Update README.md

README.md

@@ -5,7 +5,7 @@ task_categories:
 language:
 - en
 size_categories:
-- 1M<n<10M
+- 10M<n<100M
 ---
 
 TL;DR: The datasets for the temporal knowledge graph reasoning task.
@@ -25,21 +25,21 @@ See also: [[ICEWS14]](https://huggingface.co/datasets/linxy/ICEWS14) [[ICEWS05_1
 ```python
 >>> dataset = load_dataset("linxy/GDELT", "all")
 >>> len(dataset["train"]) + len(dataset["validation"]) + len(dataset["test"])
-1088769
+22117475
 >>> dataset["train"][0]
-{'query_name': 'Pe_aPt',
- 'definition': 'def Pe_aPt(e1, r1, e2, r2, e3): return Pe(e1, r1, after(Pt(e2, r2, e3)))',
- 'query': [6291, 372, 5683, 283, 5264],
- 'answer': [1077],
- 'easy_answer': [],
- 'args': ['e1', 'r1', 'e2', 'r2', 'e3']}
->>> dataset["test"][0]
 {'query_name': 'Pe',
  'definition': 'def Pe(e1, r1, t1): return Pe(e1, r1, t1)',
- 'query': [1426, 115, 28],
- 'answer': [3697],
+ 'query': [483, 18, 217],
+ 'answer': [26, 33, 40, 45, 65, 105, 107, 121, 139, 172, 187, 216, 264, 270, 313, 460, 480, 493],
  'easy_answer': [],
  'args': ['e1', 'r1', 't1']}
+>>> dataset["test"][0]
+{'query_name': 'Pe2',
+ 'definition': 'def Pe2(e1, r1, t1, r2, t2): return Pe(Pe(e1, r1, t1), r2, t2)',
+ 'query': [242, 38, 229, 1, 244],
+ 'answer': [9, 11, 24, 46, 76, 121, 140, 146, 209, 275, 280, 300, 380, 445, 463, 484],
+ 'easy_answer': [9, 11, 24, 46, 76, 146, 280, 300, 380, 445, 484],
+ 'args': ['e1', 'r1', 't1', 'r2', 't2']}
 ```
 
 'args' is the argument list of the query function, where name starting with 'e' is entity, and 'r' for relation, 't' for timestamp.
@@ -53,14 +53,14 @@ Therefore, we use the code below to load meta info which contains the vocabulary
 >>> dataset = load_dataset("linxy/GDELT", "meta")
 >>> meta_info = dataset_meta["train"][0]
 >>> meta_info
-{'dataset': 'ICEWS14',
- 'entity_count': 7128,
- 'relation_count': 230,
- 'timestamp_count': 365,
- 'valid_triples_count': 8941,
- 'test_triples_count': 8963,
- 'train_triples_count': 72826,
- 'triple_count': 90730,
+{'dataset': 'GDELT',
+ 'entity_count': 500,
+ 'relation_count': 20,
+ 'timestamp_count': 366,
+ 'valid_triples_count': 330906,
+ 'test_triples_count': 330845,
+ 'train_triples_count': 2308165,
+ 'triple_count': 2969916,
  'query_meta': {'query_name': [...], 'queries_count': [...], 'avg_answers_count': [...], ...},
  'entity2idx': {'name': [...], 'id': [...]},
  'relation2idx': {'name': [...], 'id': [...]},
@@ -71,7 +71,7 @@ Since the ids in the vocabulary are already sorted, we directly decode to access
 
 ```python
 >>> query
-[1426, 115, 28]
+[483, 18, 217]
 >>> args
 ['e1', 'r1', 't1']
 >>> for idx, arg_type in zip(query, args):
@@ -215,4 +215,4 @@ Please condiser citing this paper if you use the ```code``` or ```data``` from o
 
 TFLEX is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) license.
 
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+<p align="right">(<a href="#top">back to top</a>)</p>