https://github.com/furk4neg3/ibm_language_model_with_neural_networks

This repository includes the implementation of a language model using neural networks, developed as part of an IBM AI lab.
https://github.com/furk4neg3/ibm_language_model_with_neural_networks

Last synced: about 2 months ago
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This repository includes the implementation of a language model using neural networks, developed as part of an IBM AI lab.

• Host: GitHub
• URL: https://github.com/furk4neg3/ibm_language_model_with_neural_networks
• Owner: furk4neg3
• Created: 2024-11-10T13:45:22.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-10T13:48:56.000Z (about 2 months ago)
• Last Synced: 2024-11-10T14:32:57.512Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# IBM Language Model with Neural Networks

This repository includes the implementation of a language model using neural networks, developed as part of an IBM AI lab. The project involves building a language model from scratch, including data preprocessing, model architecture, and training for NLP applications.

## Table of Contents

- [Overview](#overview)

- [Technologies Used](#technologies-used)

- [Project Details](#project-details)

- [Key Learnings](#key-learnings)

- [References](#references)

## Overview

Language models are fundamental for various NLP tasks, such as text generation and understanding. This project focuses on building a language model with neural networks, covering the key steps of:

1. **Data Preparation**: Preprocessing text data to make it suitable for model training.

2. **Model Architecture**: Designing a neural network architecture optimized for language modeling.

3. **Training and Optimization**: Implementing training loops, adjusting hyperparameters, and evaluating the model’s performance.

## Technologies Used

- **Python**: Primary programming language

- **PyTorch**: Framework used for neural network construction and training

- **Jupyter Notebook**: Documenting and executing code

- **NLP Libraries**: Various Python libraries for text processing and data handling

## Project Details

1. **Data Preparation**:

   - Preprocessed and tokenized text data to make it ready for training.

   - Implemented custom tokenizers to convert text into sequences suitable for the language model.

2. **Model Architecture**:

   - Built a neural network architecture specifically tailored for language modeling tasks.

   - Experimented with different configurations to optimize the model’s ability to predict and generate language.

3. **Training and Evaluation**:

   - Trained the model on the prepared dataset, adjusted hyperparameters, and optimized training routines.

   - Evaluated the model’s performance in terms of language generation and accuracy in predicting text sequences.

## Key Learnings

- Comprehensive understanding of language model architecture and training.

- Proficiency in using PyTorch for building and training NLP models.

- Practical experience with NLP workflows, including data preparation, neural network design, and model evaluation.

## References

- [IBM AI Engineering Professional Certificate](https://www.coursera.org/professional-certificates/ai-engineer?)

- [Generative AI Engineering with LLMs Specialization](https://www.coursera.org/specializations/generative-ai-engineering-with-llms)

- [Gen AI Foundational Models for NLP & Language Understanding](https://www.coursera.org/learn/gen-ai-foundational-models-for-nlp-and-language-understanding?specialization=generative-ai-engineering-with-llms)