semvec/pypi-packages huggingface dataset:
tomato 2.2rc2
au-tomation without pain
Requires Python: >=3.10

# tomato: au-tomation without pain!

[![Documentation](https://badgen.net/badge/docs/dgbowl.github.io/grey?icon=firefox)](https://dgbowl.github.io/tomato)
[![PyPi version](https://badgen.net/pypi/v/dgpost/?icon=pypi)](https://pypi.org/project/tomato)
![Github link](https://badgen.net/github/tag/dgbowl/tomato/?icon=github)
![Github status](https://badgen.net/github/checks/dgbowl/tomato/?icon=github)

`tomato` is the instrument automation package developed at the [ConCat lab at TU Berlin](https://tu.berlin/en/concat). Currently supported hardware is:

- [`tomato-example-counter`](https://github.com/dgbowl/tomato-example-counter): an example device for testing purposes.
- [`tomato-psutil`](https://github.com/dgbowl/tomato-psutil): an interface to the `psutil` library for CPU and memory usage statistics
- [`tomato-bronkhorst`](https://github.com/dgbowl/tomato-bronkhorst): an interface to the `bronkhorst-propar` library for control of Bronkhorst MFCs and PCs
- [`tomato-jumo`](https://github.com/dgbowl/tomato-jumo): a driver for control of Jumo Quantrol heater controllers using `minimalmodbus`
- [`tomato-picovna`](https://github.com/dgbowl/tomato-picovna): a driver for control of Pico Technology PicoVNA network analysers using the `vna` library
- [`tomato-mcc`](https://github.com/dgbowl/tomato-mcc): a driver for reading temperatures from MCC DAQ boards using `mcculw` on Windows

See the [Documentation](https://dgbowl.github.io/tomato) for more detailed info.

## Contributors:
- [Peter Kraus](http://github.com/PeterKraus)
- [Alexandre Gbocho](https://github.com/AlexN7-Shepard)
- [Loris Ercole](http://github.com/lorisercole)
- [Graham Kimbell](http://github.com/g-kimbell)

## Acknowledgements

This project has received funding from:
- the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957189.
- the Emmy Noether Programme of the Deutsche Forschungsgemeinschaft (DFG) under project No 490703766.
- the Priority Programme (SPP) 2419 HyCAM of the Deutsche Forschungsemeinschaft (DFG) under project No 523873650.

The project is part of BATTERY 2030+, the large-scale European research initiative for inventing the sustainable batteries of the future.
nonebot-plugin-netease-name 0.1.0.post3
Nonebot plugin for for generating Minecraft Netease Version nickname
Requires Python: <4.0,>=3.10

<div align="center">
  <a>
    <img src="https://github.com/user-attachments/assets/b5162036-5b17-4cf4-b0cb-8ec842a71bc6" width="200" alt="NB Logo">
  </a>
  <h1>NeteaseName</h1>
  <h3>网易昵称生成器！</h3>

  <p>
    <a href="https://www.python.org/">
      <img src="https://img.shields.io/badge/python->=3.10-blue?logo=python&style=flat-square" alt="Python Version">
    </a>
    <a href="https://nonebot.dev/">
      <img src="https://img.shields.io/badge/nonebot2->=2.4.2-blue?style=flat-square" alt="NoneBot Version">
    </a>
    <a href="LICENSE">
      <img src="https://img.shields.io/github/license/JohnRichard4096/nonebot_plugin_suggarchat?style=flat-square" alt="License">
    </a>
    <a href="https://qm.qq.com/q/PFcfb4296m">
      <img src="https://img.shields.io/badge/QQ%E7%BE%A4-1002495699-blue?style=flat-square" alt="QQ Group">
    </a>
  </p>
</div>

# 版权

* 本项目基于wangyupu的[NNG](https://github.com/wang-yupu/netease_mc_name_generator)

* 基于GPL v3.0协议进行开源。

# 快速开始

1. 安装依赖

  ```bash
  uv install nonebot_plugin_neteasename
  ```

2. 声明插件

  打开pyproject.toml文件，在[tool.nonebot]节点下添加如下内容：

  ```toml
  plugins = ["nonebot_plugin_neteasename"]
  ```

# 指令说明

```explaintext
/name
/网易昵称
```

获取随机生成的网易风格昵称

# 配置文件

我们使用`nonebot_plugin_localstore`管理存储，在插件配置目录下有个name_strus.json文件，里面是所有可能的昵称结构，你可以修改这个文件来修改昵称结构。
casrel-datautils 1.1.5
A Chinese relation extraction data utility toolkit based on CasRel model
Requires Python: >=3.7

CasRel 数据处理工具包

这是一个基于 CasRel 模型的中文关系抽取数据处理工具包，支持 BERT 预训练模型。

## 支持的 BERT 模型

- **bert-base-chinese**: 默认中文 BERT 基础模型，适用于通用中文任务，参数量适中，bert_dim=768。

## 安装

- pip install casrel_datautils

## 使用示例

以下是一个使用 `casrel_datautils` 进行数据加载和单条样本处理的示例代码：

```python
from casrel_datautils.Base_Conf import BaseConfig
from casrel_datautils.data_loader import get_dataloader
from casrel_datautils.process import single_sample_process

# 配置基础参数
baseconf = BaseConfig(
    bert_path_or_bertModel_name="bert-base-chinese", #模型路径或模型名称
    train_data=r"本地数据路径train.json",
    test_data=r"本地数据路径test.json",
    rel_data=r"本地关系数据路径relation.json",
    batch_size=2
)

# 获取数据加载器
dataloaders = get_dataloader(baseconf)

# 单条样本处理
sample = {"text": "这是一个测试句子"}
input_tensor, mask_tensor = single_sample_process(baseconf, sample)
print(input_tensor.shape)
print(mask_tensor.shape)
```

### 说明

- **BaseConfig**: 用于设置 BERT 模型路径、数据路径和批次大小等参数。
- **get_dataloader**: 返回训练、验证和测试的数据加载器。
- **single_sample_process**: 处理单条文本样本，返回输入张量和掩码张量。

## 注意事项

- 确保数据文件（如 `train.json`、`test.json`、`relation.json`）路径正确。
- 根据任务需求选择合适的 BERT 模型。