From 103beb518fc01535d1a5edb9a8d754816e53ec2c Mon Sep 17 00:00:00 2001 From: Navan Chauhan Date: Tue, 15 Sep 2020 15:53:28 +0530 Subject: Publish deploy 2020-09-15 15:53 --- posts/2019-12-22-Fake-News-Detector/index.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'posts/2019-12-22-Fake-News-Detector/index.html') diff --git a/posts/2019-12-22-Fake-News-Detector/index.html b/posts/2019-12-22-Fake-News-Detector/index.html index 4f1197c..3ffb14a 100644 --- a/posts/2019-12-22-Fake-News-Detector/index.html +++ b/posts/2019-12-22-Fake-News-Detector/index.html @@ -1,4 +1,4 @@ -Building a Fake News Detector with Turicreate | Navan Chauhan
Navan Chauhan
7 minute readCreated on December 22, 2019Last modified on June 1, 2020

Building a Fake News Detector with Turicreate

In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app

Note: These commands are written as if you are running a jupyter notebook.

Building the Machine Learning Model

Data Gathering

To build a classifier, you need a lot of data. George McIntire (GH: @joolsa) has created a wonderful dataset containing the headline, body and wheter it is fake or real. Whenever you are looking for a dataset, always try searching on Kaggle and GitHub before you start building your own

Dependencies

I used a Google Colab instance for training my model. If you also plan on using Google Colab then I reccomend choosing a GPU Instance (It is Free) This allows you to train the model on the GPU. Turicreat is built on top of Apache's MXNet Framework, for us to use GPU we need to install a CUDA compatible MXNet package.

!pip install turicreate
+Building a Fake News Detector with Turicreate | Navan Chauhan
Navan Chauhan
7 minute readCreated on December 22, 2019Last modified on September 15, 2020
Building a Fake News Detector with Turicreate
In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app
Note: These commands are written as if you are running a jupyter notebook.
Building the Machine Learning Model
Data Gathering
To build a classifier, you need a lot of data. George McIntire (GH: @joolsa) has created a wonderful dataset containing the headline, body and whether it is fake or real. Whenever you are looking for a dataset, always try searching on Kaggle and GitHub before you start building your own
Dependencies
I used a Google Colab instance for training my model. If you also plan on using Google Colab then I recommend choosing a GPU Instance (It is Free) This allows you to train the model on the GPU. Turicreate is built on top of Apache's MXNet Framework, for us to use GPU we need to install a CUDA compatible MXNet package.
!pip install turicreate
 !pip uninstall -y mxnet
 !pip install mxnet-cu100==1.4.0.post0
 
If you do not wish to train on GPU or are running it on your computer, you can ignore the last two lines
Downloading the Dataset
!wget -q "https://github.com/joolsa/fake_real_news_dataset/raw/master/fake_or_real_news.csv.zip"
@@ -39,7 +39,7 @@
 
Exporting the Model
model_name = 'FakeNews'
 coreml_model_name = model_name + '.mlmodel'
 exportedModel = model.export_coreml(coreml_model_name)
-
Note: To download files from Google Volab, simply click on the files section in the sidebar, right click on filename and then click on downlaod
Link to Colab Notebook
Building the App using SwiftUI
Initial Setup
First we create a single view app (make sure you check the use SwiftUI button)
Then we copy our .mlmodel file to our project (Just drag and drop the file in the XCode Files Sidebar)
Our ML Model does not take a string directly as an input, rather it takes bag of words as an input. DescriptionThe bag-of-words model is a simplifying representation used in NLP, in this text is represented as a bag of words, without any regatd of grammar or order, but noting multiplicity
We define our bag of words function
func bow(text: String) -> [String: Double] {
+
Note: To download files from Google Colab, simply click on the files section in the sidebar, right click on filename and then click on download
Link to Colab Notebook
Building the App using SwiftUI
Initial Setup
First we create a single view app (make sure you check the use SwiftUI button)
Then we copy our .mlmodel file to our project (Just drag and drop the file in the XCode Files Sidebar)
Our ML Model does not take a string directly as an input, rather it takes bag of words as an input. DescriptionThe bag-of-words model is a simplifying representation used in NLP, in this text is represented as a bag of words, without any regard for grammar or order, but noting multiplicity
We define our bag of words function
func bow(text: String) -> [String: Double] {
         var bagOfWords = [String: Double]()
         
         let tagger = NSLinguisticTagger(tagSchemes: [.tokenType], options: 0)
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