Updating figures & descriptions for Ch 4 #23

Author: suranah

Ch4/README.md

@@ -1,8 +1,15 @@
 # Text Classification
 
-Set of notebooks associated with Chapter 4 of the book.
+## 🔖 Outline
 
-1. **[One Pipeline Many Classifiers](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/01_OnePipeline_ManyClassifiers.ipynb)**: Here we demonstrate text classification using various algorithms such as Naive Bayes, Logistic Regression and Support Vector Machines.
+To be added
+
+
+## 🗒️ Notebooks
+
+Set of notebooks associated with the chapter. 
+
+1. **[One Pipeline Many Classifiers](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/01_OnePipeline_ManyClassifiers.ipynb)**: Here we demonstrate text classification using various algorithms such as Naive Bayes, Logistic Regression, and Support Vector Machines.
 
 2. **[Doc2Vec for Text Classification](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/02_Doc2Vec_Example.ipynb)**: Here we demonstrate how to train your own Doc2Vec embedding and use it for text classification.
 
@@ -12,14 +19,31 @@ Set of notebooks associated with Chapter 4 of the book.
 
 5. **[NNs for Text Classification](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/05_DeepNN_Example.ipynb)**: Here we demonstrate text classification using pre-trained and custom word embeddings with various Deep Learning Models. 
 
-6. **[BERT: Text Classification](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/06_BERT_IMDB_Sentiment_Classification.ipynb)**: Here we demonstrate how we train and fine tune pytorch pre-trained BERT on IMDB reviews to predict their sentiment using HuggingFace Transformers library.
+6. **[BERT: Text Classification](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/06_BERT_IMDB_Sentiment_Classification.ipynb)**: Here we demonstrate how we train and fine-tune pytorch pre-trained BERT on IMDB reviews to predict their sentiment using HuggingFace Transformers library.
 
 7. **[BERT: Text CLassification using Ktrain](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/07_BERT_Sentiment_Classification_IMDB_ktrain.ipynb)**: Here we demonstrate how we can use BERT to predict the sentiment of movie reviews using the ktrain library.
 
 8. **[LIME-1](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/08_LimeDemo.ipynb)**: Here we demonstrate how to interpret the predictions of a logistic regression model using LIME.
 
-9. **[LIME-2](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/09_Lime_RNN.ipynb)**: Here we demonstrate how to interpret predictions of a RNN model using LIME.
+9. **[LIME-2](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/09_Lime_RNN.ipynb)**: Here we demonstrate how to interpret predictions of an RNN model using LIME.
 
 10. **[SHAP](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/10_ShapDemo.ipynb)**: Here we demonstrate how to interpret ML and DL text classification models using SHAP.
 
-11. **[Spam Classification](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/11_SpamClassification.ipynb)**: Here we demonstrate how to classify a text message as SPAM or HAM using pre-trained models from the fastai library. 
+11. **[Spam Classification](https://github.com/practical-nlp/practical-nlp/blob/master/Ch4/11_SpamClassification.ipynb)**: Here we demonstrate how to classify a text message as SPAM or HAM using pre-trained models from the fastai library. 
+
+
+## 🖼️ Figures
+
+Color figures as requested by the readers. 
+
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-1.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-2.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-3.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-4.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-5.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-6.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-7.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-8.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-9.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-10.png)
+![figure](https://github.com/practical-nlp/practical-nlp-figures/raw/master/figures/4-11.png)