semvec/pypi-packages huggingface dataset:
pytest-joke 0.1.1
Test failures are better served with humor.
License: MIT
Requires Python: >=3.4
Keywords: pytest,joke,humor,humour

# pytest-joke

[![Build Status](https://travis-ci.org/alecxe/pytest-joke.svg?branch=master)](https://travis-ci.org/alecxe/pytest-joke)
[![Requirements Status](https://requires.io/github/alecxe/pytest-joke/requirements.svg?branch=master)](https://requires.io/github/alecxe/pytest-joke/requirements/?branch=master)
[![Pyversions](https://img.shields.io/pypi/pyversions/pytest-joke.svg)](https://pypi.python.org/pypi/pytest-joke)
[![PyPI](https://img.shields.io/pypi/v/pytest-joke.svg)](https://pypi.python.org/pypi/pytest-joke)

Running tests is better with some healthy (or not) amount or humor! ��

This is a completely useless plugin created to get some practice in creating plugins for Pytest.

Powered by [pyjokes][pyjokes]. 

[pyjokes]: https://pyjok.es/


## Installation ��

You can install **pytest-joke** via [pip][pip] from [PyPI][PyPI]:

```bash
$ pip install pytest-joke
```

[pip]: https://pypi.python.org/pypi/pip/
[PyPI]: https://pypi.org/project/pytest-joke/

## Usage

When the plugin is installed, every time a test run results in a failure, the terminal would output a random joke in an attempt to cheer you up.

```bash
Humor-powered output enabled ��.

plugins: mock-1.10.4, joke-0.1.0
collected 2 items

test_joke.py .F                                                          [100%]

=================================== FAILURES ===================================
_________________________________ test_failed __________________________________

    def test_failed():
>       assert 1 == 2
E       assert 1 == 2

test_joke.py:7: AssertionError
====================== 1 failed, 1 passed in 0.03 seconds ======================

There are two ways to write error-free programs; only the third one works.
```

## TODO

 * make the plugin configurable: turn on/off, language, joke category, number of jokes

## License

Distributed under the terms of the [MIT][mit] license, **pytest-joke** is
free and open source software.

[mit]: http://opensource.org/licenses/MIT

## Motivation

[![](https://pybit.es/images/pytest-book-poster.png)](https://pybit.es/pytest-book.html)
sword 0.0.3
A fast, sharp, Python C extension for data mining.
License: MIT
Keywords: data mining,data science,search,csv,data table,data analytics

# Sword

*A Fast Data Mining C Library for Python*

## Intro

`sword` is a Python C Extension for making data mining and data searching super fast. It's designed to 
give you amazing performance in the comfort and simplicity of Python. `sword` offers a wide array of tools
to parse and search tabular data, csv, xml, and much more.

### Status

`sword` is still in the active development phase. It currently supports some CSV functionality. More detailed
and rich features will be released in coming patch releases.


## Functions

`sword` currently provides the following functions:



#### `csv_read_row`:

This function reads a single CSV row from a string, and returns a tuple containing:

* The list containing the row elements.
* The remaining unread string.

*Example:*

```py
>>> import sword
>>> sword.csv_read_row("54.7,32.6,54.7\n")
([b'54.7', b'32.6', b'54.7'], b'\n')
```

#### `csv_count_rows`:

Counts the number of rows in a CSV-string

*Exmaple:*

```py
>>> import sword
>>> sword.csv_count_rows("1,2\n3,4\n5,5,5,5\n")
3
```


#### `csv_count_cols`:

Counts the number of columns in a CSv-string

*Example:*

```py
>>> import sword
>>> sword.csv_count_cols("1,2,3,4\n")
4
```
semantic-text-similarity 1.0.3
implementations of models and metrics for semantic text similarity. that's it.
License: MIT

# semantic-text-similarity
an easy-to-use interface to fine-tuned BERT models for computing semantic similarity. that's it.

This project contains an interface to fine-tuned, BERT-based semantic text similarity models. It modifies [pytorch-transformers](https://github.com/huggingface/pytorch-transformers) by abstracting away all the research benchmarking code for ease of real-world applicability.

| Model             |          Dataset | Dev. Correlation |
|-------------------|------------------|------------------|
| Web STS BERT      | STS-B            |     0.893        |
| Clinical STS BERT | MED-STS          |     0.854        |

# Installation

Install with pip:

```
pip install semantic-text-similarity
```

or directly:

```
pip install git+https://github.com/AndriyMulyar/semantic-text-similarity
```

# Use
Maps batches of sentence pairs to real-valued scores in the range [0,5]
```python
from semantic_text_similarity.models import WebBertSimilarity
from semantic_text_similarity.models import ClinicalBertSimilarity

web_model = WebBertSimilarity(device='cpu', batch_size=10) #defaults to GPU prediction

clinical_model = ClinicalBertSimilarity(device='cuda', batch_size=10) #defaults to GPU prediction

web_model.predict([("She won an olympic gold medal","The women is an olympic champion")])
```
More [examples](/examples).



# Notes
- You will need a GPU to apply these models if you would like any hint of speed in your predictions.
- Model downloads are cached in `~/.cache/torch/semantic_text_similarity/`. Try clearing this folder if you have issues.


# Acknowledgement
Clinical models in this project were submitted to the 2019 N2C2 Shared Task Track 1.
Implementation and model training in this project was supported by funding from the Mark Dredze Lab at Johns Hopkins University.