merge changes, added datasets to README.md and if train dataset or val dataset exists

README.md

@@ -45,3 +45,12 @@ With the data downloader in place, we simply need to (1) expose the val/test exa
 
 ### 3. Adding task training data to LM training set
 This part is the easiest. I guess we just write out some text files containing the training data? We can let the usual LM preprocessing pipeline handle it from there.
+=======
+## Summary (need to convert from google docs at some point):
+https://docs.google.com/document/d/177dwJpH8GHebISXYZSn4NL98sXdCtQMH82b7O5F7jmw/edit?usp=sharing
+
+## Current Datasets:
+
+[] CoQA 
+[] DROP
+

base.py

@@ -12,6 +12,14 @@ class LM(abc.ABC):
 
 
 class Dataset(abc.ABC):
+    @abc.abstractmethod
+    def has_training_docs(self):
+        pass
+    
+    @abc.abstractmethod
+    def has_validation_docs(self):
+        pass
+
     @abc.abstractmethod
     def training_docs(self):
         pass
@@ -42,4 +50,4 @@ class Dataset(abc.ABC):
     @abc.abstractmethod
     def evaluate(self, docs, lm):
         pass
-    
\ No newline at end of file
+