Update xor-tydi.py

xor-tydi.py

@@ -33,17 +33,14 @@ _DESCRIPTION = "dataset load script for Wikipedia NQ"
 
 base = "/home/czhang/src/task-sparse/tevatron/hgf_datasets/xor-tydi"
 _DATASET_URLS = {
-    'eng_span': {
-        'train': f'https://huggingface.co/datasets/crystina-z/xor-tydi/resolve/main/train/xor-t2e-100w.jsonl.gz',
+    'targetQ': {
+        'train': f'https://huggingface.co/datasets/crystina-z/xor-tydi/resolve/main/train/targetL_dpr_train_data.json',
         'dev': f'https://huggingface.co/datasets/crystina-z/xor-tydi/resolve/main/dev/xor_dev_retrieve_eng_span_v1_1.jsonl',
         'test': f'https://huggingface.co/datasets/crystina-z/xor-tydi/resolve/main/test/xor_test_retrieve_eng_span_q_only_v1_1.jsonl',
     },
-    'full': {
-        'train': f'https://huggingface.co/datasets/crystina-z/xor-tydi/resolve/main/train/xor-t2e-100w.jsonl.gz',
-        'dev': f'https://huggingface.co/datasets/crystina-z/xor-tydi/resolve/main/dev/xor_dev_full_v1_1.jsonl',
-        'test': f'https://huggingface.co/datasets/crystina-z/xor-tydi/resolve/main/test/xor_test_full_q_only_v1_1.jsonl',
+    'engQ': {
+        'train': f'https://huggingface.co/datasets/crystina-z/xor-tydi/resolve/main/train/EN_dpr_train_data.json',
     }
-    # 'test': f"{base}",
 }
 
 
@@ -107,15 +104,29 @@ class XORTyDi(datasets.GeneratorBasedBuilder):
     
     def _generate_examples(self, files):
         """Yields examples."""
+        # def process_train_entry(data):
+        #     if data.get('negative_passages') is None:
+        #         data['negative_passages'] = []
+        #     if data.get('positive_passages') is None:
+        #         data['positive_passages'] = []
+        #     if data.get('answers') is None:
+        #         data['answers'] = []
+        #     return data['query_id'], data
+
         def process_train_entry(data):
-            if data.get('negative_passages') is None:
-                data['negative_passages'] = []
-            if data.get('positive_passages') is None:
-                data['positive_passages'] = []
-            if data.get('answers') is None:
-                data['answers'] = []
-            return data['query_id'], data
-        
+            positive_ctxs = data["positive_ctxs"]
+            hard_negative_ctxs = data["hard_negative_ctxs"]
+            # each ctx: {'title':... , 'text': ....}
+            
+            
+            return data["id"], {
+                "query_id": data["id"],
+                "query": data["question"],
+                "answers": data.get("answers", []),
+                "positive_passages": [{**doc, 'docid': f'pos-{i}-{random.randint()}'} for i, doc in enumerate(positive_ctxs)],
+                "negative_passages": [{**doc, 'docid': f'neg-{i}-{random.randint()}'} for i, doc in enumerate(hard_negative_ctxs)],
+            }
+
         def process_dev_test_entry(data):
             return data["id"], {
                 "query_id": data["id"],
@@ -125,13 +136,18 @@ class XORTyDi(datasets.GeneratorBasedBuilder):
                 "negative_passages": [],
             }
 
-        for filepath in files:
+        assert len(files) == 1
+        filepath = files[0]
+        if filepath.endswith(".jsonl"):
             with open(filepath, encoding="utf-8") as f:
                 for line in f:
                     data = json.loads(line)
 
                     if "id" in data and "query_id" not in data:
                         yield process_dev_test_entry(data)
-                    else:
-                        yield process_train_entry(data)
+        else:
+            with open(filepath, encoding="utf-8") as f:
+                all_data = json.load(f)
+                for data in all_data:
+                    yield process_train_entry(data)