Example Integration

This shows how a developer would integrate a data module into their existing AI framework

import json
import os
from typing import List, Dict

class AITrainingDataModule:
    def __init__(self, data_path: str):
        self.data_path = data_path
        self.dataset = []

    def load_data(self):
        """
        Load data from the specified path. Assumes data is in JSONL format,
        where each line is a JSON object representing a single data point.
        """
        if not os.path.exists(self.data_path):
            raise FileNotFoundError(f"Data file not found at {self.data_path}")

        with open(self.data_path, 'r', encoding='utf-8') as file:
            for line in file:
                self.dataset.append(json.loads(line.strip()))

    def preprocess_data(self):
        """
        Preprocess the dataset by tokenizing text, removing stop words,
        and performing other necessary cleaning steps.
        """
        # Example preprocessing steps (implementation depends on specific requirements)
        for data in self.dataset:
            text = data.get('text', '')
            # Tokenization, stop word removal, etc.
            # data['processed_text'] = preprocess_text_function(text)
            # Implement your preprocessing function as needed

    def get_batch(self, batch_size: int) -> List[Dict]:
        """
        Yield batches of data for training.
        """
        for i in range(0, len(self.dataset), batch_size):
            yield self.dataset[i:i + batch_size]

# Usage example
if __name__ == "__main__":
    data_module = AITrainingDataModule(data_path='path/to/your/dataset.jsonl')
    data_module.load_data()
    data_module.preprocess_data()

    for batch in data_module.get_batch(batch_size=32):
        # Pass the batch to your training function
        # train_model(batch)
        pass

Explanation:

• AITrainingDataModule Class: This class encapsulates the functionality required to load, preprocess, and batch the training data.

• load_data Method: Loads data from a specified file path. It assumes the data is in JSON Lines (JSONL) format, where each line is a JSON object representing a single data point.

• preprocess_data Method: Placeholder for preprocessing steps such as tokenization, stop word removal, etc. The actual implementation would depend on the specific requirements of your NLP task.

• get_batch Method: Yields batches of data of a specified size, which can be used during the training loop of your AI model.

Usage:

1. Initialization: Create an instance of AITrainingDataModule by providing the path to your dataset.

2. Loading Data: Call the load_data method to load the dataset into memory.

3. Preprocessing: Execute preprocess_data to perform necessary preprocessing steps on the dataset.

4. Batching: Use the get_batch method to retrieve batches of data during training.

This modular approach ensures that your data handling is organized and reusable across different AI training pipelines.