TextClassification 0.2.4
Create Text Classification Fine-Tunning Model
License: MIT
Requires Python: >=3.6
Keywords: transformers,datasets

# Text Classification with NLP Models and Transformers

## Introduction
This Python package provides a simple and efficient solution for text classification tasks using Natural Language Processing (NLP) models and Transformer-based encoder-decoder models. It supports both binary classification and multiple classification scenarios, allowing you to train, evaluate, and deploy state-of-the-art models for classifying text data.

## Features
- Binary classification: Classify text data into two classes.
- Multiple classification: Classify text data into multiple classes.
- NLP models: Utilize powerful NLP models for accurate classification, such as BERT, GPT, RoBERTa, and more.
- Transformer-based encoder-decoder models: Benefit from the encoder-decoder architecture for advanced classification tasks.
- Easy deployment: Deploy your trained models effortlessly using the PyPI package.
- Markdown format README file: Comprehensive documentation and usage instructions in Markdown format for seamless integration into your project.

## Installation
You can install the package from PyPI using the following command:

```bash
pip install text-classification-nlp
```

## Usage
To use the text classification package, follow these steps:

1. Import the necessary modules and dependencies.
```python
from text_classification_nlp import TextClassifier, TransformerEncoderDecoderModel
```

2. Load your dataset and preprocess the text data as required.
```python
# Example dataset loading and preprocessing
data = load_dataset('your_dataset')
preprocessed_data = preprocess_data(data)
```

3. Create an instance of the TextClassifier class.
```python
# Binary classification example
binary_classifier = TextClassifier(model='bert', num_classes=2)

# Multiple classification example
multi_classifier = TextClassifier(model='roberta', num_classes=5)
```

4. Train your classifier using the prepared dataset.
```python
binary_classifier.train(preprocessed_data)

multi_classifier.train(preprocessed_data)
```

5. Evaluate the performance of your trained classifier.
```python
binary_classifier.evaluate(test_data)

multi_classifier.evaluate(test_data)
```

6. Deploy your trained model for predictions.
```python
binary_classifier.predict(text)

multi_classifier.predict(text)
```

For detailed examples and advanced usage, refer to the provided documentation.

## Citation
If you use this package in your research or project, please cite it as follows:

```
Author. (Year). Text Classification with NLP Models and Transformers [Software]. Available from https://github.com/your_repository
```

## Dataset Example
Here's an example of how your dataset should be structured:

```json
{
  "data": [
    {
      "text": "This is an example text for classification.",
      "label": 1
    },
    {
      "text": "Another text for classification.",
      "label": 0
    },
    ...
  ]
}
```

Ensure that your dataset is properly formatted with the "text" field containing the input text and the "label" field containing the corresponding class label.

## Conclusion
This text classification package provides a convenient and powerful solution for binary and multiple classification tasks using NLP models and Transformer-based encoder-decoder models. By leveraging the capabilities of these models, you can achieve accurate and efficient text classification results. Install the package today and start classifying your text data with ease!