semvec/pypi-packages huggingface dataset:
python-sophy 0.1.0
A Slack notification utility
Requires Python: >=3.6

# Sophy

A simple Slack notification utility.

## Installation

```bash
pip install -e .
```

## Usage

```python
from sophy import Sophy

# Set SLACK_TOKEN environment variable first
slackbot = Sophy('user@example.com')
slackbot.notify('Hello World!')
```
d2py 0.7.0
Dive into Python.
Requires Python: >=3.10

# 动手学习 Python

![repo size](https://img.shields.io/github/repo-size/xinetzone/d2py.svg)
[![PyPI][pypi-badge]][pypi-link]
[![GitHub issues][issue-badge]][issue-link]
[![GitHub forks][fork-badge]][fork-link]
[![GitHub stars][star-badge]][star-link]
[![GitHub license][license-badge]][license-link]
[![contributors][contributor-badge]][contributor-link]
[![watcher][watcher-badge]][watcher-link]
[![Binder][binder-badge]][binder-link]
[![Downloads][download-badge]][download-link]
[![Documentation Status][status-badge]][status-link]
[![PyPI - Downloads][install-badge]][install-link]

学无止境！

[pypi-badge]: https://img.shields.io/pypi/v/d2py.svg
[pypi-link]: https://pypi.org/project/d2py/
[issue-badge]: https://img.shields.io/github/issues/xinetzone/d2py
[issue-link]: https://github.com/xinetzone/d2py/issues
[fork-badge]: https://img.shields.io/github/forks/xinetzone/d2py
[fork-link]: https://github.com/xinetzone/d2py/network
[star-badge]: https://img.shields.io/github/stars/xinetzone/d2py
[star-link]: https://github.com/xinetzone/d2py/stargazers
[license-badge]: https://img.shields.io/github/license/xinetzone/d2py
[license-link]: https://github.com/xinetzone/d2py/LICENSE
[contributor-badge]: https://img.shields.io/github/contributors/xinetzone/d2py
[contributor-link]: https://github.com/xinetzone/d2py/contributors
[watcher-badge]: https://img.shields.io/github/watchers/xinetzone/d2py
[watcher-link]: https://github.com/xinetzone/d2py/watchers
[binder-badge]: https://mybinder.org/badge_logo.svg
[binder-link]: https://mybinder.org/v2/gh/xinetzone/d2py/main
[install-badge]: https://img.shields.io/pypi/dw/d2py?label=pypi%20installs
[install-link]: https://pypistats.org/packages/d2py
[status-badge]: https://readthedocs.org/projects/d2py/badge/?version=latest
[status-link]: https://d2py.readthedocs.io/zh/latest/?badge=latest
[download-badge]: https://pepy.tech/badge/d2py
[download-link]: https://pepy.tech/project/d2py

## PyPI

支持 PyPI：

```sh
pip install d2py
```

## 文档模板

优质文档模板：

- [xbook](https://xinetzone.github.io/xbook/index.html)

## 欢迎共创

欢迎加入本项目的创作，创作规范请参阅：[Daobook demo 文档](https://daobook.github.io/.github/index.html)。
aiq-insights 0.1.2
A Python library for checking common errors in multiple-choice questions (MCQs)
Requires Python: >=3.6

# AI Q Insights

This library checks for common errors in multiple-choice questions (MCQs), including ambiguity, unequal option lengths, and duplicate questions.

## Installation

You can install the library via pip:

```bash
pip install aiq_insight

# import
from aiq_insights import MCQ, MCQChecker

# Sample MCQs
mcq1 = MCQ(
    question_text="What is the capital of France?",
    options=["Paris", "London", "Berlin", "Rome"],
    correct_option_index=0
)

mcq2 = MCQ(
    question_text="Paris is the capital of which country?",
    options=["France", "Germany", "Italy", "Italy"],
    correct_option_index=0
)

# Initialize checker
aiq_insights = MCQChecker()

# Analyze individual questions
result1 = aiq_insights.analyze_question(mcq1)
result2 = aiq_insights.analyze_question(mcq2)
print("Question 1 Analysis:", result1)
print("Question 2 Analysis:", result2)

# Analyze question set for duplicates
result_set = aiq_insights.analyze_question_set([mcq1, mcq2])
print("Question Set Analysis:", result_set)
tvsm-extractor 2.1.0
Extract TVSM dataset
Requires Python: >=3.10

### TVSM Extractor

Tooling for reading [this dataset](https://zenodo.org/records/7025971).

### Usage

Run from within a parent directory of unzipped TVSM datasets.
Files are output to "TVSM-extractor-audio" and "TVSM-extractor-images".

### Test data

All samples by default

```
tvsm-extractor
```

### Specific language

```
tvsm-extractor --language en
```

### Specific language and genre

```
tvsm-extractor --language en --genre Thrillers
```

## Specific file/files

```
tvsm-extractor test 3235
```

### Cue sheet training data

Note, the speech timings are very noisy.

```
tvsm-extractor cuesheet
```


### Pseudo training data

This project has not been tested on `TVSM-pseudo`.


### Dev

```
hatch build
hatch publish
```
compas-python-utils 0.0.2
COMPAS Python Utilities
Requires Python: >=3.8

[![Coverage Status](https://coveralls.io/repos/github/COMPAS-Surrogate/lnl_surrogate/badge.svg?branch=main)](https://coveralls.io/github/COMPAS-Surrogate/lnl_surrogate?branch=main)
[![PyPI version](https://badge.fury.io/py/compas_python_utils.svg?icon=si%3Apython)](https://badge.fury.io/py/compas_python_utils)


# Compas Python Utils

The python utilities for [COMPAS code](github.com/TeamCOMPAS/COMPAS).
piptuf-demo 0.3
A demo project.
Requires Python: >=3.6

TUF Signature for verification: 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
autonaming 0.1.0
Auto naming for experiments
Requires Python: >=3.9

# AutoNaming

A Python package that provides LLM-powered naming.

Currently supports automatically naming experiments using information from `parser` and `args` (from `argparse`).

Implemented using [APPL](https://github.com/appl-team/appl).

## Installation

```bash
pip install autonaming
```

## Usage

```python
from argparse import ArgumentParser
from autonaming.exps import name_this_exp

# Create your argument parser as usual
parser = ArgumentParser()
parser.add_argument("--learning_rate", type=float, default=0.001, help="Learning rate for training")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size for training")
parser.add_argument("--model", type=str, default="resnet18", help="Model architecture")

# Parse your arguments
args = parser.parse_args(["--learning_rate", "0.01", "--model", "alexnet"])

# Generate an informative name for your experiment
name = name_this_exp(parser, args, add_timestamp=True)
# Example output: alexnet_lr0.01_bs32__2024_12_11__12_34_56
# You can use this name to name your experiment directory
```

### Instructions for naming experiments

1. Generate a descriptive and concise name in snake_case that captures the key parameters
2. Keep names under 100 characters
3. Include the most important parameters that distinguish this experiment
4. Use standard abbreviations where appropriate (e.g. lr for learning_rate)
5. Order parameters from most to least important