diff --git a/models/README.md b/models/README.md
index 1e48f7f..d83ff53 100644
--- a/models/README.md
+++ b/models/README.md
@@ -14,10 +14,10 @@ Although we trained models for a few domains of corpora, we recommend those usin
 ## Model formats
 The models are available in two formats:
 
- * `.csv` - A comma-seprated value format storing the linear transformation matrix of size `p X 768` where `p` is the low dimension.
+ * `.csv` - A comma-seprated value format storing the linear transformation matrix of size `K X 768` where `K` is the low dimension.
  * `.pickle` - A pickle of the [Scikit-learn Decomposition](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition) modules. 
 
-## **Model Usage**
+## Model Usage
 
 The model files contain the matrix to reduce the dimensions of these user representations and this README explains how to use these models.
 
@@ -28,36 +28,50 @@ There are two steps to applying the models:
 
 ### Input format
 
-All models assume you have an input matrix of `768 X N_observations`, where `N_observations` is the training set size. The goal is produce `p X N_obsevations` output where `p` is the lower dimensional represetnation from the model. 
+All models assume you have an input matrix of `N_observations X 768`, where `N_observations` is the training set size. The goal is produce `N_obsevations X K` output where `K` is the lower dimensional represetnation from the model. 
 
 *Aggregating to user-level.* In many situations, one has multiple documents/posts/messages per individual. 
 
-### **Using CSVs through python**
+### Using CSVs through python
 
-If you are using the CSVs, here is an example for how to use it:
+Here is an example for how to use the CSV:
+
+```py
+def transform(user_emb):
+	#input:
+	#   user_emb: numpy matrix of N_observations X 768  -- matrix of average RoBERTA layer 11 per user. 
+	#output:
+	#   transformed_user_emb: numpy matrix of N_observations X P -- low dimensional user representation. 
 
 	import numpy as np
+	scalar = np.loadtxt("scalar.csv", delimiter=",")
+	#shape: (2, 768); 1st row -> mean; 2nd row -> std
+	user_emb = (user_emb - scalar[0]) / scalar[1]
 	model = np.loadtxt("model.csv", delimiter=",")
-	#shape of model: (x, 768)
+	#shape of model: (K, 768)
 	transformed_user_emb = np.dot(user_emb, model.T)
-
-### **Using pickle files through python**
+	return transformed_user_emb
+```
+### Using pickle files through python
 
 These pickle files are composed of a [Scikit-learn Decomposition](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition). To apply the learnt reduction, you can unpickle the model and run transform() method on the user embeddings.
 Here is an example showing how:
 
-	def transform(user_emb):
-		#input:
-		#   user_emb: numpy matrix of N_observations X 768  -- matrix of average RoBERTA layer 11 per user. 
-		#output:
-		#   transformed_user_emb: numpy matrix of N_observations X P -- low dimensional user representation. 
-		import pickle 
-		with open("model.pickle", "rb") as f:
-			model = pickle.load(f)["clusterModels"]["noOutcome"]
-		transformed_user_emb = model.transform(user_emb)
-		return transformed_user_emb
-
-### **Using pickle files through DLATK**
+```py
+def transform(user_emb):
+	#input:
+	#   user_emb: numpy matrix of N_observations X 768  -- matrix of average RoBERTA layer 11 per user. 
+	#output:
+	#   transformed_user_emb: numpy matrix of N_observations X K -- low dimensional user representation. 
+	import pickle 
+	with open("model.pickle", "rb") as f:
+		scalar = pickle.load(f)['scalers']['noOutcome']
+		model = pickle.load(f)['clusterModels']['noOutcome']
+	user_emb = scalar.transform(user_emb)
+	transformed_user_emb = model.transform(user_emb)
+	return transformed_user_emb
+```
+### Using pickle files through DLATK
 
 The message data is composed of the user id (user_id), message id (message_id), the message field and the outcome field(s). The user embeddings are generated by averaging the transformer representation of all the messages from a user. 
 
@@ -66,10 +80,7 @@ If the user embeddings have been generated using [DLATK](https://github.com/DLAT
 	python dlatkInterface.py -d {database-name} -t {table-name} -g {group-name} -f {user-embeddings-table-name} \
 	--transform_to_feats {dimred-table-name} --load --pickle {path-to-pickle-file}
 
-
-
-
-## **Model Description**
+## Model Description
 
 The models made available are pre-trained to reduce 768 dimensions of roberta-base using 3 datasets from different domains: Facebook (D_20), CLPsych 2019 (D_19), and CLPsych 2018 (D_18).
 D_20 dataset contains facebook posts of 55k users, while the D_19 has reddit posts from 496 users on r/SuicideWatch and D_18 contains essays written by approx 10k children. To know more about these datasets, refer to Section 3 in our paper.