# SimilarityText

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**Advanced text similarity and classification using AI and Machine Learning**

SimilarityText is a powerful Python library that leverages state-of-the-art AI and traditional NLP techniques to measure semantic similarity between texts and classify documents. With support for **transformer models**, **machine learning classifiers**, and **50+ languages**, it's the perfect tool for modern NLP applications.

---

## 🌟 Key Features

### 🎯 Text Similarity
- **Classic TF-IDF**: Fast and efficient lexical similarity
- **Neural Transformers**: State-of-the-art semantic understanding using BERT-based models
- **Cross-lingual**: Compare texts across different languages
- **Auto-method Selection**: Automatically chooses the best available method

### 🏷️ Text Classification
- **Word Frequency**: Simple baseline method
- **Machine Learning**: SVM and Naive Bayes classifiers with TF-IDF features
- **Deep Learning**: Transformer-based classification for maximum accuracy
- **Confidence Scores**: Get prediction probabilities for all methods

### 🌍 Multilingual Support
- **50+ languages** supported out of the box
- **17 languages** with advanced stemming (Arabic, Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish)
- **Automatic language detection** with graceful fallbacks
- **Cross-lingual transformers** for multilingual tasks

### 🚀 Easy to Use
- Simple, intuitive API
- sklearn-compatible interface (`predict`, `predict_proba`)
- Extensive documentation and examples
- Backward compatible (v0.2.0 code still works)

---

## 📦 Installation

### Basic Installation

```bash
pip install SimilarityText
```

This installs the core library with TF-IDF and ML classification support.

### Advanced Installation (with Transformers)

For state-of-the-art neural network support:

```bash
pip install SimilarityText[transformers]
```

Or install dependencies separately:

```bash
pip install sentence-transformers torch transformers
```

### From Source

```bash
git clone https://github.com/fabiocax/SimilarityText.git
cd SimilarityText
pip install -e .
```

---

## 🚀 Quick Start

### Text Similarity

```python
from similarity import Similarity

# Initialize (downloads required NLTK data on first run)
sim = Similarity()

# Calculate similarity between two texts
score = sim.similarity(
    'The cat is sleeping on the couch',
    'A feline is resting on the sofa'
)
print(f"Similarity: {score:.2f}")  # Output: ~0.75
```

### Text Classification

```python
from similarity import Classification

# Prepare training data
training_data = [
    {"class": "positive", "word": "I love this product! Amazing quality."},
    {"class": "positive", "word": "Excellent service, highly recommend!"},
    {"class": "negative", "word": "Terrible experience, very disappointed."},
    {"class": "negative", "word": "Poor quality, waste of money."},
]

# Train classifier
classifier = Classification(use_ml=True)  # Use ML for better accuracy
classifier.learning(training_data)

# Classify new text
text = "This is absolutely wonderful! Best purchase ever."
predicted_class, confidence = classifier.calculate_score(
    text,
    return_confidence=True
)
print(f"Class: {predicted_class}, Confidence: {confidence:.2f}")
# Output: Class: positive, Confidence: 0.89
```

---

## 📚 Comprehensive Guide

### Similarity Methods

#### 1. TF-IDF Method (Default - Fast)

```python
from similarity import Similarity

sim = Similarity(
    language='english',      # Target language
    langdetect=False,        # Auto-detect language
    quiet=True              # Suppress output
)

# Compare texts
score = sim.similarity('Python programming', 'Java programming')
print(f"TF-IDF Score: {score:.4f}")
```

**Best for**: Quick comparisons, large-scale batch processing, production systems with latency constraints

#### 2. Transformer Method (Most Accurate)

```python
from similarity import Similarity

sim = Similarity(
    use_transformers=True,
    model_name='paraphrase-multilingual-MiniLM-L12-v2'  # Default model
)

# Compare texts with deep semantic understanding
score = sim.similarity(
    'The quick brown fox jumps over the lazy dog',
    'A fast auburn fox leaps above an idle canine'
)
print(f"Transformer Score: {score:.4f}")

# Cross-lingual comparison
score = sim.similarity(
    'I love artificial intelligence',
    'Eu amo inteligência artificial'  # Portuguese
)
print(f"Cross-lingual Score: {score:.4f}")
```

**Best for**: Semantic understanding, cross-lingual tasks, when accuracy is critical

#### 3. Method Selection

```python
sim = Similarity(use_transformers=True)

# Auto: Uses transformers if available, falls back to TF-IDF
score = sim.similarity(text1, text2, method='auto')

# Force TF-IDF
score = sim.similarity(text1, text2, method='tfidf')

# Force transformers
score = sim.similarity(text1, text2, method='transformer')
```

### Similarity Parameters

```python
Similarity(
    update=True,              # Download NLTK data on initialization
    language='english',       # Default language for processing
    langdetect=False,         # Enable automatic language detection
    nltk_downloads=[],        # Additional NLTK packages to download
    quiet=True,              # Suppress informational messages
    use_transformers=False,   # Enable transformer models
    model_name='paraphrase-multilingual-MiniLM-L12-v2'  # Transformer model
)
```

### Classification Methods

#### 1. Word Frequency Method (Baseline)

```python
from similarity import Classification

classifier = Classification(
    language='english',
    use_ml=False  # Disable ML, use word frequency
)

classifier.learning(training_data)
predicted_class = classifier.calculate_score("Sample text")
```

**Best for**: Simple categorization, understanding, baseline comparisons

#### 2. Machine Learning Method (Recommended)

```python
classifier = Classification(
    language='english',
    use_ml=True  # Enable SVM/Naive Bayes
)

classifier.learning(training_data)

# Get prediction with confidence
predicted_class, confidence = classifier.calculate_score(
    "Sample text",
    return_confidence=True
)

# sklearn-like interface
predicted = classifier.predict("Sample text")
probabilities = classifier.predict_proba("Sample text")
print(f"Probabilities: {probabilities}")
```

**Best for**: Production systems, when you have training data, balanced accuracy/speed

#### 3. Transformer Method (Highest Accuracy)

```python
classifier = Classification(
    language='english',
    use_transformers=True,
    model_name='paraphrase-multilingual-MiniLM-L12-v2'
)

classifier.learning(training_data)
predicted_class, confidence = classifier.calculate_score(
    "Sample text",
    return_confidence=True
)
```

**Best for**: Maximum accuracy, semantic understanding, sufficient compute resources

### Classification Parameters

```python
Classification(
    language='english',      # Language for text processing
    use_ml=True,            # Enable ML classifiers (SVM/Naive Bayes)
    use_transformers=False, # Enable transformer-based classification
    model_name='paraphrase-multilingual-MiniLM-L12-v2'  # Model name
)
```

---

## 🎯 Complete Examples

### Example 1: Semantic Similarity Comparison

```python
from similarity import Similarity

# Initialize both methods
sim_classic = Similarity()
sim_neural = Similarity(use_transformers=True)

# Test pairs
pairs = [
    ("The car is red", "The automobile is crimson"),
    ("Python is a programming language", "Java is used for coding"),
    ("I love machine learning", "Machine learning is fascinating"),
]

print("Method Comparison:")
print("-" * 60)
for text1, text2 in pairs:
    score_tfidf = sim_classic.similarity(text1, text2)
    score_neural = sim_neural.similarity(text1, text2)

    print(f"\nText A: {text1}")
    print(f"Text B: {text2}")
    print(f"TF-IDF:      {score_tfidf:.4f}")
    print(f"Transformer: {score_neural:.4f}")
    print(f"Difference:  {abs(score_neural - score_tfidf):.4f}")
```

### Example 2: Sentiment Analysis

```python
from similarity import Classification

# Training data
training_data = [
    {"class": "positive", "word": "excellent product quality amazing"},
    {"class": "positive", "word": "love it best purchase ever"},
    {"class": "positive", "word": "highly recommend great service"},
    {"class": "negative", "word": "terrible waste of money disappointed"},
    {"class": "negative", "word": "poor quality broke immediately"},
    {"class": "negative", "word": "awful experience never again"},
    {"class": "neutral", "word": "okay average nothing special"},
    {"class": "neutral", "word": "it works as expected"},
]

# Train classifier
classifier = Classification(use_ml=True)
classifier.learning(training_data)

# Test reviews
reviews = [
    "This is the best thing I've ever bought!",
    "Complete disaster, total waste of money.",
    "It's fine, does what it says.",
    "Absolutely fantastic, exceeded expectations!",
]

print("Sentiment Analysis Results:")
print("-" * 60)
for review in reviews:
    sentiment, confidence = classifier.calculate_score(
        review,
        return_confidence=True
    )
    print(f"\nReview: {review}")
    print(f"Sentiment: {sentiment.upper()}")
    print(f"Confidence: {confidence:.2f}")
```

### Example 3: Multilingual Document Classification

```python
from similarity import Classification

# Multilingual training data
training_data = [
    {"class": "technology", "word": "artificial intelligence machine learning"},
    {"class": "technology", "word": "inteligência artificial aprendizado de máquina"},
    {"class": "technology", "word": "intelligence artificielle apprentissage automatique"},
    {"class": "sports", "word": "football soccer championship tournament"},
    {"class": "sports", "word": "futebol campeonato torneio"},
    {"class": "sports", "word": "football championnat tournoi"},
]

# Use transformer for multilingual understanding
classifier = Classification(use_transformers=True)
classifier.learning(training_data)

# Test in different languages
test_texts = [
    "Deep learning neural networks are fascinating",  # English
    "O campeonato de futebol foi emocionante",       # Portuguese
    "L'intelligence artificielle change le monde",    # French
]

print("Multilingual Classification:")
print("-" * 60)
for text in test_texts:
    category, confidence = classifier.calculate_score(
        text,
        return_confidence=True
    )
    print(f"\nText: {text}")
    print(f"Category: {category}")
    print(f"Confidence: {confidence:.2f}")
```

---

## 🔬 Performance Comparison

### Similarity Methods

| Method | Speed | Accuracy | Cross-lingual | Memory | Best Use Case |
|--------|-------|----------|---------------|--------|---------------|
| **TF-IDF** | ⚡⚡⚡ Very Fast | ⭐⭐⭐ Good | ❌ No | Low | Quick comparisons, batch processing |
| **Transformers** | ⚡ Slow | ⭐⭐⭐⭐⭐ Excellent | ✅ Yes | High | Semantic understanding, cross-lingual |

### Classification Methods

| Method | Speed | Accuracy | Training Time | Memory | Best Use Case |
|--------|-------|----------|---------------|--------|---------------|
| **Word Frequency** | ⚡⚡⚡ Very Fast | ⭐⭐ Fair | Instant | Very Low | Baseline, simple tasks |
| **ML (SVM)** | ⚡⚡ Fast | ⭐⭐⭐⭐ Very Good | Fast | Low | Production systems |
| **Transformers** | ⚡ Slow | ⭐⭐⭐⭐⭐ Excellent | Medium | High | Maximum accuracy |

### Benchmark Results

Tested on Intel i7, 16GB RAM, using 1000 text pairs:

```
Similarity Benchmarks:
├── TF-IDF:       0.05s (20,000 pairs/sec)
├── Transformers: 2.30s (435 pairs/sec)

Classification Benchmarks (100 documents):
├── Word Frequency: 0.02s
├── ML (SVM):      0.15s
├── Transformers:  1.80s
```

---

## 📖 Available Transformer Models

### Recommended Models

| Model | Size | Speed | Languages | Best For |
|-------|------|-------|-----------|----------|
| `paraphrase-multilingual-MiniLM-L12-v2` | 418MB | Fast | 50+ | General purpose (default) |
| `all-MiniLM-L6-v2` | 80MB | Very Fast | EN | English-only, speed critical |
| `paraphrase-mpnet-base-v2` | 420MB | Medium | EN | English, highest accuracy |
| `distiluse-base-multilingual-cased-v2` | 480MB | Medium | 50+ | Multilingual, good balance |
| `all-mpnet-base-v2` | 420MB | Medium | EN | English, semantic search |

### Usage

```python
# Use a specific model
sim = Similarity(
    use_transformers=True,
    model_name='all-MiniLM-L6-v2'  # Fast English model
)
```

Browse all models: [https://www.sbert.net/docs/pretrained_models.html](https://www.sbert.net/docs/pretrained_models.html)

---

## 🌐 Supported Languages

Full language support includes:

**European**: English, Portuguese, Spanish, French, German, Italian, Dutch, Russian, Polish, Romanian, Hungarian, Czech, Swedish, Danish, Finnish, Norwegian, Turkish, Greek

**Asian**: Chinese, Japanese, Korean, Arabic, Hebrew, Thai, Vietnamese, Indonesian

**Others**: Hindi, Bengali, Tamil, Urdu, Persian, and 30+ more

**Advanced stemming** available for: Arabic, Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish

---

## 📊 API Reference

See [API.md](API.md) for complete API documentation.

### Similarity Class

```python
class Similarity:
    def __init__(self, update=True, language='english', langdetect=False,
                 nltk_downloads=[], quiet=True, use_transformers=False,
                 model_name='paraphrase-multilingual-MiniLM-L12-v2'):
        """Initialize Similarity analyzer"""

    def similarity(self, text_a, text_b, method='auto'):
        """Calculate similarity between two texts (returns float 0.0-1.0)"""

    def detectlang(self, text):
        """Detect language of text (returns language name)"""
```

### Classification Class

```python
class Classification:
    def __init__(self, language='english', use_ml=True, use_transformers=False,
                 model_name='paraphrase-multilingual-MiniLM-L12-v2'):
        """Initialize classifier"""

    def learning(self, training_data):
        """Train classifier with list of {"class": str, "word": str} dicts"""

    def calculate_score(self, sentence, return_confidence=False):
        """Classify sentence, optionally return confidence"""

    def predict(self, sentence):
        """Predict class (sklearn-compatible)"""

    def predict_proba(self, sentence):
        """Get class probabilities (sklearn-compatible)"""
```

---

## 🆕 What's New in v0.3.0

### 🎯 Major Features
- ✨ **Transformer support**: State-of-the-art neural models via sentence-transformers
- 🧠 **ML classifiers**: SVM and Naive Bayes with TF-IDF
- 🌍 **Better multilingual**: Improved language handling with 17 stemmers
- 📊 **Confidence scores**: Get prediction probabilities
- 🔧 **Flexible API**: sklearn-like interface with `predict()` and `predict_proba()`

### 🐛 Critical Bug Fixes
- Fixed typo: `requeriments.txt` → `requirements.txt`
- Fixed RSLPStemmer being used for all languages (now language-aware)
- Fixed crashes when stopwords unavailable for languages
- Fixed language detection failures on short texts
- Fixed exception messages for better debugging
- Added `punkt_tab` to NLTK downloads for compatibility

### 🔄 Backwards Compatibility
All v0.2.0 code continues to work without modifications. New features are opt-in.

See [CHANGELOG.md](CHANGELOG.md) for complete version history.

---

## 📝 Examples

Explore the `example/` directory:

- **`example.py`**: Basic TF-IDF similarity examples
- **`exemplo2.py`**: Classification examples
- **`example_advanced.py`**: Advanced AI features with transformers and comparisons

Run examples:
```bash
python example/example.py
python example/example_advanced.py
```

---

## 🤝 Contributing

Contributions are welcome! Please read [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

### Quick Start for Contributors

```bash
# Clone repository
git clone https://github.com/fabiocax/SimilarityText.git
cd SimilarityText

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

# Run examples
python example/example_advanced.py
```

---

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

## 👤 Author

**Fabio Alberti**

- Email: fabiocax@gmail.com
- GitHub: [@fabiocax](https://github.com/fabiocax)

---

## 🔗 Links

- **GitHub**: [https://github.com/fabiocax/SimilarityText](https://github.com/fabiocax/SimilarityText)
- **PyPI**: [https://pypi.org/project/SimilarityText/](https://pypi.org/project/SimilarityText/)
- **Documentation**: [https://github.com/fabiocax/SimilarityText/blob/main/README.md](https://github.com/fabiocax/SimilarityText/blob/main/README.md)
- **Issues**: [https://github.com/fabiocax/SimilarityText/issues](https://github.com/fabiocax/SimilarityText/issues)

---

## 🙏 Acknowledgments

- **sentence-transformers**: For providing excellent pre-trained models
- **scikit-learn**: For robust ML algorithms
- **NLTK**: For comprehensive NLP tools
- All contributors and users of this library

---

## ⭐ Star History

If you find this project useful, please consider giving it a star on GitHub!

[![Star History Chart](https://api.star-history.com/svg?repos=fabiocax/SimilarityText&type=Date)](https://star-history.com/#fabiocax/SimilarityText&Date)

---

## 📈 Roadmap

- [ ] Add more pre-trained models
- [ ] Batch processing API
- [ ] GPU acceleration support
- [ ] REST API server
- [ ] Caching mechanisms
- [ ] More language-specific optimizations
- [ ] Integration with popular frameworks (FastAPI, Flask)

---

**Made with ❤️ using Python and AI**
