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

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.

Before proceeding, ensure you have the following dependencies installed:

• Python 3.12

Install Required Packages

pip install -r requirements.txt

Run analysis.ipynb

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.

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.

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 streamlit_app.py