Sentiment Analysis Model Fine-Tuning

Application Link: https://huggingface.co/faizack/Fine_Tune_Roberta-base-sentiment

Overview

This repository illustrates how to fine-tune the CardiffNLP Twitter RoBERTa Base model for sentiment analysis using your own dataset. The pre-trained model employed here is based on the RoBERTa architecture and has been specialized in sentiment analysis tasks using Twitter data.

Setup and Prerequisites

Before proceeding, ensure you have the following dependencies installed:

• Python 3.12

Install Required Packages

pip install -r requirements.txt

Notebook for Analysis and Fine-Tuning

Run analysis.ipynb

Data Preparation

1. Dataset Acquisition: Obtain the Amazon Reviews dataset from Kaggle API and load in .env as shown .env.example.

2. Data Extraction and Preparation: Extract the compressed data files (train.ft.txt.bz2 and test.ft.txt.bz2) and preprocess them into readable text files (train.ft.txt and test.ft.txt).

3. Data Loading: Load the preprocessed data into Pandas DataFrames for further processing.

Model Fine-Tuning

1. Tokenization: Use the AutoTokenizer from Hugging Face's Transformers library to tokenize the text data.

2. Model Initialization: Initialize the pre-trained sentiment analysis model (AutoModelForSequenceClassification) from the CardiffNLP Twitter RoBERTa Base.

3. Training: Fine-tune the initialized model on the preprocessed dataset. Apply appropriate training parameters and evaluation metrics.

4. Model Saving: Save the fine-tuned model and tokenizer for future usage.

Inference

1. Model Loading: Load the saved fine-tuned model and tokenizer.

2. Prediction: Tokenize the input text and perform sentiment analysis using the loaded model.

3. Result Interpretation: Interpret the model's output to determine the sentiment label and confidence score.

For running the Flask API and the Streamlit app, follow these simplified steps:

Note : Before Running the Flask API make sure you run all step in Analysis

1. Flask API:

   • Start the Flask API:

     cd server/;flask --app api.py run

2. Streamlit App:

   • Run the Streamlit app:

     streamlit run app.py