AI Learn

This repository serves as a collection of AI and machine learning projects, models, and experiments. Each project is organized into its own directory and includes all necessary files for dataset preparation, model training, evaluation, and deployment.

Table of Contents

• Projects
• Getting Started
• Requirements
• Contributing

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Projects

1. First Model: Sentiment Analysis

• Description: A sentiment analysis model fine-tuned on a Hugging Face transformer (cardiffnlp/twitter-roberta-base-sentiment-latest).
• Key Features:
  • Dataset preparation using creatingdataset.ipynb.
  • Model training and evaluation using training.ipynb.
  • Deployment via Hugging Face pipelines.
• Directory: first-model-sentiment-analysis/
• Output: Fine-tuned model saved in my_sentiment_model/.

2. Second Model: Suicide Detection

• Description: A suicide detection model fine-tuned on the distilbert-base-uncased-finetuned-sst-2-english transformer model.
• Key Features:
  • Dataset preparation and filtering using json2jsonl.py and trainingModel.ipynb.
  • Fine-tuning the model for binary classification (non-suicide and suicide).
  • Deployment via Hugging Face pipelines with custom label mapping.
• Directory: suicide-detection/
• Output: Fine-tuned model saved in my_suicide_buddy/.
• Model Link: "amor3x/my_suicide_buddy"

3. Third Model: Mental Health Sentiment Analysis

• Description: Mental Sentiment model finetuned on the distilbert/distilbert-base-uncased transformer model.
• Key Features:
  • Dataset preparation and filtering using json2jsonl.py and trainingModel.ipynb.
  • Fine-tuning the model for classification (anxiety , normal , bipolar , depression and suicidal).
  • Deployment via Hugging Face pipelines with custom label mapping.
• Directory: mental-health/
• Output: Fine-tuned model saved in mental_health_bud/.
• Model Link: "amor3x/mental_health_bud"

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Getting Started

To get started with any project in this repository:

1. Clone the repository:

   git clone https://github.com/your-username/ai-learn.git
   cd ai-learn

2. Navigate to project directory of your choice
3. Run the notebook Cells in appropriate order :
   • Creating Dataset
   • Training Model

Requirements

Each project may have its own dependencies. Below are the general requirements:

• Python 3.x
• Jupyter Notebook
• Hugging Face Transformers
• Kagglehub
• Datasets library
• Additional libraries as specified in individual project directories.

Tools

• https://codebeautify.org/csv-to-json-converter FOR csv to JSON
• Hugging face for models and datasets
• Kaggle for Datasets

Contributing

Contributions are welcome! If you'd like to add a new project or improve an existing one:

Command to Upload Model to HuggingFace

$ huggingface-cli repo create my_suicide_buddy $ huggingface-cli upload amor3x/mental_health_bud