semvec/pypi-packages huggingface dataset:
RobotFramework-PropertyFile 0.0.1
An utility package for exposing Java-style properties from property-files as variables in Robot Framework
License: MIT

# robotframework-propertyfile
An utility package for exposing Java-style properties from property-files as variables in Robot Framework

# Usage

You need to download the file `propertyfile.py` from here:

https://gist.githubusercontent.com/AdamHepner/bc3e754fb2e808bce190e6e24dff8225/raw/5ef13029dbf04ae1bd68f9bd84afbd61a9406b0c/PropertyFile.py

and make it available in your Robot Framework library search path

Then you can point your .robot file towards any properties file using:

```robotframework
| Settings | | | |
| Variables | propertyfile | path_to_your_propertyfile.prop | variable_prefix |
```

This will make sure, that all properties from the file `path_to_your_propertyfile.prop` are available in your .robot file, prefixed with `variable_prefix` followed by underscore. So, the following entry in property file:

```
http.proxyHost = 192.168.0.1
```

becomes `${variable_prefix_http_proxyHost}`.

**All available environment variables are expanded!**
dephell-markers 1.0.3
Work with environment markers (PEP-496)
License: MIT
Requires Python: >=3.5
Keywords: dephell,markers,packaging,versioning


Dephell Markers
===============


.. image:: https://travis-ci.org/dephell/dephell_markers.svg?branch=master
   :target: https://travis-ci.org/dephell/dephell_markers
   :alt: travis


.. image:: https://ci.appveyor.com/api/projects/status/github/dephell/dephell_markers?svg=true
   :target: https://ci.appveyor.com/project/orsinium/dephell-markers
   :alt: appveyor


.. image:: https://img.shields.io/pypi/l/dephell-markers.svg
   :target: https://github.com/dephell/dephell_markers/blob/master/LICENSE
   :alt: MIT License


Work with environment markers (PEP-496).

Installation
------------

Install from `PyPI <https://pypi.org/project/dephell-markers/>`_\ :

.. code-block:: bash

   python3 -m pip install --user dephell_markers

Usage
-----

.. code-block:: python

   from dephell_markers import Markers

   m = Markers('os_name == "posix" and python_version >= "2.7"')

   m.get_version(name='python_version')
   # '>=2.7'

   m.get_string(name='os_name')
   # 'posix'

   Markers('python_version >= "2.4" or python_version <= "2.7"').get_version(name='python_version')
   '<=2.7 || >=2.4'

   Markers('python_version >= "2.4" or python_version <= "2.7"').python_version
   # RangeSpecifier(<=2.7 || >=2.4)


   # Nothing better than lie:
   Markers('python_version == "2.4" or os_name == "linux"').get_version(name='python_version')
   # None
mi-ner 0.6.1
A python library for named entity recognition evaluation
License: MIT
Keywords: named entity recognition,nlp,natural language processing

# miNER

A python library for NER (Named Entity Recognition) evaluation

We can evaluate the performance of NER by distinguishing between known entities and unknown entities using this library.


## Support

- Tagging Scheme
    - IOB2
    - BIOES
    - BIOUL
- metrics
    - precision
    - recall
    - f1


## Requirements

- python3
- cython


## Installation

```shell
pip install cython  # must execute before `pip install mi-ner`
pip install mi-ner
```


## Usage

#### Sample

```python
>>> from miner import Miner
>>> answers = [
    'B-PSN O O B-LOC O O O O'.split(' '),
    'B-PSN I-PSN O O B-LOC I-LOC O O O O'.split(' '),
    'S-PSN O O S-PSN O O B-LOC I-LOC E-LOC O O O O'.split(' ')
]
>>> predicts = [
    'B-PSN O O B-LOC O O O O'.split(' '),
    'B-PSN B-PSN O O B-LOC I-LOC O O O O'.split(' '),
    'S-PSN O O O O O B-LOC I-LOC E-LOC O O O O'.split(' ')
]
>>> sentences = [
    '花子 さん は 東京 に 行き まし た'.split(' '),
    '山田 太郎 君 は 東京 駅 に 向かい まし た'.split(' '),
    '花子 さん と ボブ くん は 東京 スカイ ツリー に 行き まし た'.split(' '),
]
>>> knowns = {'PSN': ['花子'], 'LOC': ['東京']}  # known words (words included in training data)
>>> m = Miner(answers, predicts, sentences, knowns)
>>> m.default_report(True)

	precision    recall    f1_score   num
LOC	 1.000        1.000     1.000      3
PSN	 0.500        0.500     0.500      4
overall	 0.714        0.714     0.714      7
{'LOC': {'precision': 1.0, 'recall': 1.0, 'f1_score': 1.0, 'num': 3},
'PSN': {'precision': 0.5, 'recall': 0.5, 'f1_score': 0.5, 'num': 4},
'overall': {'precision': 0.7142857142857143, 'recall': 0.7142857142857143, 'f1_score': 0.7142857142857143, 'num': 7}}
>>> m.unknown_only_report(True)

	precision    recall    f1_score   num
LOC	 1.000        1.000     1.000      2
PSN	 0.000        0.000     0.000      2
overall	 0.500        0.500     0.500      4
{'LOC': {'precision': 1.0, 'recall': 1.0, 'f1_score': 1.0, 'num': 2},
'PSN': {'precision': 0.0, 'recall': 0.0, 'f1_score': 0, 'num': 2},
'overall': {'precision': 0.5, 'recall': 0.5, 'f1_score': 0.5, 'num': 4}}
>>> m.return_predict_named_entities()
{'known': {'LOC': ['東京'], 'PSN': ['花子'], 'overall': []},
'unknown': {'LOC': ['東京スカイツリー', '東京駅'], 'PSN': ['山田', '太郎'], 'overall': []}}
```

#### Methods

|  method  |  description  |
| ---- | ---- |
|  default\_report(print\_)  |  return result of named entity recognition. if print\_=True, showing result   |
|  known\_only\_report(print\_)  |  return result of known named entity recognition.  |
|  unknown\_only\_report(print\_)  |  return result of unknown named entity recognition.  |
|  return\_predict\_named\_entities()  |  return named entities along predicted label(predicts). |
|  return\_answer\_named\_entities()  |  return named entities along answer label(answer). |
|  return\_miss\_labelings() | return miss labeling sentences. |
|  segmentation\_score(mode) | show parcentages of matching answer and predict labels.  if `known` or` unknown` for `mode`, return labeling accuracy for known or unknown NE. |


## License

MIT