Update README.md

README.md

@@ -20,16 +20,17 @@ widget:
 <img src="https://upload.wikimedia.org/wikipedia/commons/5/5b/NCI_peas_in_pod.jpg" alt="erwt" width="200" >
 
 # ERWT-year
-🌺ERWT is a language model that is (🤭 maybe 🤫) better at history than you...🌺
 
-\*ERWT is dutch for PEA.
+🌺ERWT is a language model that is (🤭 maybe 🤫) better at history than you...🌺
 
 ERWT is a fine-tuned [`distilbert-base-cased`](https://huggingface.co/distilbert-base-cased) model trained on historical newspapers from the [Heritage Made Digital collection](https://huggingface.co/datasets/davanstrien/hmd-erwt-training) with **temporal metadata**.
 
-ERWT performs time-sensitive masked language modelling. It can also guess the year a text was written.
+ERWT performs **time-sensitive masked language modelling** and can be used for **date prediction** as well.
 
 This model is served to you by [Kaspar von Beelen](https://huggingface.co/Kaspar) and [Daniel van Strien](https://huggingface.co/davanstrien), *"Improving AI, one pea at a time"*.
 
+\*ERWT is dutch for PEA.
+
 ## Introductory Note: Repent Now. 😇
 
 The ERWT models are trained for **experimental purposes**, please use them with care. 
@@ -61,7 +62,6 @@ Exposing the model to temporal metadata allows us to investigate **historical la
 
 ### Historical Language Change: Her/His Majesty? 👑
 
-
 Let's show how ERWT works with a very concrete example. 
 
 The ERWT models are trained on British newspapers from before 1880 (Why? Long story, don't ask...) and can be used to monitor historical change in this specific context.
@@ -137,7 +137,13 @@ mask_filler("[MASK] [DATE] The Franco-Prussian war is a matter of great concern.
 
 ERWT clearly learned a lot about history of German unification by ploughing through a plethora of nineteenth century newspaper articles: it correctly returns "1870" as the predicted year.
 
-Again, we have to ask: Who cares? Wikipedia can tell us very much the same, and don't we already have correct timestamps for newspaper data.
+Again, we have to ask: Who cares? Wikipedia can tell us pretty much the same. More importantly, don't we already have timestamps for newspaper data.
+
+In both cases, our answers would be "yes, but...". ERWT's time-stamping powers has little instrumental use and won't make us rich (but donations are welcome of course 🤑) we nonetheless believe date prediction has value for research purposes. We can use ERWT for "fictitious" prediction, i.e. as a diagnostic tool. 
+
+Firstly, masking the temporal information, 
+
+Secondly, 
 
 
 ## Limitations