This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
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Project Description

Dragnet

Dragnet isn’t interested in the shiny chrome or boilerplate dressing of a web page. It’s interested in… ‘just the facts.’ The machine learning models in Dragnet extract the main article content and optionally user generated comments from a web page. They provide state of the art performance on variety of test benchmarks.

For more information on our approach check out:

Installing

The build requires numpy, lxml and a new version of Cython, so first make sure they are installed, then install Dragnet:

pip install numpy
pip install --upgrade cython
pip install lxml
pip install dragnet

GETTING STARTED

Depending on your use case, we provide two separate models to extract just the main article content or the content and any user generated comments. Each model implements the analyze method that takes an HTML string and returns the content string.

import requests
from dragnet import content_extractor, content_comments_extractor

# fetch HTML
url = 'https://moz.com/devblog/dragnet-content-extraction-from-diverse-feature-sets/'
r = requests.get(url)

# get main article without comments
content = content_extractor.analyze(r.content)

# get article and comments
content_comments = content_comments_extractor.analyze(r.content)
Release History

Release History

1.0.1

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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1.0.0

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
dragnet-1.0.1-cp27-none-macosx_10_10_intel.whl (1.1 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Jan 29, 2015
dragnet-1.0.1.tar.gz (923.8 kB) Copy SHA256 Checksum SHA256 Source Jan 29, 2015

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