ipynb
notebook to create prompts corresponding to the input, incorporating various metadata. - Bucket Classification: Implemented the
training-and-inference-distilbert.ipynb
notebook to classify the data into 7 distinct buckets based on the number of likes. - Regression Analysis: Employed the
regression_on_buckets.ipynb
notebook to perform regression on each bucket using various regressor models. - Deployment: Execute the
task1-deployed.ipynb
notebook to run the project and observe the inferences easily.
In order to run the deployment code, you must add the following files to their respective model folders. They can be accessed from link