fix readme

create_split.py

@@ -37,19 +37,26 @@ if not os.path.exists("data/t_rex.filter_unified.test.jsonl"):
         print(f"{json.dumps(i, indent=4)}")
         flag = input(">>> (enter to add to test)")
         if flag == '':
+            i['relation'] = i.pop('predicate')
+            i['head'] = i.pop('subject')
+            i['tail'] = i.pop('object')
             f_writer.write(json.dumps(i) + '\n')
     f_writer.close()
 
 with open("data/t_rex.filter_unified.test.jsonl") as f:
     data_test = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
-    test_predicate = set([i['predicate'] for i in data_test])
+    test_predicate = set([i['relation'] for i in data_test])
 
 
 seed(42)
 for min_e_freq, max_p_freq in product(parameters_min_e_freq, parameters_max_p_freq):
     with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.jsonl") as f:
         data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
-    data = [i for i in data if i['predicate'] not in test_predicate]
+    for i in data:
+        i['relation'] = i.pop('predicate')
+        i['head'] = i.pop('subject')
+        i['tail'] = i.pop('object')
+    data = [i for i in data if i['relation'] not in test_predicate]
     shuffle(data)
     data_train = data[:int(len(data) * 0.9)]
     data_valid = data[int(len(data) * 0.9):]
@@ -57,10 +64,3 @@ for min_e_freq, max_p_freq in product(parameters_min_e_freq, parameters_max_p_fr
         f.write('\n'.join([json.dumps(i) for i in data_train]))
     with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.validation.jsonl", "w") as f:
         f.write('\n'.join([json.dumps(i) for i in data_valid]))
-
-
-#
-# # make test split
-# with open(f"data/t_rex.filter_unified.jsonl") as f:
-#     data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
-# train_data, validation_data, test_data = create_split(data)

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filtering_purify.py

@@ -71,7 +71,6 @@ def main(min_entity_freq, max_pairs_predicate, min_pairs_predicate: int = 3, ran
     df_balanced.pop("count_object")
     df_balanced.pop("count_sum")
     df_balanced = df_balanced.drop_duplicates(subset=['subject', 'object', 'predicate'], keep='last')
-
     # return data
     target_data = [i.to_dict() for _, i in df_balanced.iterrows()]
     predicate_dist = df_balanced.groupby("predicate")['text'].count().sort_values(ascending=False).to_dict()

t_rex.py

@@ -7,7 +7,7 @@ import datasets
 logger = datasets.logging.get_logger(__name__)
 _DESCRIPTION = """T-Rex dataset."""
 _NAME = "t_rex"
-_VERSION = "0.0.7"
+_VERSION = "0.1.0"
 _CITATION = """
 @inproceedings{elsahar2018t,
   title={T-rex: A large scale alignment of natural language with knowledge base triples},
@@ -77,11 +77,11 @@ class TREX(datasets.GeneratorBasedBuilder):
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
+                    "relation": datasets.Value("string"),
+                    "head": datasets.Value("string"),
+                    "tail": datasets.Value("string"),
                     "title": datasets.Value("string"),
                     "text": datasets.Value("string"),
-                    "predicate": datasets.Value("string"),
-                    "object": datasets.Value("string"),
-                    "subject": datasets.Value("string")
                 }
             ),
             supervised_keys=None,