emotionclassifier 0.1.1

A flexible emotion classifier with support for multiple models

Emotion Classifier

A flexible emotion classifier package with support for multiple models, customizable preprocessing, visualization tools, fine-tuning capabilities, and more.

Overview

emotionclassifier is a Python package designed to classify emotions in text using various pre-trained models from Hugging Face's Transformers library. This package provides a user-friendly interface for emotion classification, along with tools for data preprocessing, visualization, fine-tuning, and integration with popular data platforms.

Features

• Multiple Model Support: Easily switch between different pre-trained models.
• Customizable Preprocessing: Clean and preprocess text data with customizable functions.
• Visualization Tools: Visualize emotion distributions and trends over time.
• Fine-tuning Capability: Fine-tune models on your own datasets.
• User-friendly CLI: Command-line interface for quick emotion classification.
• Integration with Data Platforms: Seamless integration with pandas DataFrames.
• Extended Post-processing: Additional utilities for detailed emotion analysis.

Installation

You can install the package using pip:

pip install emotionclassifier

Usage

Basic Usage

Here's an example of how to use the EmotionClassifier to classify a single text:

from emotionclassifier import EmotionClassifier

# Initialize the classifier with the default model
classifier = EmotionClassifier()

# Classify a single text
text = "I am very happy today!"
result = classifier.predict(text)
print("Emotion:", result['label'])
print("Confidence:", result['confidence'])

Batch Processing

You can classify multiple texts at once using the predict_batch method:

texts = ["I am very happy today!", "I am so sad."]
results = classifier.predict_batch(texts)
print("Batch processing results:", results)

Visualization

To visualize the emotion distribution of a text:

from emotionclassifier import plot_emotion_distribution

result = classifier.predict("I am very happy today!")
plot_emotion_distribution(result['probabilities'], classifier.labels.values())

CLI Usage

You can also use the package from the command line:

emotionclassifier --model deberta-v3-small --text "I am very happy today!"

DataFrame Integration

Integrate with pandas DataFrames to classify text columns:

import pandas as pd
from emotionclassifier import DataFrameEmotionClassifier

df = pd.DataFrame({
    'text': ["I am very happy today!", "I am so sad."]
})

classifier = DataFrameEmotionClassifier()
df = classifier.classify_dataframe(df, 'text')
print(df)

Emotion Trends Over Time

Analyze and plot emotion trends over time:

from emotionclassifier import EmotionTrends

texts = ["I am very happy today!", "I am feeling okay.", "I am very sad."]
trends = EmotionTrends()
emotions = trends.analyze_trends(texts)
trends.plot_trends(emotions)

Fine-tuning

Fine-tune a pre-trained model on your own dataset:

from emotionclassifier.fine_tune import fine_tune_model

# Define your train and validation datasets
train_dataset = ...
val_dataset = ...

# Fine-tune the model
fine_tune_model(classifier.model, classifier.tokenizer, train_dataset, val_dataset, output_dir='fine_tuned_model')

Running Tests

Run the tests using pytest:

poetry run pytest

License

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

Acknowledgements

This package uses pre-trained models from the Hugging Face Transformers library.