seshasaiveerla01/ILOCANO_to_ENGLISH

README for Jupyter Notebooks: NLP Project and Evaluation

Overview

This repository contains two Jupyter notebooks designed for natural language processing (NLP) tasks and their evaluation. Each notebook has a specific role in the overall workflow of an NLP project.

Files

1. Q7 NLP_project2.ipynb

This notebook focuses on implementing an NLP pipeline. Key components likely include:

• —Data preprocessing
• —Model training and validation
• —Feature extraction
• —Model optimization

2. Q8 Evaluation.ipynb

This notebook is dedicated to evaluating the performance of the NLP models developed in the previous notebook. Key components may include:

• —Performance metrics computation
• —Visualization of results
• —Comparative analysis of models

Requirements

To execute these notebooks, ensure the following dependencies are installed:

• —Python (>= 3.7)
• —Jupyter Notebook
• —Standard NLP libraries such as nltk, spacy, sklearn, and pandas

Usage

1. 1.Open Q7 NLP_project2.ipynb to preprocess data, train, and validate your models.
2. 2.Use Q8 Evaluation.ipynb to evaluate the models and visualize performance metrics.

Notes

• —Modify paths in the notebooks to match your local data directory structure.
• —Ensure all necessary datasets and pre-trained models are available before running the notebooks.

Step 2: Data Preparation

Ensure your dataset is accessible and properly formatted (e.g., CSV or JSON). Update the dataset path in the notebooks accordingly.

Step 3: Run Q7 NLP_project2.ipynb

Open the notebook in Jupyter Notebook or Jupyter Lab: bash Copy code jupyter notebook Q7\ NLP_project2.ipynb Follow the instructions in the notebook to preprocess data, train, and optimize models. Save the trained models and results for evaluation.

Step 4: Run Q8 Evaluation.ipynb

Open the evaluation notebook: bash Copy code jupyter notebook Q8\ Evaluation.ipynb Load the models and results from the previous notebook. Analyze the performance metrics and visualizations to draw insights. Notes Modify hyperparameters and preprocessing steps as needed to suit your dataset and task. Use GPU acceleration for deep learning models if working with large datasets. Outputs The notebooks produce the following outputs:

Trained machine learning or deep learning models. Performance metrics (saved in CSV or JSON format). Visualizations (e.g., confusion matrix, metric curves).