semvec/pypi-packages huggingface dataset:
tinydb-smartcache 2.0.0
A smarter query cache for TinyDB
License: MIT
Requires Python: >=3.5,<4.0
Keywords: database,nosql

tinydb-smartcache
^^^^^^^^^^^^^^^^^

|Build Status| |Coverage| |Version|

``tinydb-smartcache`` provides a smart query cache for TinyDB. It updates the
query cache when inserting/removing/updating elements so the cache doesn't get
invalidated. It's useful if you perform lots of queries while the data changes
only a little.

Installation
************

.. code-block:: bash

    $ pip install tinydb_smartcache

Usage
*****

.. code-block:: python

    >>> from tinydb import TinyDB
    >>> from tinydb_smartcache import SmartCacheTable
    >>> db = TinyDB('db.json')
    >>> db.table_class = SmartCacheTable
    >>> db.table('foo')
    >>> # foo will now use the smart query cache

If you want to enable TinyDB for all databases in a session, run:

.. code-block:: python

    >>> from tinydb import TinyDB
    >>> from tinydb_smartcache import SmartCacheTable
    >>> TinyDB.table_class = SmartCacheTable
    >>> # All databases/tables will now use the smart query cache

Changelog
*********

**v2.0.0** (2020-08-25)
-----------------------

- Add support for TinyDB v4. Drops support for TinyDB <= 3 and Python 2.

**v1.0.3** (2019-10-26)
-----------------------

- Make ``SmartCacheTable`` work again after breakage with TinyDB v3.12.0

**v1.0.2** (2015-11-17)
-----------------------

- Account for changes in TinyDB 3.0

**v1.0.1** (2015-11-17)
-----------------------

- Fix installation via pip

**v1.0.0** (2015-09-17)
-----------------------

- Initial release on PyPI

.. |Build Status| image:: https://img.shields.io/github/workflow/status/msiemens/tinydb-smartcache/Python%20CI?style=flat-square
   :target: https://github.com/msiemens/tinydb-smartcache/actions?query=workflow%3A%22Python+CI%22
.. |Coverage| image:: http://img.shields.io/coveralls/msiemens/tinydb-smartcache.svg?style=flat-square
   :target: https://coveralls.io/r/msiemens/tinydb-smartcache
.. |Version| image:: http://img.shields.io/pypi/v/tinydb-smartcache.svg?style=flat-square
   :target: https://pypi.python.org/pypi/tinydb-smartcache/
Kickbase-Bot 0.0.15
Python bot framework for kickbase
License: MIT

# Kickbase_Bot
[Kickbase](https://www.kickbase.com/) bot framework for Python 3.  This work is unofficial and not related to kickbase in any way. All of this was done for scientific reasons only and you should not use it for anything else but for your personal learning!

## Installation
- From pypi:
`pip3 install kickbase_bot`

## Requirements
- [kickbase_api](https://github.com/kevinskyba/kickbase-api-python/)
- [pymongo](https://github.com/mongodb/mongo-python-driver)

## Usage
```python
# Press the green button in the gutter to run the script.
import logging

from kickbase_api.models.chat_item import ChatItem
from kickbase_api.models.feed_item import FeedItem, FeedType

from kickbase_bot import logger as kickbase_logger
from kickbase_bot.kickbase_bot import KickbaseBot

sh = logging.StreamHandler()
sh.setLevel(logging.DEBUG)
kickbase_logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
sh.setFormatter(formatter)
kickbase_logger.addHandler(sh)


def on_feed_item(feed_item: FeedItem, bot: KickbaseBot):
    pass


def on_chat_item(chat_item: ChatItem, bot: KickbaseBot):
    if chat_item.user_id != bot.kickbase_api.user.id:
        print("Got chat: " + chat_item.message + " from: " + chat_item.username)
        bot.kickbase_api.post_chat_message("Hey {}".format(chat_item.username), bot.selected_league)


if __name__ == '__main__':
    kkbs_bot = KickbaseBot(periodic_feed_interval=5,
                           google_identity_toolkit_api_key="example",
                           mongodb_host="localhost",
                           mongodb_user="kkbs_bot", mongodb_password="kkbs_bot")
    kkbs_bot.connect("example", "example_password")

    kkbs_bot.add_feed_item_callback(on_feed_item)
    kkbs_bot.add_chat_item_callback(on_chat_item)

    kkbs_bot.run("example_league_id")
```

## Docs


## API


## License

[MIT License](LICENSE.md)
codeswitch 1.1
Code Switch is a NLP tool can use for language identification, pos tagging, name entity recognition, sentiment analysis of code mixed data.
License: MIT
Requires Python: >=3.5

# Code Switch
[![Documentation Status](https://readthedocs.org/projects/codeswitch/badge/?version=latest)](https://codeswitch.readthedocs.io/en/latest/?badge=latest)
[![PyPI Version](https://img.shields.io/pypi/v/codeswitch)](https://pypi.org/project/codeswitch/)

**CodeSwitch** is a NLP tool, can use for language identification, pos tagging, name entity recognition, sentiment analysis of code mixed data.

## Supported Code-Mixed Language
We used [LinCE](https://ritual.uh.edu/lince/home) dataset for training **multilingual BERT** model using huggingface [transformers](https://github.com/huggingface/transformers). `LinCE` has four language mixed data. We took three of it `spanish-english`, `hindi-english` and `nepali-english`. Hope we will train and add other language and task too.

* Spanish-English(spa-eng)
* Hindi-English(hin-eng)
* Nepali-English(nep-eng)

### Language Code
* `spa-eng` for spanish-english
* `hin-eng` for hindi-english
* `nep-eng` for nepali-english

## Installation
```
pip install codeswitch
```
## Dependency
* pytorch >=1.6.0

## Training Details
* All three(lid, ner, pos) sequence tagging model was trainend with huggingface [token classification](https://github.com/huggingface/transformers/tree/master/examples/token-classification)
* Sentiment Analysis Model trained with huggingface [text classification](https://github.com/huggingface/transformers/tree/master/examples/text-classification)
* You can find every model and evaluation results [here](https://huggingface.co/sagorsarker)

## Features & Supported Language
* Language Identification
  - spanish-english
  - hindi-english
  - nepali-english
* POS
  - spanish-english
  - hindi-english
* NER
  - spanish-english
  - hindi-english
* Sentiment Analysis
  - spanish-english

## Language Identification

```py
from codeswitch.codeswitch import LanguageIdentification
lid = LanguageIdentification('spa-eng') 
# for hindi-english use 'hin-eng', 
# for nepali-english use 'nep-eng'
text = "" # your code-mixed sentence 
result = lid.identify(text)
print(result)
```

## POS Tagging
```py
from codeswitch.codeswitch import POS
pos = POS('spa-eng')
# for hindi-english use 'hin-eng'
text = "" # your mixed sentence 
result = pos.tag(text)
print(result)

```


## NER Tagging
```py
from codeswitch.codeswitch import NER
ner = NER('spa-eng')
# for hindi-english use 'hin-eng'
text = "" # your mixed sentence 
result = ner.tag(text)
print(result)

```

## Sentiment Analysis

```py
from codeswitch.codeswitch import SentimentAnalysis
sa = SentimentAnalysis('spa-eng')
sentence = "El perro le ladraba a La Gatita .. .. lol #teamlagatita en las playas de Key Biscayne este Memorial day"
result = sa.analyze(sentence)
print(result)
# [{'label': 'LABEL_1', 'score': 0.9587041735649109}]


```

## Acknowledgement
* [LinCE](https://ritual.uh.edu/lince/home)
* [BERT](https://arxiv.org/abs/1810.04805)
* [huggingface](https://github.com/huggingface)