MeoMaya documentation, built to be used.

1. Introduction

MeoMaya is a lightweight, high-performance Natural Language Processing (NLP) framework built entirely in Python. It is designed to be simple, modular, and efficient, making it an ideal choice for developers, researchers, and students who need a powerful NLP toolkit without the overhead of larger, more complex libraries.

The framework provides a complete text processing pipeline, including normalization, tokenization, part-of-speech (POS) tagging, and parsing. Additionally, it features a pure-Python machine learning stack with a TF-IDF vectorizer and a centroid-based classifier, allowing for straightforward implementation of text classification and analysis tasks.

2. Installation

Prerequisites

• Python 3.11 or higher
• pip package manager

Steps

git clone https://github.com/KashyapSinh-Gohil/meomaya.git
cd meomaya

python -m venv .venv
source .venv/bin/activate
pip install -r meomaya/requirements.txt

Optional: pip install indic-nlp-library for Indian language support.

3. Quick Start

Modelify

from meomaya.core.modelify import Modelify
m = Modelify(mode="text")
result = m.run("MeoMaya makes NLP easy and fun!")
import json
print(json.dumps(result, indent=2))

CLI

python -m meomaya "MeoMaya is great for command-line use." --mode text

4. Core Components

Normalizer

from meomaya.core.normalizer import Normalizer
normalizer = Normalizer(lang="en")
normalizer.normalize("This is an EXAMPLE")

Tokenizer

from meomaya.core.tokenizer import Tokenizer
tokenizer = Tokenizer(lang="en")
tokens = tokenizer.tokenize("Hello, world!")

Tagger

from meomaya.core.tagger import Tagger
tagger = Tagger(lang="en")
tagger.tag(['MeoMaya','is','cool'])

Parser

from meomaya.core.parser import Parser
parser = Parser(lang="en")
parser.parse([('MeoMaya','NNP'),('is','VBZ')])

5. Machine Learning Utilities

Vectorizer

from meomaya.ml.vectorizer import Vectorizer
vectorizer = Vectorizer()
X = vectorizer.fit_transform([...])

Classifier

from meomaya.ml.classifier import Classifier
# train, save, load, predict

6. REST API

uvicorn meomaya.api.server:app --host 0.0.0.0 --port 8000

curl -X POST http://localhost:8000/run -H 'Content-Type: application/json' \
-d '{"input":"Hello","mode":"text"}'

7. Hardware Selection

from meomaya.core.hardware import select_device
print(select_device())  # 'cpu', 'cuda', or 'mps'

Advanced Sentiment Demo

See meomaya/examples/full_nlp_workflow_demo.py to train and save a vectorizer & classifier.

python meomaya/examples/full_nlp_workflow_demo.py

CLI

python -m meomaya "Your text" --mode text

Advanced Usage

from meomaya.core.normalizer import Normalizer
from meomaya.core.tokenizer import Tokenizer
from meomaya.core.tagger import Tagger
from meomaya.core.parser import Parser
# ...build pipelines...

API Reference

• Normalizer(lang: str = "en") — normalize(text: str) -> str
• Tokenizer(lang: str = "en") — tokenize(text: str) -> list[str]
• Tagger(lang: str = "en") — tag(tokens: list[str]) -> list[tuple[str,str]]
• Parser(lang: str = "en") — parse(tagged_tokens: list[tuple[str,str]]) -> dict

Troubleshooting

1. ImportError for indic_nlp_library: install optional dependency.
2. Incorrect path for corpus: verify file path.
3. Performance: process in batches for large datasets.

See tests/ or open an issue on the project repo for help.