semvec/pypi-packages huggingface dataset:
librecubo_geometric 0.0.1
This is a template repository for Python projects that use Poetry for their dependency management.
Requires Python: >=3.9,<4.0

# librecubo-geometric

[![Release](https://img.shields.io/github/v/release/jfloresf/librecubo-geometric)](https://img.shields.io/github/v/release/jfloresf/librecubo-geometric)
[![Build status](https://img.shields.io/github/actions/workflow/status/jfloresf/librecubo-geometric/main.yml?branch=main)](https://github.com/jfloresf/librecubo-geometric/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/jfloresf/librecubo-geometric/branch/main/graph/badge.svg)](https://codecov.io/gh/jfloresf/librecubo-geometric)
[![Commit activity](https://img.shields.io/github/commit-activity/m/jfloresf/librecubo-geometric)](https://img.shields.io/github/commit-activity/m/jfloresf/librecubo-geometric)
[![License](https://img.shields.io/github/license/jfloresf/librecubo-geometric)](https://img.shields.io/github/license/jfloresf/librecubo-geometric)

This is a template repository for Python projects that use Poetry for their dependency management.

- **Github repository**: <https://github.com/jfloresf/librecubo-geometric/>
- **Documentation** <https://jfloresf.github.io/librecubo-geometric/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:jfloresf/librecubo-geometric.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPi or Artifactory, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

## Releasing a new version

- Create an API Token on [Pypi](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting [this page](https://github.com/jfloresf/librecubo-geometric/settings/secrets/actions/new).
- Create a [new release](https://github.com/jfloresf/librecubo-geometric/releases/new) on Github.
- Create a new tag in the form `*.*.*`.

For more details, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
sentia 1.17
A text generation model combining multiple neural network architectures
Requires Python: >=3.10

# SENTIA

SENTIA is a PyTorch implementation of a text generation model combining multiple neural network architectures like GRUs, Transformers, MHAs and MEPA.

## Installation

```bash
pip install sentia
```
# Usage
```python
import torch
from sentia import SENTIA

# Create model
model = SENTIA(vocab_size=10000, embedding_dim=512, num_heads=8, num_layers=6, hidden_dim=512)

# Forward pass
input_ids = torch.randint(0, 10000, (1,32)) 
outputs = model(input_ids)

# Generate text 
generated = model.generate(input_ids, max_length=128)
```
# Model Architecture
The SENTIA model consists of the following components:

- Embedding layer
- GRU layer
- MEPA (Mutation Enhanced Plasticity Architecture) layers
- Transformer decoder layers
- Multi-head attention layer
- Output head layers
These components are combined to leverage the strengths of multiple architectures for improved text generation capabilities.
# Training
The fit() method can bne used to train the model on a dataset. It handles the training loop, gradient accumulation, and RL calculations. Currently the scheduler parameter only supports StepLR
librecubo_smoother 0.0.1
This is a template repository for Python projects that use Poetry for their dependency management.
Requires Python: >=3.9,<4.0

# librecubo-smoother

[![Release](https://img.shields.io/github/v/release/jfloresf/librecubo-smoother)](https://img.shields.io/github/v/release/jfloresf/librecubo-smoother)
[![Build status](https://img.shields.io/github/actions/workflow/status/jfloresf/librecubo-smoother/main.yml?branch=main)](https://github.com/jfloresf/librecubo-smoother/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/jfloresf/librecubo-smoother/branch/main/graph/badge.svg)](https://codecov.io/gh/jfloresf/librecubo-smoother)
[![Commit activity](https://img.shields.io/github/commit-activity/m/jfloresf/librecubo-smoother)](https://img.shields.io/github/commit-activity/m/jfloresf/librecubo-smoother)
[![License](https://img.shields.io/github/license/jfloresf/librecubo-smoother)](https://img.shields.io/github/license/jfloresf/librecubo-smoother)

This is a template repository for Python projects that use Poetry for their dependency management.

- **Github repository**: <https://github.com/jfloresf/librecubo-smoother/>
- **Documentation** <https://jfloresf.github.io/librecubo-smoother/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:jfloresf/librecubo-smoother.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPi or Artifactory, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

## Releasing a new version

- Create an API Token on [Pypi](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting [this page](https://github.com/jfloresf/librecubo-smoother/settings/secrets/actions/new).
- Create a [new release](https://github.com/jfloresf/librecubo-smoother/releases/new) on Github.
- Create a new tag in the form `*.*.*`.

For more details, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
umbrage-evals 0.0.1
Umbrage Evals SDK
Requires Python: >=3.9

# Umbrage Evals Python SDK

## Overview

This is the Python SDK for Umbrage Evals.

## Installation

To install the package, run the following command:

```bash
pip install umbrage-evals
```

## Dependencies

-   Python 3.9+

## Example usage

```python
# TODO: Add example usage
```

## LICENSE
Copyright (c) 2023 Umbrage Studios, LLC

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to use the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
DdlGeneratorTool 1.2.1
DDL文件解析程序

# ddl code generation tool

Currently, only supports generating Java objects.

## Installation

```shell
pip3 install DdlGeneratorTool
```

## ddl2Pojo Usage

1. 从文件创建

```shell
DdlGeneratorTool kaquan_user_dicount.sql
```

2. 从粘贴板直接创建

```shell
 DdlGeneratorTool --p true   
```

- By default, the result will be printed to the current terminal window.
- If you want to save it to a file, you can use the `--ddl_file` parameter.

## 效果

![](https://vison-blog.oss-cn-beijing.aliyuncs.com/20231201111616.png)

## development

```shell
python3 setup.py sdist bdist_wheel 
twine upload dist/*
```