semvec/pypi-packages huggingface dataset:
NLPUtilsBert 0.0.1
Common Utilities
License: MIT
Requires Python: >=3.11.9

# NLPUtilsBert: Advanced NLP Utilities for Text Classification and Analysis

## Overview

NLPUtilsBert is a comprehensive Python package designed to simplify text classification and exploratory data analysis (EDA) using state-of-the-art NLP techniques. The package leverages BERT models and provides intuitive tools for machine learning practitioners and researchers.

## Features

### Text Classification
- Supports BERT and transformer-based models
- Easy model training and evaluation
- Comprehensive metrics visualization
- Supports early stopping and mixed-precision training
- Automatic handling of model checkpoints

### Text Exploratory Data Analysis (EDA)
- Word cloud generation per category
- Text preprocessing using spaCy
- Category distribution visualization
- Lemmatization and stop-word removal

## Installation

```bash
pip install nlputilsbert
```

## Quick Start

### Text Classification

```python
from nlputilsbert import TextClassificationModel
import pandas as pd

# Initialize the model
model = TextClassificationModel(
    model_name='bert-base-uncased', 
    batch_size=16, 
    learning_rate=2e-5, 
    num_train_epochs=3
)

# Prepare your data
df = pd.read_csv('your_dataset.csv')
y = model.get_y(df, target_col='category', id2label_path='label_mapping.json')

# Create datasets
ds_train, ds_val, ds_test = model.create_datasets(df, y)

# Train the model
model.train(ds_train, ds_val)

# Evaluate
model.evaluate(ds_test)

# Make predictions
prediction = model.predict("Your sample text")
```

### Text EDA

```python
from nlputilsbert import TextEDA
import pandas as pd

# Initialize EDA
eda = TextEDA(
    dataframe=df, 
    text_column='text', 
    label_column='category', 
    plots_dir='eda_plots'
)

# Perform EDA
eda.perform_eda()
```

## Requirements

Python 3.9+
- pandas==2.2.3
- seaborn==0.13.2
- spacy==3.8.2
- wordcloud==1.9.4
- torch==2.5.1
- scikit_learn==1.5.2
- transformers==4.46.3
- torchvision==0.20.1
- accelerate==1.1.1

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License.

## Acknowledgements

- Hugging Face Transformers
- spaCy
- scikit-learn
infra-sdk 0.1.0
Infrastructure management CLI using OpenTofu
License: MIT
Requires Python: >=3.8
Keywords: cli, infrastructure, opentofu, terraform

# Infra SDK

A powerful CLI tool that simplifies infrastructure management using OpenTofu (a Terraform fork).

## Features

- Environment-based infrastructure management
- Interactive module selection
- State management with SQLite
- Project configuration with `infra.yaml`
- Support for multiple infrastructure modules

## Installation

```bash
pip install infra-sdk
```

## Quick Start

1. Initialize a new project:
```bash
infra init
```

2. Create an environment:
```bash
infra env create dev
```

3. Create infrastructure from a module:
```bash
infra create path/to/module
```

## Project Structure

After initialization, your project will have this structure:
```
your-project/
├── infra.yaml          # Project configuration
└── .infra/            # State directory (configurable)
    ├── state.db       # SQLite database
    ├── states/        # OpenTofu state files
    └── temp/          # Temporary module copies
```

## Commands

- `infra init` - Initialize a new project
- `infra env create <name>` - Create a new environment
- `infra env list` - List all environments
- `infra create <module>` - Create infrastructure from a module
- `infra destroy [module]` - Destroy infrastructure

## Requirements

- Python >= 3.8
- OpenTofu binary (default path: `~/.launchflow/bin/tofu`)

## License

MIT License