# 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!
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"description": "# Text Classification with NLP Models and Transformers\r\n\r\n## Introduction\r\nThis 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.\r\n\r\n## Features\r\n- Binary classification: Classify text data into two classes.\r\n- Multiple classification: Classify text data into multiple classes.\r\n- NLP models: Utilize powerful NLP models for accurate classification, such as BERT, GPT, RoBERTa, and more.\r\n- Transformer-based encoder-decoder models: Benefit from the encoder-decoder architecture for advanced classification tasks.\r\n- Easy deployment: Deploy your trained models effortlessly using the PyPI package.\r\n- Markdown format README file: Comprehensive documentation and usage instructions in Markdown format for seamless integration into your project.\r\n\r\n## Installation\r\nYou can install the package from PyPI using the following command:\r\n\r\n```bash\r\npip install text-classification-nlp\r\n```\r\n\r\n## Usage\r\nTo use the text classification package, follow these steps:\r\n\r\n1. Import the necessary modules and dependencies.\r\n```python\r\nfrom text_classification_nlp import TextClassifier, TransformerEncoderDecoderModel\r\n```\r\n\r\n2. Load your dataset and preprocess the text data as required.\r\n```python\r\n# Example dataset loading and preprocessing\r\ndata = load_dataset('your_dataset')\r\npreprocessed_data = preprocess_data(data)\r\n```\r\n\r\n3. Create an instance of the TextClassifier class.\r\n```python\r\n# Binary classification example\r\nbinary_classifier = TextClassifier(model='bert', num_classes=2)\r\n\r\n# Multiple classification example\r\nmulti_classifier = TextClassifier(model='roberta', num_classes=5)\r\n```\r\n\r\n4. Train your classifier using the prepared dataset.\r\n```python\r\nbinary_classifier.train(preprocessed_data)\r\n\r\nmulti_classifier.train(preprocessed_data)\r\n```\r\n\r\n5. Evaluate the performance of your trained classifier.\r\n```python\r\nbinary_classifier.evaluate(test_data)\r\n\r\nmulti_classifier.evaluate(test_data)\r\n```\r\n\r\n6. Deploy your trained model for predictions.\r\n```python\r\nbinary_classifier.predict(text)\r\n\r\nmulti_classifier.predict(text)\r\n```\r\n\r\nFor detailed examples and advanced usage, refer to the provided documentation.\r\n\r\n## Citation\r\nIf you use this package in your research or project, please cite it as follows:\r\n\r\n```\r\nAuthor. (Year). Text Classification with NLP Models and Transformers [Software]. Available from https://github.com/your_repository\r\n```\r\n\r\n## Dataset Example\r\nHere's an example of how your dataset should be structured:\r\n\r\n```json\r\n{\r\n \"data\": [\r\n {\r\n \"text\": \"This is an example text for classification.\",\r\n \"label\": 1\r\n },\r\n {\r\n \"text\": \"Another text for classification.\",\r\n \"label\": 0\r\n },\r\n ...\r\n ]\r\n}\r\n```\r\n\r\nEnsure that your dataset is properly formatted with the \"text\" field containing the input text and the \"label\" field containing the corresponding class label.\r\n\r\n## Conclusion\r\nThis 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!\r\n",
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