AI and NLP Model Collection

This repository contains several Jupyter notebooks demonstrating various AI and Natural Language Processing (NLP) models and techniques. The notebooks cover a range of tasks including language translation, sentiment analysis, and image generation.

Notebooks Overview

1. HuggingFace_NLP_Model.ipynb

• Description: This notebook demonstrates how to use Hugging Face's Transformers library to leverage pre-trained NLP models for various tasks such as text classification, named entity recognition, and more.
• Key Features:
  • Loading and using pre-trained models
  • Fine-tuning models on custom datasets
  • Example usage of models like BERT, GPT-2, etc.

2. languages_translation_model_use.ipynb

• Description: This notebook focuses on language translation using state-of-the-art models. It provides a practical guide to implementing translation models and evaluating their performance.
• Key Features:
  • Preprocessing and tokenization for translation tasks
  • Using translation models such as MarianMT or T5
  • Example code for translating text between multiple languages

3. sentiment_analysis_LLM_openai_model.ipynb

• Description: This notebook covers sentiment analysis using OpenAI's language models. It includes examples of how to perform sentiment analysis and interpret model outputs.
• Key Features:
  • Using OpenAI’s API for sentiment analysis
  • Processing and analyzing sentiment in text
  • Fine-tuning models if needed

4. stable_diffusion.ipynb

• Description: This notebook provides an introduction to Stable Diffusion for generating images from textual descriptions. It demonstrates how to use diffusion models for creative image generation tasks.
• Key Features:
  • Generating images from text prompts
  • Customizing image generation parameters
  • Example use cases for art and creative projects

Getting Started

To get started with the notebooks, follow these steps:

1. Clone the Repository:

   git clone https://github.com/your-repo/ai-nlp-models.git
   cd ai-nlp-models