semvec/pypi-packages huggingface dataset:
jaraco.pmxbot 6.2.0
pmxbot commands by jaraco
Requires Python: >=3.8

.. image:: https://img.shields.io/pypi/v/jaraco.pmxbot.svg
   :target: https://pypi.org/project/jaraco.pmxbot

.. image:: https://img.shields.io/pypi/pyversions/jaraco.pmxbot.svg

.. image:: https://github.com/jaraco/jaraco.pmxbot/workflows/tests/badge.svg
   :target: https://github.com/jaraco/jaraco.pmxbot/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/psf/black
   :alt: Code style: Black

.. .. image:: https://readthedocs.org/projects/PROJECT_RTD/badge/?version=latest
..    :target: https://PROJECT_RTD.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2023-informational
   :target: https://blog.jaraco.com/skeleton

Plugins for `pmxbot <https://pypi.org/project/pmxbot>`_ by jaraco.
sescore2 1.0.8
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes
Requires Python: >=3.8.0,<4.0.0

<h1>SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes</h1>

SESCORE2, is a SSL method to train a metric for general text generation tasks without human ratings. We develop a technique to synthesize candidate sentences with varying levels of mistakes for training. To make these self-constructed samples realistic, we introduce retrieval augmented synthesis on anchor text; It outperforms SEScore in four text generation tasks with three languages (The overall kendall correlation improves 14.3%).

<h3>Paper: https://arxiv.org/abs/2212.09305</h3>

<h3>Author Email: wendaxu@cs.ucsb.edu</h3>

<h3>Maintainer Email: zihan_ma@ucsb.edu</h3>

<h3>Install all dependencies:</h3>

````
```
pip install sescore2
```
````

<h3>Instructions to score sentences using SEScore2:</h3>

Currently, the PyPI version only support English (en) and German (de) Checkpoint. The model checkpoint is trained using mT5-xl and using Human rating data to fine-tune.

To run SEScore2 for text generation evaluation:

````
```
from sescore2 import SEScore2

scorer = SEScore2('en') # Download and load in metric with specified language, en (English), de (German), ja ('Japanese')

refs = ["Jova becomes Western Hemisphere's strongest hurricane so far in 2023 ... for now", "Jova becomes Western Hemisphere's strongest hurricane so far in 2023 ... for now"]

outs = ["Jova set to become Western Hemisphere's most powerful hurricane in 2023...so far", "Jova set to become Western Hemisphere's weakest hurricane in 2023"]

scores_ls = scorer.score(refs, outs, 1)
```
````


<h2>GitHub Page</h2>

If you want to reproduce the synthetic data, and use the original XLM/RemBERT SEScore2 weight, please refer to the GitHub repository: https://github.com/xu1998hz/SEScore2

<h3>Install all dependencies for GitHub version:</h3>

````
```
pip install -r requirement/requirements.txt

# To evaluate WMT shared metric task using official script
git clone https://github.com/google-research/mt-metrics-eval.git
cd mt-metrics-eval
pip install .

# Download evaluation data for WMT20, 21 and 22
alias mtme='python3 -m mt_metrics_eval.mtme'
mtme --download  # Puts ~1G of data into $HOME/.mt-metrics-eval.
```
````

<h3>Score sentences using SEScore2 for GitHub version:</h3>

Download weights and data from Google Drive (https://drive.google.com/drive/folders/1I9oji2_rwvifuUSqO-59Fi_vIok_Wvq8?usp=sharing)
We support five languages: English, German, Spanish, Chinese and Japanese.


````
```
from SEScore2 import SEScore2
from train.regression import *

scorer = SEScore2('en') # load in metric with specified language, en (English), de (German), ja ('Japanese')

refs = ["SEScore is a simple but effective next generation text generation evaluation metric", "SEScore it really works"]

outs = ["SEScore is a simple effective text evaluation metric for next generation", "SEScore is not working"]

scores_ls = scorer.score(refs, outs, 1)
```
shared-python-library 1.0.1
# airtech-shared-python-library
Requires Python: >=3.7, <4
Keywords: shared,library,postgres

# airtech-shared-python-library

## Descrição

O airtech-shared-python-library é um projeto de biblioteca compartilhada que oferece funcionalidades específicas, com suporte para PostgreSQL por meio de um adaptador dedicado.

## Requisitos

- Python
- PostgreSQL

## Instalação

Certifique-se de ter o Python e o PostgreSQL instalados no seu ambiente. Em seguida, execute os seguintes comandos:

```bash
pip install airtech-shared-python-library
```

## Configuração do Adaptador PostgreSQL

Para utilizar o adaptador PostgreSQL, é necessário configurar as informações de conexão. Aqui está um exemplo de como configurar o adaptador no seu código Python:

```python
from airtech_shared_python_library.adaptadores import PostgreSQLConnection

# Configuração do adaptador PostgreSQL
conexao_postgres = PostgreSQLConnection(
    host='localhost',
    port=5432,
    database='seu_banco_de_dados',
    user='seu_usuario',
    password='sua_senha'
)
```

Lembre-se de substituir as informações acima pelos detalhes específicos do seu ambiente.

## Uso do Adaptador PostgreSQL

Agora que o adaptador está configurado, você pode usá-lo para realizar consultas no banco de dados PostgreSQL. Aqui está um exemplo básico:

```python
# Exemplo de uso do adaptador PostgreSQL
with conexao_postgres as conexao:
    cursor = conexao.create_cursor()
    resultado = conexao.execute_query("SELECT * FROM sua_tabela;")
    for linha in resultado.fetchall():
        print(linha)
```

## Contribuição

Se você quiser contribuir para este projeto, siga estas etapas:

- Fork do projeto
- Crie uma nova branch (git checkout -b feature/nova-feature)
- Faça commit das suas alterações (git commit -m 'Adicionar nova feature')
- Faça push para a branch (git push origin feature/nova-feature)
- Abra um pull request