semvec/pypi-packages huggingface dataset:
mkdocs-encriptmail-plugin 0.9.8
A MkDocs plugin that converts markdown encoded email-links into spambot save <a href> elements.
License: MIT
Requires Python: >=3.5
Keywords: mkdocs python markdown

# MkDocs encriptMail Plugin

This [MkDocs](https://www.mkdocs.org) plugin converts markdown encoded email addresses like

```
[Email-Link](mailto:test@test.test)
```

into 

```html
<a href="javascript:linkTo_UnCryptMailto(%27ocknvq,vguvBvguv0vguv%27)">
    Email-Link
</a>
```

or

```
[test@test.test](mailto:test@test.test)
```

into 

```html
<a href="javascript:linkTo_UnCryptMailto(%27ocknvq,vguvBvguv0vguv%27)">
    test(Q)test(P)test
</a>
```

This allows to hide email address from spam bots and allows user to link email addresses.

## Requirements

This package requires Python >=3.5 and MkDocs version 1.0 or higher.  

## Installation

Install the package with pip:

```cmd
pip install mkdocs-encriptmail-plugin
```

Enable the plugin in your `mkdocs.yml`:

```yaml
plugins:
    - search
    - encriptmail:
       placeholderAt: (Q)
       placeholderDot: (P)
```

**Note:** If you have no `plugins` entry in your config file yet, you'll likely also want to add the `search` plugin. MkDocs enables it by default if there is no `plugins` entry set, but now you have to enable it explicitly.

More information about plugins in the [MkDocs documentation](https://www.mkdocs.org/user-guide/plugins/)
MiSeCom 0.1.1
Detect if the English has missing sentence components such as Subject, Verb, Object
License: MIT
Requires Python: >=3.8

# Missing Sentence Components

Multilabel classification with RoBERTa from huggingface library

## How to use

```bash
pip install python
```

```python
from MiSeCom import MissSentComp
miss_sentence_component = MissSentComp('transZ/misecom', 'roberta-base')
sent = "I education company."
print(miss_sentence_component(sent)) # I education company <ma> <mp> <mv>
```