textpredict 0.1.2

A Python package for text classification using transformer models.

TextPredict

Advanced Text Classification with Transformer Models

TextPredict is a powerful Python package designed for text classification tasks leveraging advanced transformer models. It supports a variety of tasks including sentiment analysis, emotion detection, and zero-shot classification, making it an essential tool for developers and data scientists working in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI).

Features

• Sentiment Analysis: Classify text based on sentiment with high accuracy.
• Emotion Detection: Detect emotions in text effortlessly.
• Zero-Shot Classification: Classify text into unseen categories without any additional training.
• Fine-Tuning: Easily fine-tune models to improve performance on specific datasets.
• Model Evaluation: Evaluate model performance with robust metrics.
• Distributed Training: Support for distributed training to leverage multiple GPUs.

Installation

pip install textpredict

Usage

Sentiment Analysis

from textpredict import TextPredict

tp = TextPredict()
result = tp.analyse("I love using this package!", task="sentiment")
print(result)

Emotion Detection

result = tp.analyse("I am excited about this!", task="emotion")
print(result)

Zero-Shot Classification

result = tp.analyse(
    "This package is great for zero-shot learning.",
    task="zeroshot",
    class_list=["positive", "negative", "neutral"],
)
print(result)

Fine-Tuning Models

from datasets import load_dataset

# Load dataset
dataset = load_dataset("imdb")

# Fine-tune model
tp.tune_model(
    task="sentiment",
    training_data=dataset["train"],
    eval_data=dataset["test"],
    num_train_epochs=1,
    batch_size=8,
    learning_rate=2e-5,
    early_stopping_patience=3,
)

Evaluating Models

metrics = tp.evaluate_model(task="sentiment", eval_data=dataset["test"])
print("Evaluation metrics:", metrics)

Saving and Loading Models

tp.save_model(task="sentiment", output_dir="./fine_tuned_sentiment_model")
tp.load_model(task="sentiment", model_dir="./fine_tuned_sentiment_model")
result = tp.analyse("I love using this package after fine-tuning!", task="sentiment")
print(result)

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue on GitHub.

License

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

Links

• GitHub Repository: Github
• PyPI Project: PYPI
• Documentation: Readthedocs
• Source Code: Source Code
• Issue Tracker: Issue Tracker