semvec/pypi-packages huggingface dataset:
synthai-generator 0.1.0
A framework for generating synthetic data using LLMs with techniques to ensure diversity and reduce bias
Requires Python: >=3.8

# SynthAI: Synthetic Data Generation Framework

[![PyPI version](https://img.shields.io/pypi/v/synthai.svg)](https://pypi.org/project/synthai/)
[![Python Version](https://img.shields.io/pypi/pyversions/synthai.svg)](https://pypi.org/project/synthai/)
[![License](https://img.shields.io/github/license/biswanathroul/synthai.svg)](https://github.com/biswanathroul/synthai/blob/main/LICENSE)

SynthAI is a lightweight framework for generating high-quality synthetic data using LLMs with techniques to ensure diversity and reduce bias.

## Features

- 🤖 **LLM-Powered Generation**: Create realistic synthetic data using language models
- 🧩 **Domain Adapters**: Specialized components for different data domains (text, tabular, time-series)
- 🔄 **Diversity Enhancement**: Built-in techniques to increase diversity in generated data
- ⚖️ **Bias Reduction**: Methods to detect and mitigate bias in synthetic datasets
- 📊 **Quality Evaluation**: Tools to measure the quality and utility of generated data
- 🚀 **Resource Efficiency**: Optimized to work with lighter models and minimal compute requirements

## Installation

```bash
pip install synthai
```

## Quick Start

```python
from synthai import SyntheticDataGenerator
from synthai.generators import TextGenerator
from synthai.evaluators import DiversityEvaluator

# Initialize a generator
generator = SyntheticDataGenerator(
    generator_type=TextGenerator(model="distilgpt2"),
    domain="customer_reviews"
)

# Generate synthetic data
synthetic_data = generator.generate(
    num_samples=100,
    prompt_template="Write a {sentiment} review for a {product_type}",
    parameters={
        "sentiment": ["positive", "negative", "neutral"],
        "product_type": ["smartphone", "laptop", "headphones"]
    }
)

# Evaluate diversity of the generated data
evaluator = DiversityEvaluator()
diversity_score = evaluator.evaluate(synthetic_data)
print(f"Diversity score: {diversity_score}")

# Save the generated data
generator.save_data(synthetic_data, "synthetic_reviews.csv")
```

## Documentation

For full documentation, visit [synthai.readthedocs.io](https://synthai.readthedocs.io).

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Author

- **Biswanath Roul** - [GitHub](https://github.com/biswanathroul)

## Acknowledgments

Special thanks to the open-source community and the advancements in LLM technology that make this library possible.
mavehgvs 0.7.0
Regular expression-based validation of HGVS-style variant strings for Multiplexed Assays of Variant Effect.
Requires Python: >=3.6

[![Build Status](https://travis-ci.com/VariantEffect/mavehgvs.svg?branch=main)](https://travis-ci.com/VariantEffect/mavehgvs)
[![Coverage Status](https://coveralls.io/repos/github/VariantEffect/mavehgvs/badge.svg?branch=main)](https://coveralls.io/github/VariantEffect/mavehgvs?branch=main)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

# mavehgvs
mavehgvs is the Python reference implementation of the MAVE-HGVS variant representation standard,
a strict subset of [HGVS](http://varnomen.hgvs.org/), used primarily for clinical genomics.

## The MAVE-HGVS Standard
MAVE-HGVS is a strict subset of the [HGVS Sequence Variant Nomenclature](https://varnomen.hgvs.org/), version 20.05.
HGVS nomenclature is comprehensive and very expressive and consequently includes a lot of syntax that is not needed to
represent variants from Multiplexed Assay of Variant Effect (MAVE) data and makes the variant strings more challenging
to parse.

While packages exist for parsing HGVS (most notably the
[biocommons hgvs package](https://github.com/biocommons/hgvs/), they are intended for use in human genetics and
rely on sequence databases and reference sequence (called "target sequence" for MAVE-HGVS), which are not always
available for or relevant for multiplexed assays.

MAVE-HGVS is an attempt to define an easy-to-parse subset of the HGVS nomenclature that captures those variants that
occur in MAVE datasets, while excluding many variant types that are unlikely to be found. Importantly, the
mavehgvs implementation does not rely on external sequence databases or identifiers.

## Supported Variants
MAVE-HGVS supports DNA, RNA, and protein variants.
MAVE-HGVS supports a subset of HGVS variants including:

* substitutions
* deletions
* duplications
* insertions
* frame shifts

Many HGVS variants are unsupported including:

* inversions
* conversions
* extensions
* changes in methylation state
* RNA fusion transcripts
* mosaicism
* chimerism
* variants with uncertain consequence
* variants in trans or unknown phase
* complex variants (e.g. translocations)

For further details, including example variants, see the specification in the package documentation.

# Installation
Install mavehgvs from pip using:

```bash
pip3 install mavehgvs
```

To set up the package for development purposes, include the optional dependencies and
install pre-commit:

    pip3 install mavehgvs[dev]
    pre-commit install

# Feedback
To report a problem or request a new feature with either the mavehgvs package or the MAVE-HGVS standard,
please use the GitHub issue tracker.
vwc 0.0.2
wc with a preview
Requires Python: >=3.10

# 🚻👀 `vwc`

Like `wc`, but with a live preview output to stderr if it's a tty.

## ▶️ installing

```bash
$ pip install vwc
$ echo "hey" | wc -l
1
```

## ✅ To Do

 [ ] Integration tests
 [ ] Detect platform and mirror it
 [ ] Compile with Cython if available

## ⚖️ License

WTFP with one additional clause:

* ⛔ Don't blame me

## Links

* [🏠 home](https://bitplane.net/dev/python/vwc)
* [🐱 github](https://github.com/bitplane/vwc)
* [🐍 pypi](https://pypi.org/project/vwc)
* [📖 pydoc](https://bitplane.net/dev/python/vwc/pydoc)
databloom 2.1.23
A Python SDK for data integration with Nessie and Iceberg
Requires Python: >=3.11.8

# DataBloom SDK Client

A Python SDK client for data integration with PostgreSQL, MySQL, Nessie, and S3.

## Quick Start

```bash
# Setup environment
conda create -n data_bloom python=3.11
conda activate data_bloom

# Install
pip install -e ".[dev]"
```

## Configuration

Create `.env` file with your credentials:

## Testing

```bash
# Run all tests
make test
```

## Development

```bash
make format          # Format code
make lint           # Run linter
make doc            # Build docs
```

## License

VNG License


python3 -m twine upload --verbose --repository-url https://repo.ird.vng.vn/repository/pypi-all/simple -u admin -p "nmz2whg5BQM3hjh@pya" dist/databloom-2.1.19*