Fine-Tuning a Pre-trained LLM for Sentiment Classification

Author(s): Dimitris Effrosynidis

Originally published on Towards AI.

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In a previous tutorial, Traditional vs. Generative AI for Sentiment Classification, we predicted the sentiment of product reviews from the Flipkart Customer Review dataset.

We compared several methods:

Logistic Regression with TF-IDF: A simple yet effective baseline using term-frequency-based features for classification.Logistic Regression with Pretrained Embeddings: Utilize advanced embedding models like all-MiniLM-L6-v2 to generate semantic representations for training a classifier.Zero-shot Classification: Perform classification without labeled data by leveraging cosine similarity between document and label embeddings.Generative Models: Explore generative language models like Flan-T5, which classify text by generating responses based on a prompt.Task-Specific Sentiment Models: Leverage fine-tuned sentiment models like juliensimon/reviews-sentiment-analysis for domain-specific performance.

Here are the results of that experiment:

In this tutorial, we will fine-tune the Task-Specific Sentiment Model (juliensimon/reviews-sentiment-analysis) and see if its 0.79 accuracy will improve.

To get the complete code, visit my GitHub portfolio.

If you run this code on Google Colab (or any other cloud platform), please ensure that all necessary dependencies are installed.

Run the following code block to install the required packages:

%%capture!pip install datasets transformers sentence-transformers evaluate

We will use the same dataset and the same pre-processing as the previous tutorial.

import pandas as pdimport numpy as… Read the full blog for free on Medium.

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