fakenewschallenge | UCL Machine Reading - FNC-1 Submission | Machine Learning library

by uclnlp Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | fakenewschallenge Summary

fakenewschallenge is a Python library typically used in Artificial Intelligence, Machine Learning applications. fakenewschallenge has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However fakenewschallenge build file is not available. You can download it from GitHub.

UCL Machine Reading - FNC-1 Submission

Support

Quality

Security

License

Reuse

Support

fakenewschallenge has a low active ecosystem.

It has 162 star(s) with 54 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 4 have been closed. On average issues are closed in 0 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of fakenewschallenge is current.

Quality

fakenewschallenge has 0 bugs and 3 code smells.

Security

fakenewschallenge has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

fakenewschallenge code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

fakenewschallenge is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

fakenewschallenge releases are not available. You will need to build from source code and install.

fakenewschallenge has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

fakenewschallenge saves you 90 person hours of effort in developing the same functionality from scratch.

It has 231 lines of code, 6 functions and 2 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed fakenewschallenge and discovered the below as its top functions. This is intended to give you an instant insight into fakenewschallenge implemented functionality, and help decide if they suit your requirements.

Train the model .
Create a pipeline .
Reads a table from a file .
Save predictions to file .
Initialize instance attributes .
Loads the checkpoint .

Get all kandi verified functions for this library.

fakenewschallenge Key Features

No Key Features are available at this moment for fakenewschallenge.

fakenewschallenge Examples and Code Snippets

No Code Snippets are available at this moment for fakenewschallenge.

Community Discussions

Trending Discussions on fakenewschallenge

How to concatenate two TF-IDF Vectors as well as other features that can be fed into a model?

How to correctly split unbalanced dataset incorporating train test and cross validation set

QUESTION

How to concatenate two TF-IDF Vectors as well as other features that can be fed into a model?

Asked 2020-Aug-17 at 21:30

The solution I found that works in my case is posted below. Hope this helps someone. How would I concatenate the output of TF-IDF created with sklearn to be passed into a Keras model or tensor that could then be fed into a dense neural network? I'm working on the FakeNewsChallenge dataset. Any guidance would be helpful.

The FakeNewsChallenge dataset is as such:

Training Set - [Headline, Body text, label]

Training Set is split into two different CSVs (train_bodies, train_stances) and are linked by BodyIDs.
train_bodies - [Body ID (num), articleBody (text)]
train_stances - [Headline (text), Body ID (num), Stance (text)]

Test Set - [Headline, Bodytext]

Test set is split into two different CSVs (test_stances_inlabled, test_bodies]
Test_bodies - [Body ID, aritcleBody]
Test_stances_unlabled - [Headline, Body ID]

Distribution makes it extremely hard:

rows - 49972
unrelated - 0.73131
discuss - 0.17828
agree - 0.076012
disagree - 0.0168094

Stance - [ unrelated, discuss, agree, disagree]

What I would like to do is concatenate two separate TF-IDF Vectors as well as other features that I can then feed into a some layer for instance a dense layer. How would you go about that? I

...

ANSWER

Answered 2020-Aug-17 at 21:30

There was a comment prior to mine that answered the question but I do not see the comment anymore. I apparently forgot about this method, but was using it in other areas of my program.

You use the numpy.hstack(tup) or numpy.vstack(tup), where

tup - sequence of ndarrays

The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.

It returns a stacked: ndarray.

Here is some code just incase.

Note: I do not have cosine similarity calculation here. Do that however you want. I'm trying to do this fast but also as clear as possible. Hope this helps someone.

Source https://stackoverflow.com/questions/63417829

QUESTION

How to correctly split unbalanced dataset incorporating train test and cross validation set

Asked 2020-Aug-16 at 21:26

The picture above is what I'm trying to replicate. I just don't know if I'm going about it the right way. I'm working with the FakeNewsChallenge dataset and its extremely unbalanced, and I'm trying to replicate and improve on a method used in a paper.

Agree - 7.36%

Disagree - 1.68%

Discuss - 17.82%

Unrelated - 73.13%

I'm splitting the data in this way:

(split dataset into 67/33 split)

train 67%, test 33%

(split training further 80/20 for validation)

training 80%, validation 20%

(Then split training and validation using 3 fold cross validation set)

As an aside, getting that 1.68% of disagree and agree has been extremely difficult.

This is where I'm having an issue as it's not making total sense to me. Is the validation set created in the 80/20 split being stratified as well in the 5fold?

Here is where I am at currently:

Split data into 67% Training Set and 33% Test Set

...

ANSWER

Answered 2020-Aug-16 at 06:14

You need to add one more parameter in the function 'train_test_split()':

Source https://stackoverflow.com/questions/63433707

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install fakenewschallenge

To get started, simply download the files in this repository to a local directory.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: