added README for model usage

models/README.md

@@ -0,0 +1,22 @@
+## **Model usage**
+
+---
+
+### **Using pickle files through python**
+
+These pickle files are composed of a sckit learn decomposition class. Hence, to apply the learnt reduction, you can unpickle the model and run transform() method on the user embeddings.
+Here is an example showing how:
+
+	import pickle 
+	with open("model.pickle", "rb") as f:
+		model = pickle.load(f)["clusterModels"]["noOutcome"]
+	#user embeddings are stored in a variable calles user_emb
+	user_emb = model.transform(user_emb)
+
+### **Using pickle files through DLATK**
+
+If the user embeddings have been generated using [DLATK](github.com/DLATK/DLATK/) by following the commands [here](https://github.com/adithya8/ContextualEmbeddingDR#commands-to-extract-dimension-reduced-tables-using-a-specific-method), then you can use these pickle files directly by using the following command:
+
+	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}
+
+