websitecategorization - npm Package Compare versions

Comparing version 1.0.10 to 1.0.11

package.json

 {
   "name": "websitecategorization",
-  "version": "1.0.10",
+  "version": "1.0.11",
   "homepage": "websitecategorizationapi.com",
@@ -5,0 +5,0 @@ "keywords": [

README.md

@@ -51,2 +51,22 @@ <a href="https://github.com/explainableaixai/websitecategorizationapi/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/explainableaixai/websitecategorizationapi"></a>
 
+# Machine Learning Models for Website Categorization
+
+Text classification is usually automated as it is often used on use cases where the number of texts needed to be classified number in millions. 
+
+For this reasons, we most often machine learning models for text classifications. 
+
+In early period of machine learning, the most common models used for text classification were ranging from simpler ones, like Naive Bayes to more complex ones like Random Forests, Support Vector Machines and Logistic Regression. 
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+Support Vector Machines are especially good in terms of accuracy and f1 scores achieved, it however has a downside in that the complexity of training a SVM model rapidly increases with number of texts in training dataset. 
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+In last decade, with the rise of neural networks, more text classification models utilize the deep neural networks for this purpose. Earlier deep neural networks for text classification were often based on LSTM neural net. In recent times there have been other neural network architectures successfuly used for text classification. 
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+Authors in this highly cited paper: [https://arxiv.org/pdf/1803.01271.pdf](https://arxiv.org/pdf/1803.01271.pdf) researched convolutional neural networks for text classification and came to conclusion that even a simple convolutional archi-
+tecture outperforms canonical recurrent networks as the previously mentioned LSTMs across different classification tasks. 
+
+The NN model in question can be accessed here: [https://github.com/locuslab/TCN](https://github.com/philipperemy/keras-tcn), with keras implementation available at [https://github.com/philipperemy/keras-tcn](https://github.com/philipperemy/keras-tcn). 
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 # UI Dashboard
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Number of lines in readme file

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