Wolaita-POST 0.1.0

A POS tagger for the Wolaita language using deep learning

Wolaita_POST

Overview

Wolaita_POST is a Python package designed for accurate Part-of-Speech (POS) tagging in the Wolaita language. It employs deep learning models, including Bi-GRU, Bi-LSTM, and others, and integrates FastText embeddings for enhanced performance. The package utilizes pretrained models to simplify deployment and improve tagging accuracy. Wolaita_POST is an essential tool for researchers and developers focused on Natural Language Processing (NLP) for lesser-resourced languages, providing a robust solution for Wolaita language text analysis.

Features

• Accurate POS Tagging: Utilizes deep learning models (Bi-GRU, Bi-LSTM, etc.) to achieve precise Part-of-Speech tagging for Wolaita language text.
• Pretrained Models: Ready-to-use pretrained models for quick deployment and high accuracy.
• FastText Embeddings: Incorporates FastText word embeddings to capture subword information and improve performance on low-resource languages.
• Easy Integration: Simple API that allows researchers and developers to integrate POS tagging into their NLP pipelines.
• Supports Wolaita Language: Specifically designed for the Wolaita language, addressing the challenges of processing lesser-resourced languages.
• Customizable: Flexible configuration to accommodate different models, tokenizers, and word vectors based on project requirements.
• Efficient Deployment: Enables easy deployment for various NLP applications, such as machine translation and named entity recognition (NER).

Installation

To install Wolaita_POST, you can use pip:

• pip install Wolaita_POST

##Usage

After installation, you can use Wolaita_POST as follows:

1. Import the package: from Wolaita_POST import pos_tagger
2. Set file paths for your pretrained model, word vectors, and tokenizers: model_path = "/content/drive/MyDrive/POS/Bi_GRU_model.h5" # Adjust if your model file has a different extension word_vector_path = "/content/drive/MyDrive/POS/fasttext_compatible.bin" word_tokenizer_path = "/content/drive/MyDrive/POS/wolaita_tokenizerX.pkl" tag_tokenizer_path = "/content/drive/MyDrive/POS/wolaita_tag_tokenizerY.pkl"
3. Initialize the POS tagger: pos_tagger = WolaitaPOSTagger( model_path=model_path, word_vector_path=word_vector_path, word_tokenizer_path=word_tokenizer_path, tag_tokenizer_path=tag_tokenizer_path )
4. Use the POS tagger to tag Wolaita text: text = ['Insert your sample text here'] tagged_text = pos_tagger.tag(text) print(tagged_text)

The tagged_text will contain the part-of-speech tags for the given Wolaita text.

##Running Tests If you want to verify functionality, you can use pytest. Run this command in your project directory:

• !pytest /content/drive/MyDrive/Wolaita_POST/tests > test_report.txt

##License This project is licensed under the MIT License. See the LICENSE file for more details.

##Contributing Contributions are welcome! If you have suggestions for improving the package or find any issues, feel free to open a pull request or submit an issue on GitHub.

##Acknowledgements Special thanks to the developers and researchers who contributed to this project, making it possible to expand NLP resources for the Wolaita language.