Lingo: Advanced NLP Toolkit

Lightweight, Fast, and Transformer-Ready

Overview

Lingo is a modern, high-performance Natural Language Processing (NLP) toolkit designed for researchers, data scientists, and developers building intelligent language-powered applications. It combines ease of use, speed, and state-of-the-art transformer capabilities, offering an end-to-end pipeline — from text preprocessing to advanced contextual understanding.

Lingo bridges the gap between traditional NLP techniques and next-generation transformer-based architectures like BERT, GPT, and LLaMA, ensuring flexibility, scalability, and cutting-edge accuracy.

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🚀 Quick Start

Installation

# One-command installation (recommended)
pip install lingo-nlp-toolkit

# Full installation with all dependencies
pip install lingo-nlp-toolkit[full]

# Development installation
pip install lingo-nlp-toolkit[dev]

# GPU support
pip install lingo-nlp-toolkit[gpu]

✨ Auto-Setup: Lingo automatically downloads all required NLP data and models on first use!

📦 PyPI Package: lingo-nlp-toolkit on PyPI

Examples & Use Cases

# Basic usage
python examples/basic_usage.py

# Advanced real-world applications
python examples/advanced_use_cases.py

# Enterprise-grade NLP workflows
python examples/enterprise_nlp.py

# Capability showcase
python examples/showcase.py

# Interactive demo
python demo.py

First Steps

from lingo import Pipeline

# Create a sentiment analysis pipeline
nlp = Pipeline(task="sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest")

# Run inference
text = "I absolutely love the new product update!"
result = nlp(text)
print(result)
# Output: {'label': 'POSITIVE', 'score': 0.988}

Command Line Usage

# Sentiment analysis
lingo run sentiment-analysis --model cardiffnlp/twitter-roberta-base-sentiment-latest --text "I love this product!"

# List available models
lingo list-models

# Download a model
lingo download-model --model bert-base-uncased

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✨ Key Features

1. Text Preprocessing & Normalization

• ✅ Unicode normalization (NFC/NFD)
• ✅ Lowercasing, punctuation removal, special character stripping
• ✅ Stopword removal (multi-language support)
• ✅ Lemmatization & stemming
• ✅ Advanced tokenization (Word, Sentence, Subword)
• ✅ Spell correction & slang expansion

2. Core NLP Tasks

• ✅ Text Classification - Multi-class & multi-label
• ✅ Named Entity Recognition (NER) - Domain-specific models
• ✅ Sentiment Analysis - Binary, ternary, fine-grained
• ✅ Text Embeddings - BERT, Sentence-BERT, LLaMA
• ✅ Question Answering - Extractive & generative
• ✅ Text Summarization - Abstractive & extractive

3. Hugging Face Integration

• ✅ Load any model from Hugging Face Hub
• ✅ Fine-tune pre-trained transformers
• ✅ Export models to Hugging Face Hub
• ✅ Mixed precision training

4. Performance & Scalability

• ✅ GPU & multi-core CPU support
• ✅ Asynchronous batch processing
• ✅ Memory-efficient tokenization
• ✅ Lightweight deployment mode

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📚 Comprehensive Examples

Text Classification

from lingo import Pipeline

# Create classifier
classifier = Pipeline(
    task="text-classification",
    model="bert-base-uncased"
)

# Classify texts
texts = [
    "This is a positive review about the product.",
    "I'm not satisfied with the service quality.",
    "The product meets my expectations."
]

results = classifier(texts)
for text, result in zip(texts, results):
    print(f"{text[:30]}... → {result['label']}")

# Output:
# This is a positive review abou... → LABEL_0
# I'm not satisfied with the ser... → LABEL_0
# The product meets my expectati... → LABEL_0

Named Entity Recognition

# Create NER pipeline
ner = Pipeline(
    task="ner",
    model="dslim/bert-base-NER"
)

# Extract entities
text = "Apple Inc. is headquartered in Cupertino, California. Tim Cook is the CEO."
entities = ner(text)

for entity in entities:
    print(f"Entity: {entity['entity']}, Type: {entity['word']}, Score: {entity['score']:.3f}")

# Output:
# Entity: B-LOC, Type: cup, Score: 0.940
# Entity: B-LOC, Type: ##ert, Score: 0.671
# Entity: I-LOC, Type: ##ino, Score: 0.437
# Entity: B-LOC, Type: ca, Score: 0.506

Sentiment Analysis

# Create sentiment analyzer
sentiment = Pipeline(
    task="sentiment-analysis",
    model="cardiffnlp/twitter-roberta-base-sentiment-latest"
)

# Analyze sentiment
texts = [
    "I love this amazing product!",
    "This is terrible, worst purchase ever.",
    "It's okay, nothing special."
]

results = sentiment(texts)
for text, result in zip(texts, results):
    print(f"{text[:30]}... → {result['label']} ({result['score']:.3f})")

# Output:
# I love this amazing product!... → positive (0.987)
# This is terrible, worst purcha... → negative (0.953)
# It's okay, nothing special.... → neutral (0.596)

Text Embeddings & Similarity

# Create embedding pipeline
embeddings = Pipeline(
    task="embedding",
    model="sentence-transformers/all-MiniLM-L6-v2"
)

# Generate embeddings
texts = [
    "The cat is on the mat.",
    "A cat is sitting on the mat.",
    "The weather is beautiful today."
]

embeds = embeddings(texts)

# Calculate similarity
from lingo.models import EmbeddingModel
embedding_model = embeddings.model

similarity = embedding_model.similarity(texts[0], texts[1])
print(f"Similarity: {similarity:.3f}")

# Output:
# Similarity: 0.907

Question Answering

# Create QA pipeline
qa = Pipeline(
    task="question-answering",
    model="deepset/roberta-base-squad2"
)

# Answer questions
context = """
Python is a high-level programming language created by Guido van Rossum in 1991.
It's known for its simplicity and readability, making it popular for beginners.
Python is widely used in data science, machine learning, and web development.
"""

question = "Who created Python?"
answer = qa(question=question, context=context)

print(f"Q: {question}")
print(f"A: {answer['answer']} (confidence: {answer['score']:.3f})")

# Output:
# Q: Who created Python?
# A: Guido van Rossum (confidence: 0.990)

Text Summarization

# Create summarization pipeline
summarizer = Pipeline(
    task="summarization",
    model="facebook/bart-large-cnn"
)

# Summarize long text
long_text = """
Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century.
It encompasses a wide range of capabilities including machine learning, natural language processing, computer vision,
and robotics. AI systems can now perform tasks that were once thought to be exclusively human, such as recognizing
speech, translating languages, making decisions, and solving complex problems.
"""

summary = summarizer(long_text)
print(f"Summary: {summary['summary_text']}")

# Output:
# Summary: artificial intelligence (ai) has emerged as one of the most transformative technologies of the 21st century. it encompasses a wide range of capabilities including machine learning, natural language processing, computer vision, and robotics. ai systems can now perform tasks that were once thought to be exclusively human, such as recognizing speech and translating languages.

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🔧 Advanced Usage

Custom Preprocessing

from lingo import TextPreprocessor

# Configure preprocessing
preprocessor = TextPreprocessor(
    config={
        "lowercase": True,
        "remove_punctuation": True,
        "remove_stopwords": True,
        "lemmatize": True,
        "use_spacy": True,
        "spacy_model": "en_core_web_sm"
    }
)

# Process text
text = "The quick brown foxes are jumping over the lazy dogs! 🦊🐕"
cleaned = preprocessor(text)
print(f"Cleaned: {cleaned}")

# Get detailed preprocessing results
pipeline_result = preprocessor.get_preprocessing_pipeline(text)
print(f"Words: {pipeline_result['words']}")
print(f"Lemmatized: {pipeline_result['lemmatized']}")

# Output:
# Cleaned: the quick brown foxes are jumping over the lazy dogs
# Words: ['the', 'quick', 'brown', 'foxes', 'are', 'jumping', 'over', 'the', 'lazy', 'dogs']
# Lemmatized: ['the', 'quick', 'brown', 'fox', 'are', 'jumping', 'over', 'the', 'lazy', 'dog']

Batch Processing

# Process large datasets efficiently
texts = ["Text 1", "Text 2", "Text 3", ...]  # Large list

# Batch processing
results = pipeline.batch_predict(texts, batch_size=32)

# Or use utility function
from lingo.utils import batch_texts
batches = batch_texts(texts, batch_size=32)

Model Evaluation

from lingo.utils import evaluate_classification

# Evaluate model performance
y_true = ["positive", "negative", "positive", "neutral"]
y_pred = ["positive", "negative", "positive", "positive"]

metrics = evaluate_classification(y_true, y_pred)
print(f"Accuracy: {metrics['accuracy']:.3f}")
print(f"F1 Score: {metrics['f1']:.3f}")

# Output:
# Accuracy: 0.750
# F1 Score: 0.800

Pipeline Configuration

# Load configuration from file
import yaml

with open("config.yaml", "r") as f:
    config = yaml.safe_load(f)

# Create pipeline with custom config
pipeline = Pipeline(
    task="sentiment-analysis",
    model="cardiffnlp/twitter-roberta-base-sentiment-latest",
    config=config
)

---

📁 Project Structure

lingo/
├── lingo/                    # Core package
│   ├── __init__.py          # Main imports
│   ├── core.py              # Pipeline class
│   ├── preprocessing.py      # Text preprocessing
│   ├── models.py            # NLP model classes
│   ├── utils.py             # Utility functions
│   └── cli.py               # Command-line interface
├── examples/                 # Usage examples
│   └── basic_usage.py       # Basic examples
├── lingo/configs/           # Configuration files
│   └── default.yaml         # Default config
├── tests/                   # Test suite
├── setup.py                 # Package setup
├── requirements.txt          # Dependencies
└── README.md                # This file

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⚡ Performance & Optimization

Device Selection

# Automatic device detection
pipeline = Pipeline(task="sentiment-analysis", model="...", device="auto")

# Manual device selection
pipeline = Pipeline(task="sentiment-analysis", model="...", device="cuda")
pipeline = Pipeline(task="sentiment-analysis", model="...", device="mps")  # Apple Silicon

Batch Processing

# Optimize for large datasets
results = pipeline.batch_predict(texts, batch_size=64)

Memory Management

# Use mixed precision for faster inference
pipeline = Pipeline(
    task="sentiment-analysis",
    model="...",
    config={"use_mixed_precision": True}
)

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🔌 Integration & Extensibility

With Existing Libraries

# spaCy integration
import spacy
nlp = spacy.load("en_core_web_sm")

# NLTK integration
import nltk
from nltk.tokenize import word_tokenize

# scikit-learn integration
from sklearn.metrics import classification_report

Custom Models

# Extend base model class
from lingo.models import BaseModel

class CustomModel(BaseModel):
    def _load_model(self):
        # Custom model loading logic
        pass

    def __call__(self, inputs, **kwargs):
        # Custom inference logic
        pass

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🚀 Deployment & Production

Save & Load Pipelines

# Save pipeline
pipeline.save("./saved_pipeline")

# Load pipeline
loaded_pipeline = Pipeline.load("./saved_pipeline")

REST API Template

from fastapi import FastAPI
from lingo import Pipeline

app = FastAPI()

# Load pipeline
pipeline = Pipeline.load("./saved_pipeline")

@app.post("/analyze")
async def analyze_text(text: str):
    result = pipeline(text)
    return {"result": result}

Docker Deployment

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
EXPOSE 8000

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

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📊 Benchmarks & Performance

Task	Model	Speed (CPU)	Speed (GPU)	Memory Usage
Sentiment Analysis	RoBERTa-base	50 ms	15 ms	500 MB
NER	BERT-base-NER	80 ms	25 ms	400 MB
Text Classification	DistilBERT	30 ms	10 ms	300 MB
Embeddings	MiniLM-L6	40 ms	12 ms	200 MB

Benchmarks on Intel i7-10700K (CPU) and RTX 3080 (GPU)

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone repository
git clone https://github.com/irfanalidv/lingo-nlp-toolkit.git
cd lingo-nlp-toolkit

# Install in development mode
pip install -e .[dev]

# Run tests
pytest

# Format code
black lingo/
isort lingo/

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Hugging Face for the amazing transformers library
• spaCy for excellent NLP tools
• NLTK for foundational NLP capabilities
• PyTorch for deep learning framework
• scikit-learn for machine learning utilities

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📞 Support & Community

• Documentation: GitHub Repository
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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⭐ Star History

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Made with ❤️ by Md Irfan Ali