# TextPredict
[](https://www.python.org)  [](https://github.com/psf/black) [](https://github.com/astral-sh/ruff) [](https://github.com/PyCQA/bandit) [](https://pepy.tech/project/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
```sh
pip install textpredict
```
### Usage
#### Sentiment Analysis
```python
from textpredict import TextPredict
tp = TextPredict()
result = tp.analyse("I love using this package!", task="sentiment")
print(result)
```
#### Emotion Detection
```python
result = tp.analyse("I am excited about this!", task="emotion")
print(result)
```
#### Zero-Shot Classification
```python
result = tp.analyse(
"This package is great for zero-shot learning.",
task="zeroshot",
class_list=["positive", "negative", "neutral"],
)
print(result)
```
#### Fine-Tuning Models
```python
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
```python
metrics = tp.evaluate_model(task="sentiment", eval_data=dataset["test"])
print("Evaluation metrics:", metrics)
```
#### Saving and Loading Models
```python
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](https://github.com/ankit-aglawe/textpredict)
- **PyPI Project**: [PYPI](https://pypi.org/project/textpredict/)
- **Documentation**: [Readthedocs](https://github.com/ankit-aglawe/sentimentpredictor#readme)
- **Source Code**: [Source Code](https://github.com/ankit-aglawe/sentimentpredictor)
- **Issue Tracker**: [Issue Tracker](https://github.com/ankit-aglawe/sentimentpredictor/issues)
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"description": "# TextPredict\n\n[](https://www.python.org)  [](https://github.com/psf/black) [](https://github.com/astral-sh/ruff) [](https://github.com/PyCQA/bandit) [](https://pepy.tech/project/textpredict)\n\n\n\n\n## Advanced Text Classification with Transformer Models\n\n**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).\n\n### Features\n\n- **Sentiment Analysis**: Classify text based on sentiment with high accuracy.\n- **Emotion Detection**: Detect emotions in text effortlessly.\n- **Zero-Shot Classification**: Classify text into unseen categories without any additional training.\n- **Fine-Tuning**: Easily fine-tune models to improve performance on specific datasets.\n- **Model Evaluation**: Evaluate model performance with robust metrics.\n- **Distributed Training**: Support for distributed training to leverage multiple GPUs.\n\n### Installation\n\n```sh\npip install textpredict\n```\n\n### Usage\n\n#### Sentiment Analysis\n\n```python\nfrom textpredict import TextPredict\n\ntp = TextPredict()\nresult = tp.analyse(\"I love using this package!\", task=\"sentiment\")\nprint(result)\n```\n\n#### Emotion Detection\n\n```python\nresult = tp.analyse(\"I am excited about this!\", task=\"emotion\")\nprint(result)\n```\n\n#### Zero-Shot Classification\n\n```python\nresult = tp.analyse(\n \"This package is great for zero-shot learning.\",\n task=\"zeroshot\",\n class_list=[\"positive\", \"negative\", \"neutral\"],\n)\nprint(result)\n```\n\n#### Fine-Tuning Models\n\n```python\nfrom datasets import load_dataset\n\n# Load dataset\ndataset = load_dataset(\"imdb\")\n\n# Fine-tune model\ntp.tune_model(\n task=\"sentiment\",\n training_data=dataset[\"train\"],\n eval_data=dataset[\"test\"],\n num_train_epochs=1,\n batch_size=8,\n learning_rate=2e-5,\n early_stopping_patience=3,\n)\n```\n\n#### Evaluating Models\n\n```python\nmetrics = tp.evaluate_model(task=\"sentiment\", eval_data=dataset[\"test\"])\nprint(\"Evaluation metrics:\", metrics)\n```\n\n#### Saving and Loading Models\n\n```python\ntp.save_model(task=\"sentiment\", output_dir=\"./fine_tuned_sentiment_model\")\ntp.load_model(task=\"sentiment\", model_dir=\"./fine_tuned_sentiment_model\")\nresult = tp.analyse(\"I love using this package after fine-tuning!\", task=\"sentiment\")\nprint(result)\n```\n\n\n### Contributing\n\nContributions are welcome! Please feel free to submit a pull request or open an issue on GitHub.\n\n### License\n\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\n### Links\n\n- **GitHub Repository**: [Github](https://github.com/ankit-aglawe/textpredict)\n- **PyPI Project**: [PYPI](https://pypi.org/project/textpredict/)\n- **Documentation**: [Readthedocs](https://github.com/ankit-aglawe/sentimentpredictor#readme)\n- **Source Code**: [Source Code](https://github.com/ankit-aglawe/sentimentpredictor)\n- **Issue Tracker**: [Issue Tracker](https://github.com/ankit-aglawe/sentimentpredictor/issues)\n",
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