semvec/pypi-packages huggingface dataset:
acace 0.1.1
Adaptive Context-Aware Content Engine (ACACE) – Main orchestrator package for optimizing token usage and preserving contextual coherence across LLM content generation workflows.
Requires Python: <4.0,>=3.8

# acace

Adaptive Context-Aware Content Engine (ACACE) – Main orchestrator package for optimizing token usage and preserving contextual coherence across LLM content generation workflows.

## Installation

```bash
pip install acace
```

## Features

- Core functionality for ACACE
- Integration with other ACACE components
- Standardized interfaces

## Usage

```python
from acace import main_function

result = main_function()
```

## Documentation

Detailed documentation can be found in the [docs](docs/) directory.

## Integration with ACACE

This package is designed to work seamlessly with the main [ACACE](https://github.com/Sumedh1599/acace) library.

## License

MIT License
acace-token-weightor 0.1.1
Assigns importance weights to tokens using NLP heuristics for compression prioritization.
Requires Python: <4.0,>=3.8

# acace_token_weightor

Assigns importance weights to tokens using NLP heuristics for compression prioritization.

## Installation

```bash
pip install acace_token_weightor
```

## Features

- Core functionality for ACACE
- Integration with other ACACE components
- Standardized interfaces

## Usage

```python
from acace_token_weightor import main_function

result = main_function()
```

## Documentation

Detailed documentation can be found in the [docs](docs/) directory.

## Integration with ACACE

This package is designed to work seamlessly with the main [ACACE](https://github.com/Sumedh1599/acace) library.

## License

MIT License
acace-tokenizer 0.1.1
Efficient tokenizer tailored for semantic weighting and context-aware transformation pipelines.
Requires Python: <4.0,>=3.8

# acace_tokenizer

Efficient tokenizer tailored for semantic weighting and context-aware transformation pipelines.

## Installation

```bash
pip install acace_tokenizer
```

## Features

- Core functionality for ACACE
- Integration with other ACACE components
- Standardized interfaces

## Usage

```python
from acace_tokenizer import main_function

result = main_function()
```

## Documentation

Detailed documentation can be found in the [docs](docs/) directory.

## Integration with ACACE

This package is designed to work seamlessly with the main [ACACE](https://github.com/Sumedh1599/acace) library.

## License

MIT License
ml-etl-package 0.1.0
A simple ML pipeline package with ETL steps using scikit-learn
Requires Python: >=3.8

# README.md
# ML ETL Package

A lightweight Python package for running a simple Machine Learning ETL pipeline using Scikit-learn.

## Features
- 📥 Extract: Load data from CSV files
- 🔧 Transform: Clean and scale data with `StandardScaler`
- 📤 Load: Save transformed data to CSV
- 🤖 Model: Train and save a Linear Regression model
- 🔮 Predict: Make predictions from scaled inputs

## Installation
```bash
pip install ml-etl-package
```

## Usage Example
```python
from ml_etl_package.extract import extract_from_csv
from ml_etl_package.transform import transform
from ml_etl_package.load import save_to_csv
from ml_etl_package.model import train_and_save_model, load_model
from ml_etl_package.predict import predict

# Extract
df = extract_from_csv("data.csv")

# Transform
X, y, scaler = transform(df, feature_cols=["sqft", "bedrooms", "bathrooms"], target_col="price")

# Load (save transformed data)
save_to_csv(X, y, filename="transformed_output.csv")

# Train
train_and_save_model(X, y)
model = load_model()

# Predict
input_data = [1200, 3, 2]  # sqft, bedrooms, bathrooms
result = predict(model, scaler, input_data)
print(f"Predicted price: {result:.2f}")
```

## License
MIT