semvec/pypi-packages huggingface dataset:
parallelsoup 2.0.0
A library that utilizes BeautifulSoup to provide multi threaded scrapping, reducing the total time involved in the scrapping process
Requires Python: >=3.6

# ParallelSoup
A library that utilizes BeautifulSoup to provide multi threaded scrapping, reducing the total time involved in the scrapping process

<br />

## Aim
The library should implement the following affectively: (this list can be extended in future):
* Parallelization (I mean duh, you didn't see the repo name or what...)
* Should consider the fact that user already has serial BeautifulSoup code
* Adding to above point: should be easier for user too just use our library
* All parts of the library should be documented
* All parts of the library should have unit tests written for verification of their functionality
* Showcase written examples for different sorts of scrapping

<br />

## Example
A small example of scraping imdb with ParallelSoup with goodness of 8 threads
```py
from parallelsoup import ParallelSoup

urls = []
for i in range(0, 10):
    urls.append("https://www.imdb.com/search/title/?countries=in&adult=include&start=" + str(50*i + 1) + "&ref_=adv_nxt")

def extractor(soup):
    data = []
    for item in soup.findAll('div', attrs={'class':'lister-item mode-advanced'}):
        title = item.find('div', attrs={'class':'lister-item-content'}).h3.a
        if title != None:
            data.append(title.text)
    return data

ps = ParallelSoup(8, urls, extractor)
ps.start()
dataParallel = ps.get()
print(dataParallel)
```

## Issues
This library is still in development, for reporting any bugs or suggestions please open a new issue [here](https://github.com/sarthakpranesh/ParallelSoup/issues)
turboactivate 4.4.4.1
Python integration for the LimeLM TurboActivate library. This lets you add hardware-locked licensing (a.k.a. node-locked licensing) to your Python app.

# Python integration for TurboActivate

Python integration for [LimeLM's](https://wyday.com/limelm/) [TurboActivate library](https://wyday.com/limelm/help/using-turboactivate/). This lets you add [hardware-locked licensing (a.k.a. node-locked licensing)](https://wyday.com/limelm/help/licensing-types/#hardware-locked) to your Python app.

## Usage

We have a full example app and tutorial here: [Using TurboActivate with Python](https://wyday.com/limelm/help/using-turboactivate-with-python/)

That article shows you how to add [node-locked licensing](https://wyday.com/limelm/help/licensing-types/#hardware-locked) to your app, add [verified trials](https://wyday.com/limelm/help/trials/#verified) to your app, and how to package your Python app for distribution.
pyclier-demo 0.1.0
Pyclier Demo.
Requires Python: >=3.6

# Pyclier Demo

Pyclier Demo.

> Note: This project was created with [Cookiecutter](https://github.com/cookiecutter/cookiecutter) and the [`zhangxianbing/cookiecutter-pypackage`](https://github.com/zhangxianbing/cookiecutter-pypackage) project template.

## Installation

```bash
 # install
 git clone https://github.com/zhangxianbing/pyclier-demo
 cd pyclier-demo && make install
```

## Usage
pytorch-mcrf 0.0.3
Multiple CRF implementation for PyTorch
Requires Python: >=3.6

# Masked CRF

**NOT Official** Pytorch implemented Masked CRF.

## Installation

### Dependencies
- Python >= 3.6
  - torch == 1.5.1 (better > 1.0)
  - tqdm == 4.53.0
  - pyyaml == 5.3.1

### Download and Install

1. Editable installation:

```bash
pip install -e .
```

2. Install from PyPI:

```bash
pip install pytorch-mcrf -i https://pypi.org/simple
```

## Quick Start

### Settings

All the settings are in `config.yaml`, you can change model settings from this file.

### Run

```bash
python run.py
```

## Results

Micro-F1 results

### WeiboNER

| Batch Size | Optimizer | Learning Rate | Max Seqence Length |
| ---------: | :-------: | ------------: | -----------------: |
|          8 |    SGD    |         0.015 |                128 |

| Method                      |    Dev |   Test | #Illegal Tags |
| :-------------------------- | -----: | -----: | ------------: |
| PlainCRF                    | 57.564 | 51.733 |             4 |
| MaskedCRF ( decoding only ) | 55.662 | 51.351 |             0 |
| MaskedCRF                   | 55.380 | 50.287 |             0 |


### MSRA

| Batch Size | Optimizer | Learning Rate | Max Seqence Length |
| ---------: | :-------: | ------------: | -----------------: |
|          8 |    SGD    |         0.015 |                250 |

Model is selected directly from test set since there is no official dev set.

| Method                      |   Test | #Illegal Tags |
| :-------------------------- | -----: | ------------: |
| PlainCRF                    | 86.274 |             1 |
| MaskedCRF ( decoding only ) | 86.285 |             0 |
| MaskedCRF                   | 84.714 |             0 |


## Acknowledgements

- Official: [MaskedCRF](https://github.com/DandyQi/MaskedCRF)
- [pytorch-crf](https://github.com/kmkurn/pytorch-crf)
- [allennlp](https://github.com/allenai/allennlp)
- [ChineseWeiboNER](https://github.com/OYE93/Chinese-NLP-Corpus/tree/master/NER/)

## UPDATES
- v0.0.2: fix setuptools packages finding issue