Skip to main content

A library for pre-processing data used in machine learning models

Project description

lib-ml

lib-ml is a Python library designed for preprocessing text data, especially tailored for machine learning applications. The library offers robust tools for tokenization, sequence padding, and label encoding, ensuring that text data is optimally prepared for model training and analysis. The library is available on PyPI and can be easily integrated into your projects.

Installation

Install lib-ml from PyPI:

pip install lib-ml-group3

Features

lib-ml includes the following features:

  • Data Tokenization: Convert text into sequences of tokens or characters.
  • Sequence Padding: Pad sequences to a uniform length to ensure consistency among data inputs.
  • Label Encoding: Encode labels in a way that is suitable for machine learning models.
  • Persistence: Save and load preprocessed data using Python's pickle module for easy reusability.

Usage

Here is a quick example of how to use lib-ml for text data preprocessing:

from lib_ml.preprocessing import preprocess_data

# Preprocess the data and save it to disk
preprocess_data()

The preprocess_data() function reads data from specified input directories, processes the text and labels, and saves the tokenized and encoded outputs to designated output directories.

License

lib-ml is open source software licensed as MIT.

Support

If you have any questions or issues with lib-ml, please open an issue on the project repository, and we will get back to you as soon as possible.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lib_ml_group3-0.3.1.tar.gz (3.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

lib_ml_group3-0.3.1-py3-none-any.whl (4.6 kB view details)

Uploaded Python 3

File details

Details for the file lib_ml_group3-0.3.1.tar.gz.

File metadata

  • Download URL: lib_ml_group3-0.3.1.tar.gz
  • Upload date:
  • Size: 3.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.9.19 Linux/6.5.0-1018-azure

File hashes

Hashes for lib_ml_group3-0.3.1.tar.gz
Algorithm Hash digest
SHA256 81884b051a8911d4b9a9dcd75ab3afcad3349f1e2530d9dc7c8f35c68ba90f61
MD5 d7fa84651c47ac8059d802b416dc5c2b
BLAKE2b-256 9269621378af24517569f8ebd46e8f4c6d999df80e7c965986a6d194ad2c9d72

See more details on using hashes here.

File details

Details for the file lib_ml_group3-0.3.1-py3-none-any.whl.

File metadata

  • Download URL: lib_ml_group3-0.3.1-py3-none-any.whl
  • Upload date:
  • Size: 4.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.9.19 Linux/6.5.0-1018-azure

File hashes

Hashes for lib_ml_group3-0.3.1-py3-none-any.whl
Algorithm Hash digest
SHA256 a9cc4e1e4c7e3fb849847ffa943c0911e2a79f1d53eb600d095b01e845e7d495
MD5 4d5230cf63e05215b4cac40ba4f77695
BLAKE2b-256 033d13e513a83cbc2d009991ce7e36dfc13ea39b827b5f31ed7cff39573cd832

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page