Update README.md

README.md

@@ -158,16 +158,20 @@ Secondly, we could use it as an analytical tool, to study how temporal variation
 
 ## Limitations
 
-
-The ERWT series were trained for evaluation purposes, and carry some critical limitations. 
+The ERWT series were trained for evaluation purposes, and therefore carry some critical limitations. 
 
 ### Training Data
 
 Many of the limitations are a direct result of the data. ERWT models are trained on a rather small subsample of nineteenth-century British newspapers, and its predictions have to be understood in this context (remember, Her Majesty?). Moreover, the corpus has a strong Metropolitan and liberal bias (see section on Data Description for more information). 
 
+### Training Routine
+
+We created this model as part of a wider experiment, which attempted to establish best practices for training models with metadata. An overview of all the models is available on our [GitHub](https://github.com/Living-with-machines/ERWT/) page.
 
+To reduce training time, we based our experiments on a random subsample of the HMD corpus, consisting of half a billion tokens. 
+Furthermore, we only trained the models for one epoch, which implies . 
 
-We only trained for one epoch, which suggests. For the evaluation purposes we were interested in the relative performance of our models.
+We were mainly interested in the relative performance of the different ERWT models and .
 
 ## Data Description