fix readme

check_stats.py

@@ -58,3 +58,11 @@ df_test = pd.DataFrame([{
 for c in df_test.columns:
     df_test.loc[:, c] = df_test[c].map('{:,d}'.format)
 print(df_test.to_markdown(index=False))
+df["number of triples (test)"] = df_test["number of triples (test)"].values[0]
+df["number of unique predicates (test)"] = df_test["number of unique predicates (test)"].values[0]
+df["number of unique entities (test)"] = df_test["number of unique entities (test)"].values[0]
+df.pop("number of triples (all)")
+df.pop("number of unique predicates (all)")
+df.pop("number of unique entities (all)")
+df = df[sorted(df.columns)]
+df.to_csv("data/stats.csv")

data/stats.csv

@@ -0,0 +1,17 @@
+data,number of triples (test),number of triples (train),number of triples (validation),number of unique entities (test),number of unique entities (train),number of unique entities (validation),number of unique predicates (test),number of unique predicates (train),number of unique predicates (validation)
+filter_unified.min_entity_1_max_predicate_100,122,"7,075",787,188,"8,496","1,324",34,212,166
+filter_unified.min_entity_1_max_predicate_50,122,"4,131",459,188,"5,111",790,34,212,156
+filter_unified.min_entity_1_max_predicate_25,122,"2,358",262,188,"3,079",465,34,212,144
+filter_unified.min_entity_1_max_predicate_10,122,"1,134",127,188,"1,587",233,34,210,94
+filter_unified.min_entity_2_max_predicate_100,122,"4,873",542,188,"5,386",887,34,195,139
+filter_unified.min_entity_2_max_predicate_50,122,"3,002",334,188,"3,457",575,34,193,139
+filter_unified.min_entity_2_max_predicate_25,122,"1,711",191,188,"2,112",331,34,195,113
+filter_unified.min_entity_2_max_predicate_10,122,858,96,188,"1,149",177,34,194,81
+filter_unified.min_entity_3_max_predicate_100,122,"3,659",407,188,"3,892",662,34,173,116
+filter_unified.min_entity_3_max_predicate_50,122,"2,336",260,188,"2,616",447,34,174,115
+filter_unified.min_entity_3_max_predicate_25,122,"1,390",155,188,"1,664",272,34,173,94
+filter_unified.min_entity_3_max_predicate_10,122,689,77,188,922,135,34,171,59
+filter_unified.min_entity_4_max_predicate_100,122,"2,995",333,188,"3,104",563,34,158,105
+filter_unified.min_entity_4_max_predicate_50,122,"1,989",222,188,"2,225",375,34,157,102
+filter_unified.min_entity_4_max_predicate_25,122,"1,221",136,188,"1,458",237,34,158,76
+filter_unified.min_entity_4_max_predicate_10,122,603,68,188,797,126,34,157,52

t_rex.py

@@ -7,7 +7,7 @@ import datasets
 logger = datasets.logging.get_logger(__name__)
 _DESCRIPTION = """T-Rex dataset."""
 _NAME = "t_rex"
-_VERSION = "0.0.6"
+_VERSION = "0.0.7"
 _CITATION = """
 @inproceedings{elsahar2018t,
   title={T-rex: A large scale alignment of natural language with knowledge base triples},
@@ -23,7 +23,7 @@ MIN_ENTITY_FREQ = [1, 2, 3, 4]
 MAX_PREDICATE_FREQ = [100, 50, 25, 10]
 
 _TYPES = [f"filter_unified.min_entity_{a}_max_predicate_{b}" for a, b in product(MIN_ENTITY_FREQ, MAX_PREDICATE_FREQ)]
-_TYPES += ["raw", "filter", "filter_unified"]
+# _TYPES += ["raw", "filter", "filter_unified"]
 _NON_SPLITS = ["raw", "filter", "filter_unified"]
 _URLS = {i: {str(datasets.Split.TRAIN): [f'{_URL}/t_rex.{i}.jsonl']} if i in _NON_SPLITS else {
     str(datasets.Split.TRAIN): [f'{_URL}/t_rex.{i}.train.jsonl'],