Data Preparation Guide

• We need to decide on which sources to be used for collecting data. These sources can include websites, APIs, datasets, or other repositories.
• Ensure that the selected sources align with the objectives we define in initial step
• detailed information about the data we plan to collect:
  • Total Data Size: Specify the overall volume of data we anticipate working with.
  • Data Format: Define the format of the data (e.g., text, JSON, CSV).
  • Data Categories or Labels (if applicable): If data is organized into categories or labeled, describe these categories.
• Decide how to collect the data. This may involve web scraping, making API requests, manual data entry, or other methods.
• Address ethical considerations, particularly related to user consent and data privacy if applicable.

• the steps involved in cleaning and preprocessing the data. Common steps include:
  • Removing HTML tags and special characters: Clean the text data to eliminate any unwanted symbols or formatting.
  • Tokenization: Split text into individual words or sub-word tokens.
  • Lowercasing: Convert all text to lowercase to ensure uniformity.
  • Handling Missing Values: Implement strategies for dealing with missing data points.
• Implement data quality control measures to identify and rectify issues during the cleaning process. This may include:
  • Deduplication: Identify and remove duplicate entries.
  • Outlier Detection: Identify and handle outliers.
  • Formatting Consistency Checks: Ensure that all data adheres to consistent formatting standards.

• Examine the distribution of classes within dataset. Understand how often each class appears. Identify any class imbalances.
• Address class imbalances, if applicable. Common techniques include over-sampling the minority class, under-sampling the majority class, or using synthetic data generation methods.

• If required, use data augmentation techniques to increase the diversity of dataset.
• Common augmentation methods include back-translation, paraphrasing, or adding noise to text data.

• Divide dataset into three primary subsets: training, validation, and test sets
• The training set is used for model training, the validation set for hyper-parameter tuning, and the test set for final model evaluation.

Use the specific tokenizer library and version to convert text data into tokens and into numerical encodings. Ensure compatibility with chosen LLM architecture.

• Use the required input format based on LLM. Depending on the model, this may involve special tokens like [CLS] and [SEP], or other formatting requirements.

• Efficient data loading pipeline can be implemented using libraries like PyTorch's DataLoader or TensorFlow's Dataset API.
• Ensure that it can handle batch processing and parallelization, optimizing data ingestion for model training.

• Set up preprocessing pipelines that apply transformations consistently to new data during inference. This ensures that input data is properly formatted and processed before being fed into the LLM.

• Maintain thorough documentation of the entire data preparation process. Include information about data sources, preprocessing steps, data augmentation, and any challenges encountered.
• Utilize version control tools such as Git to track changes in data preparation code. This facilitates reproducibility and collaboration.
• Ensure that data preparation process can be reproduced. Provide clear documentation on how to replicate entire process, including data collection and preprocessing steps.