semvec/pypi-packages huggingface dataset:
nonebot-plugin-todo-nlp 0.1.4
一款自动识别提醒内容，可生成todo图片并定时推送的nonebot插件
License: MIT
Requires Python: >=3.9,<4.0

# nonebot-plugin-todo-nlp

一款自动识别提醒内容，可生成todo图片并定时推送的nonebot2插件，v11适配器可用

nlp支持来源于[jionlp](https://github.com/dongrixinyu/JioNLP) （十分便利的nlp库）

图片生成功能来源于nonebot插件[htmlrender](https://github.com/kexue-z/nonebot-plugin-htmlrender) 
（我们先进的的浏览器制图已经完全超越了老式的PIL制图了（不是））

### 插件特点：

* 允许多样化的日期描述方法，可以在语句中包含“明天”、“9月1日”等日期提示
* 自动识别语句中的事项

### 安装

#### 从 PyPI 安装（推荐）

- 使用 nb-cli  

```
nb plugin install nonebot_plugin_todo_nlp
2022-08-29:等一个pull request merged，暂时用不了
```

- 使用 poetry

```
poetry add nonebot_plugin_todo_nlp
```

- 使用 pip

```
pip install nonebot_plugin_todo_nlp
```

#### 从 GitHub 安装（不推荐）

```
git clone https://github.com/Jigsaw111/nonebot_plugin_todo.git
```


### 食用方法：
触发关键词：
* 增加事项: '提醒', 'nonebot_todo'
* 完成事项: '完成'
* 删除事项（支持正则表达式）: '删除', '去掉'
* 修改事项时间: '更正', '改'
* 获取图片: '获取todo'

> 若要强调事件名称（nlp有的时候会犯蠢，比如在示例中机器人没能识别出“中秋假期”这一关键词）：使用英文双引号括上事项名称

> **由于nonebot使用uvicorn框架，在windows平台使用时图片的导出可能会出现not implemented error。**\
> 解决方法：将env中的FASTAPI_RELOAD改为false。\
> 见nonebot-plugin-htmlrender [issue#25](https://github.com/kexue-z/nonebot-plugin-htmlrender/issues/25) ;\
> nonebot2文档中对此亦有提及，见[fastapi_reload](https://nb2.baka.icu/docs/tutorial/choose-driver#fastapi_reload) 。

### 配置方法：
在env中添加如同以下格式的配置（多个send time则多次推送，注意时间首位去0）：\
在群聊中使用时，只有管理员和群主可以增删todo项目。
私聊情况下，好友均可使用todo增删功能，此处配置是推送名单。
```
TODO_USERS=["1234567890"]
TODO_GROUPS=["1234567890"]
TODO_SEND_TIME=[{"HOUR":8,"MINUTE":0},{"HOUR":19,"MINUTE":34}]
```

### TODO：
- [ ] 迁移到postgre数据库
- [ ] 增加优先级相关功能支持
- [ ] 增加完成todo统计，对完成状况进行跟踪
- [ ] 增加todo项目复用功能（比如每日/每周某天的提醒可以复用而不用手动再次添加）
- [ ] 完善相关console log
- [ ] 更加优雅的todo使用订阅与推送时间配置
- [ ] （可能后期会加上其他的todo主题？）

### 示例：

![1.png](https://github.com/CofinCup/nonebot_plugin_todo_nlp/blob/master/readme_resource/1.png)

![2.jpg](https://github.com/CofinCup/nonebot_plugin_todo_nlp/blob/master/readme_resource/2.jpg)
pyriandx 0.3.0
API client CLI/SDK for PierianDx web services
License: MIT
Requires Python: >=3.6

# pyriandx

![Pull Request Build Status](https://github.com/umccr/pyriandx/workflows/Pull%20Request%20Build/badge.svg)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/pyriandx?style=flat)](https://pypistats.org/packages/pyriandx) 
[![PyPI](https://img.shields.io/pypi/v/pyriandx?style=flat)](https://pypi.org/project/pyriandx)
[![PyPI - License](https://img.shields.io/pypi/l/pyriandx?style=flat)](https://opensource.org/licenses/MIT)


`pyriandx` is CLI client and Python SDK/Library for PierianDx Clinical Genomics Workspace (CGW) web services -- https://umccr.github.io/pyriandx/

* [Test Coverage](https://umccr.github.io/pyriandx/coverage/)
* [PyDoc](https://umccr.github.io/pyriandx/pyriandx/)
* https://github.com/umccr/pyriandx


## TL;DR

- Install through `pip` like so
    ```
    pip install pyriandx
    ```

- Export `PDX_` environment variables
    ```
    export PDX_USERNAME=<YOUR_PierianDx_CGW_LOGIN_EMAIL>
    export PDX_PASSWORD=<YOUR_PierianDx_CGW_LOGIN_PASSWORD>
    export PDX_INSTITUTION=<ASK_YOUR_PierianDx_CGW_ACCOUNT_MANAGER>
    export PDX_BASE_URL=https://app.pieriandx.com/cgw-api/v2.0.0
    ```

- Print CLI command help
    ```
    pyriandx version
    pyriandx help
    pyriandx case help
    pyriandx list help
    pyriandx create help
    pyriandx upload help
    pyriandx run help
    pyriandx job help
    pyriandx poll help
    pyriandx report help
    ```

- More examples/tutorials available at [User Guide](https://umccr.github.io/pyriandx/user.html)


## License

MIT License and DISCLAIMER

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
partial-tagger 0.6.1
Sequence Tagger for Partially Annotated Dataset in PyTorch
License: MIT
Requires Python: >=3.8,<4.0

# Sequence Tagger for Partially Annotated Dataset in PyTorch

This is a CRF tagger for partially annotated dataset in PyTorch. You can easily utilize
marginal log likelihood for CRF (Tsuboi, et al., 2008). The implementation of this library is based on Rush, 2020.


## Usage

First, import some modules as follows.

```py
from partial_tagger.crf.nn import CRF
from partial_tagger.crf import functional as F
```

Initialize `CRF` by giving it the number of tags.

```py
num_tags = 2
crf = CRF(num_tags)
```

Prepare incomplete tag sequence (partial annotation) and convert it to a tag bitmap.  
This tag bitmap represents the target value for CRF.

```py
# 0-1 indicates a true tag
# -1 indicates that a tag is unknown
incomplete_tags = torch.tensor([[0, 1, 0, 1, -1, -1, -1, 1, 0, 1]])

tag_bitmap = F.to_tag_bitmap(incomplete_tags, num_tags=num_tags, partial_index=-1)

```

Compute marginal log likelihood from logits.

```py
batch_size = 1
sequence_length = 10
# Dummy logits
logits = torch.randn(batch_size, sequence_length, num_tags)

log_potentials = crf(logits)

loss = F.marginal_log_likelihood(log_potentials, tag_bitmap).sum().neg()
```

## Installation

To install this package:

```bash
pip install partial-tagger
```

## References

- Yuta Tsuboi, Hisashi Kashima, Shinsuke Mori, Hiroki Oda, and Yuji Matsumoto. 2008. [Training Conditional Random Fields Using Incomplete Annotations](https://aclanthology.org/C08-1113/). In _Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)_, pages 897–904, Manchester, UK. Coling 2008 Organizing Committee.
- Alexander Rush. 2020. [Torch-Struct: Deep Structured Prediction Library](https://aclanthology.org/2020.acl-demos.38/). In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations_, pages 335–342, Online. Association for Computational Linguistics.