Modals - Natural Language Processing

Steps to create a Modal

1. Data Collection
   1. Identify sources of Indian historical stories, texts, and related content.
   2. Gather a diverse dataset from books, articles, historical documents, folklore, etc.
   3. Ensure the dataset covers a wide range of historical periods, events, and cultural aspects.
2. Preprocessing
   1. Clean the collected data by removing noise, special characters, and irrelevant information.
   2. Tokenize the text into words or subwords for modeling.
   3. Split the dataset into training, validation, and possibly test sets.
3. Model Selection
   1. Choose a generative AI model architecture suitable for text generation.
   2. Consider Transformer-based models like GPT (Generative Pre-trained Transformer) or LSTM (Long Short-Term Memory) networks.
   3. Decide whether to build the model from scratch or use pre-trained models for fine-tuning.
4. Model Training
   1. Train the selected model on the preprocessed dataset.
   2. Define training objectives, such as maximizing likelihood or minimizing perplexity.
   3. Experiment with different training configurations and hyperparameters.
5. Hyperparameter Tuning
   1. Optimize hyperparameters like learning rate, batch size, and model architecture.
   2. Use techniques like grid search or random search to explore the hyperparameter space.
   3. Monitor training metrics and adjust hyperparameters accordingly.
6. Evaluation
   1. Evaluate the trained model's performance using metrics like perplexity, BLEU score, or human evaluation.
   2. Assess the model's ability to generate coherent and contextually relevant Indian historical stories.
7. Deployment
   1. Integrate the trained model into your storytelling app's backend infrastructure.
   2. Develop APIs or endpoints for generating stories based on user prompts.
   3. Deploy the app on a web server or cloud platform for accessibility.
8. User Interface
   1. Design a user-friendly interface for interacting with the storytelling app.
   2. Implement features for inputting prompts, selecting genres and emotions, and viewing generated stories.
   3. Consider incorporating multimedia elements like images or audio to enhance the storytelling experience.
9. Testing and Iteration
   1. Conduct thorough testing to identify and fix any bugs or issues.
   2. Gather feedback from users and iterate on the app's features and functionality.
   3. Continuously update and improve the generative AI model based on user interactions and performance metrics.
10. Continuous Improvement
    1. Monitor app usage and user feedback to identify areas for improvement.
    2. Collect additional data to expand the dataset and enhance the model's diversity.
    3. Stay updated on advancements in NLP research and incorporate new techniques or models to improve story generation quality.