bm-preprocessing 1.4.4

A package to preprocess text data

📄 bm-preprocessing

bm-preprocessing is a Python package providing easy-to-use NLP preprocessing utilities built on top of NLTK and pandas. It helps you clean, normalize, tokenize, and vectorize text data efficiently using a modular pipeline.

---

✨ Features

• Text cleaning and normalization
• Tokenization and stopword removal
• Lemmatization
• TF-IDF and Bag-of-Words vectorization
• Pipeline-based preprocessing
• Built on NLTK and pandas
• Scikit-learn–style API

---

📦 Installation

Install from PyPI:

pip install bm-preprocessing

---

🚀 Quick Start

Basic Usage with Pipeline

from bm_preprocessing import (
    TextCleaner,
    Tokenizer,
    Normalizer,
    StopwordFilter,
    Lemmatizer,
    Vectorizer,
    Pipeline
)

# Sample documents
documents = [
    "This is an example document! It has punctuation & numbers: 123.",
    "Natural Language Processing is AMAZING!!!",
    "Preprocessing text is very important for NLP tasks."
]

# Create preprocessing components
cleaner = TextCleaner(
    lowercase=True,
    remove_punctuation=True,
    remove_numbers=True,
    strip_whitespace=True
)

tokenizer = Tokenizer(method="word")

normalizer = Normalizer(
    expand_contractions=True,
    fix_unicode=True
)

stopword_filter = StopwordFilter(language="english")

lemmatizer = Lemmatizer(method="wordnet")

vectorizer = Vectorizer(
    method="tfidf",
    max_features=5000,
    ngram_range=(1, 2)
)

# Build pipeline
preprocessing_pipeline = Pipeline([
    cleaner,
    normalizer,
    tokenizer,
    stopword_filter,
    lemmatizer,
    vectorizer
])

# Run preprocessing
processed_data = preprocessing_pipeline.fit_transform(documents)

# Inspect output
print("Processed Features Shape:", processed_data.shape)
print("Sample Vector:", processed_data[0])

---

🧩 Step-by-Step Processing (Without Pipeline)

You can also run each step manually:

from bm_preprocessing import (
    TextCleaner,
    Tokenizer,
    StopwordFilter,
    Lemmatizer,
    Vectorizer
)

docs = [
    "Machine learning is fun!",
    "Text preprocessing improves results."
]

# Initialize tools
cleaner = TextCleaner(lowercase=True)
tokenizer = Tokenizer()
stopwords = StopwordFilter("english")
lemmatizer = Lemmatizer()
vectorizer = Vectorizer(method="bow")

# Process
cleaned = [cleaner.clean(d) for d in docs]
tokens = [tokenizer.tokenize(d) for d in cleaned]
filtered = [stopwords.remove(t) for t in tokens]
lemmatized = [lemmatizer.lemmatize(t) for t in filtered]

vectors = vectorizer.fit_transform(lemmatized)

print(vectors)

---

🛠️ Components Overview

Component	Description
TextCleaner	Removes noise and formats text
Tokenizer	Splits text into tokens
Normalizer	Standardizes text
StopwordFilter	Removes common filler words
Lemmatizer	Converts words to base form
Vectorizer	Converts text to numeric features
Pipeline	Chains components into a workflow

---

🧠 Deep Learning Preparation Example

For sequence models:

from bm_preprocessing import (
    TextCleaner,
    Tokenizer,
    SequencePadder,
    VocabularyBuilder
)

texts = [
    "Deep learning for NLP",
    "Transformers are powerful"
]

cleaner = TextCleaner(lowercase=True)
tokenizer = Tokenizer()
vocab = VocabularyBuilder(max_size=10000)
padder = SequencePadder(max_length=50)

# Clean
cleaned = [cleaner.clean(t) for t in texts]

# Tokenize
tokens = [tokenizer.tokenize(t) for t in cleaned]

# Build vocabulary
vocab.fit(tokens)

# Encode
encoded = [vocab.encode(t) for t in tokens]

# Pad
padded = padder.pad(encoded)

print(padded)

---

📚 Requirements

• Python 3.8+
• nltk
• pandas
• scikit-learn (for vectorization)

Install dependencies automatically with:

pip install bm-preprocessing

---

📂 Project Structure

bm_preprocessing/
│
├── cleaning.py
├── tokenization.py
├── normalization.py
├── filtering.py
├── lemmatization.py
├── vectorization.py
├── pipeline.py
└── __init__.py

---

🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a new branch
3. Commit your changes
4. Open a pull request

---

📄 License

This project is licensed under the MIT License.

---

📬 Support

If you encounter any issues or have feature requests, please open an issue on GitHub.

---