semvec/pypi-packages huggingface dataset:
ttl-cache 1.6
Decorator to wrap a function with a memoizing callable that has TTL result
Keywords: cache,ttl,decorator,functools

# ttl-cache

### How to use it

```sh
pip install ttl-cache
```

```python
import ttl_cache


# use ttl_cache directly
@ttl_cache
def expensive_operation(a, b):
    ...
    ...
    return SOME_RESULT

expensive_operation(xx, yy)
expensive_operation(xx, yy)  # prefer cached result
# ... 60 seconds later
expensive_operation(xx, yy)  # compute again


# or
@ttl_cache(2.0)  # cache the result in the next 2 seconds, default is 60.0 seconds
def expensive_operation(a, b):
    ...
    ...

```
chitubox 0.1.5
Utility to manage networked ChiTuBox based LCD Resin printers
Requires Python: >=3.5

# ChiTuBox Network Manager

Tool to interact with the ChiTuBoard based networked LCD Resin printer

```
usage: chitucli [-h] --ip IP
                [--list | --query | --axis-z | --version | --print | --upload | --download | --delete]
                [files [files ...]]

positional arguments:
  files

optional arguments:
  -h, --help      show this help message and exit
  --ip IP, -i IP
  --list, -L
  --query, -Q
  --axis-z, -Z
  --version, -V
  --print, -P
  --upload, -U
  --download, -D
  --delete, -X
```
rukip 0.0.6
An Embedded CKIP Rasa NLU Components
Requires Python: >=3.6

# rukip
An Embedded CKIP Rasa NLU Components

## Introduction
This open-source library implements [Rasa](https://github.com/RasaHQ/rasa) custom components. 

It offers```tokenizer``` and ```featurizer``` powered by [ckiptagger](https://github.com/ckiplab/ckiptagger) as components in RasaNLU pipeline.

## Installation
```bash
pip install rukip
```
rukip is a python library hosted on PyPI. Requirements:

* python >= 3.6
* rasa >= 1.4.0
* ckiptagger[tf] >= 0.0.19

## Usage

### Download model files
The model files are available on several mirror sites.

- [iis-ckip](http://ckip.iis.sinica.edu.tw/data/ckiptagger/data.zip)
- [gdrive-ckip](https://drive.google.com/drive/folders/105IKCb88evUyLKlLondvDBoh7Dy_I1tm)
- [gdrive-jacobvsdanniel](https://drive.google.com/drive/folders/15BDjL2IaX3eYdFVzT422VwCb743Hrbi3)

You can download and extract to the desired path by the following steps:

1. Downloads to ```./data.zip``` 
2. Extracts to ```./data/```

Add ```CKIPTokenizer``` component into rasa nlu pipeline and configure model_path as ```./data```. 

The following is the example ***Rasa NLU config file*** 

```yaml
 language: "zh"
 pipeline:
   - name: "rukip.tokenizer.CKIPTokenizer"
     model_path: "./data"
   - name: "rukip.featurizer.CKIPFeaturizer"
     model_path: "./data"
     token_features: ["word", "pos"]
   - name: "CRFEntityExtractor"
     features: [["ner_features"], ["ner_features"], ["ner_features"]]
   - name: "CountVectorsFeaturizer"
   - name: "EmbeddingIntentClassifier"

```

### Components
#### CKIPTokenizer
This component has **one** required field (model_path) to be configured and offers **two** optional fields for user to assign dictionaries.

* ```recommend_dict_path ``` is the file containing list of user-defined recommended-word. Default is ```None```
* ```cooerce_dict_path``` is the file containing a list of must-word. Default is ```None```

The following is the example of user-defined dictionary. Each line shows one pair of word and weight.

```
土地公 1
土地婆 1
公有 2
來亂的 1
緯來體育台 1

``` 

#### CKIPFeaturizer
This component has **one** required field (model_path) to be configured and offers another optional fields for user to assign.

* ```token_features ``` is list of features extracted from ckiptagger to generate ```ner_features```. Default is ```["word", "pos"]```

## Development
```bash
$> git clone git@github.com:circlelychen/rukip.git
$> pip install -r requirements-to-freeze.dev.txt
$> make test
```

## License
licensed under the GNU General Public License v3.0