lib-ml-remla-team14-a2 1.0.14.dev0

A ML preprocessing library.

lib_ml_remla_team14_a2

lib_ml_remla_team14_a2 contains pre-processing logic for data used in our ML training pipeline.

Features

• Tokenization of text data at a character level.
• Padding of text sequences to uniform lengths.

Installation

Install lib_ml_remla_team14_a2 using pip:

pip install lib_ml_remla_team14_a2

Usage

Importing the Class

from lib_ml.preprocessing import TextPreprocessor

Initializing the Preprocessor'

You can customize the behavior of the TextPreprocessor by adjusting its configuration. Here's how to initialize it with the default settings:

preprocessor = TextPreprocessor()

Or customize its behavior:

config = {
    'lower': True,
    'char_level': True,
    'oov_token': '-n-',  # Token used for out-of-vocabulary characters
    'sequence_length': 150  # Adjust sequence length as needed
}
preprocessor = TextPreprocessor(config=config)

Preprocessing Text Data

Use the fit_text method to fit the tokenizer on your text data, and transform_text to convert text data into padded sequences:

texts = ["hello", "world"]
preprocessor.fit_text(texts)
processed_texts = preprocessor.transform_text(texts)

Output

The transform_text method returns an array of tokenized and padded sequences, ready for use in training or inference:

print(processed_texts)  # Output will be a NumPy array of processed text data