add generate docs and generate text for coqa

datasets/coqa/evaluateCoQA.py → coqa.py

 
-from ...base import Dataset
+from base import Dataset
 import os
 import json
+import random
+
 class CoQA(Dataset):
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return False
+    
     def training_docs(self):
-        pass
+        myjson = json.load(open('data/coqa/coqa-train-v1.0.json'))['data']
+        return self.load_doc(myjson)
 
     def validation_docs(self):
         pass
-    
+
     def test_docs(self):
-        pass
+        myjson = json.load(open('data/coqa/coqa-dev-v1.0.json'))['data']    
+        return self.load_doc(myjson)
     
     def fewshot_examples(self, k):
         traindocs = list(self.training_docs())
@@ -22,8 +32,22 @@ class CoQA(Dataset):
     def fewshot_description(self):
         pass
 
+    def load_doc(self, myjson):
+        docs = []
+        for item in myjson:
+            new_instance = [item['story']]
+            qa_pairs = zip(item['questions'], item['answers'])
+            for pair in qa_pairs:
+                new_instance.append('\n')
+                new_instance.append(''.join(['Q: ',pair[0]['input_text']]))
+                new_instance.append(''.join(['A: ',pair[1]['input_text']]))
+            docs.append(new_instance)  
+        return docs
+    
     def doc_to_text(self, doc, include_target=True):
-        json.load(open(doc))
+        text = '\n<|endoftext|>\n'.join(['\n'.join(instance) for instance in doc])
+        text = text + '\n<|endoftext|>'
+        return text
 
-        
- 
+    def evaluate(self, docs, lm):
+        pass