fix readme

README.md

@@ -27,8 +27,9 @@ This dataset contains 5 different word analogy questions used in [Analogy Langua
 | `u4`      | 48/432            | 5,4,3         | 5                     | [EnglishForEveryone](https://englishforeveryone.org/Topics/Analogies.html) |
 | `google`  | 50/500            | 4             | 2                     | [Mikolov et al., (2013)](https://www.aclweb.org/anthology/N13-1090.pdf)    |
 | `bats`    | 199/1799          | 4             | 3                     | [Gladkova et al., (2016)](https://www.aclweb.org/anthology/N18-2017.pdf)   |
-| `t_rex_relation_similarity`    | 467/467          | 6             | -                     | [relbert/t_rex_relation_similarity](https://huggingface.co/datasets/relbert/t_rex_relation_similarity)  |
-| `semeval2012_relational_similarity`    | 78/-          | 3             | -                     | [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity)  |
+| `semeval2012_relational_similarity`   | 78/-      | 3 | -             | [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity)  |
+| `t_rex_relation_similarity`           | 467/467   | 6 | -             | [relbert/t_rex_relation_similarity](https://huggingface.co/datasets/relbert/t_rex_relation_similarity)  |
+| `conceptnet_relation_similarity`      | 546/570   | 6 | -             | [relbert/conceptnet_relation_similarity](https://huggingface.co/datasets/relbert/conceptnet_relation_similarity)  |
 
 ## Dataset Structure
 ### Data Instances

add_new_analogy.py

@@ -14,41 +14,63 @@ from datasets import load_dataset
 #     f.write("\n".join([json.dumps(i) for i in analogy_data]))
 
 
-# create analogy from `relbert/t_rex_relation_similarity`
-data = load_dataset("relbert/t_rex_relation_similarity", "filter_unified.min_entity_1_max_predicate_100", split="test")
-analogy_data = []
-for i in data:
-    if len(i['positives']) < 2:
-        continue
-    for m, (q, c) in enumerate(combinations(i['positives'], 2)):
-        if m > 5:
-            break
-        negative = i['negatives']
-        for n in range(6):
-            seed(n)
-            shuffle(negative)
-            analogy_data.append({
-                "stem": q, "choice": [c] + negative[:5], "answer": 0, "prefix": i["relation_type"]
-            })
-os.makedirs("dataset/t_rex_relation_similarity", exist_ok=True)
-with open("dataset/t_rex_relation_similarity/test.jsonl", "w") as f:
-    f.write("\n".join([json.dumps(i) for i in analogy_data]))
+# # create analogy from `relbert/t_rex_relation_similarity`
+# data = load_dataset("relbert/t_rex_relation_similarity", "filter_unified.min_entity_1_max_predicate_100", split="test")
+# analogy_data = []
+# for i in data:
+#     if len(i['positives']) < 2:
+#         continue
+#     for m, (q, c) in enumerate(combinations(i['positives'], 2)):
+#         if m > 5:
+#             break
+#         negative = i['negatives']
+#         for n in range(6):
+#             seed(n)
+#             shuffle(negative)
+#             analogy_data.append({
+#                 "stem": q, "choice": [c] + negative[:5], "answer": 0, "prefix": i["relation_type"]
+#             })
+# os.makedirs("dataset/t_rex_relation_similarity", exist_ok=True)
+# with open("dataset/t_rex_relation_similarity/test.jsonl", "w") as f:
+#     f.write("\n".join([json.dumps(i) for i in analogy_data]))
+#
+# data = load_dataset("relbert/t_rex_relation_similarity", "filter_unified.min_entity_4_max_predicate_100", split="validation")
+# analogy_data = []
+# for i in data:
+#     if len(i['positives']) < 5:
+#         continue
+#     for m, (q, c) in enumerate(combinations(i['positives'], 2)):
+#         if m > 5:
+#             break
+#         negative = i['negatives']
+#         for n in range(3):
+#             seed(n)
+#             shuffle(negative)
+#             analogy_data.append({
+#                 "stem": q, "choice": [c] + negative[:5], "answer": 0, "prefix": i["relation_type"]
+#             })
+# os.makedirs("dataset/t_rex_relation_similarity", exist_ok=True)
+# with open("dataset/t_rex_relation_similarity/valid.jsonl", "w") as f:
+#     f.write("\n".join([json.dumps(i) for i in analogy_data]))
 
-data = load_dataset("relbert/t_rex_relation_similarity", "filter_unified.min_entity_4_max_predicate_100", split="validation")
-analogy_data = []
-for i in data:
-    if len(i['positives']) < 5:
-        continue
-    for m, (q, c) in enumerate(combinations(i['positives'], 2)):
-        if m > 5:
-            break
-        negative = i['negatives']
-        for n in range(3):
-            seed(n)
-            shuffle(negative)
-            analogy_data.append({
-                "stem": q, "choice": [c] + negative[:5], "answer": 0, "prefix": i["relation_type"]
-            })
-os.makedirs("dataset/t_rex_relation_similarity", exist_ok=True)
-with open("dataset/t_rex_relation_similarity/valid.jsonl", "w") as f:
-    f.write("\n".join([json.dumps(i) for i in analogy_data]))
+# create analogy from `relbert/conceptnet_relation_similarity`
+for s in ['test', 'validation']:
+    data = load_dataset("relbert/conceptnet_relation_similarity", split=s)
+    analogy_data = []
+    for i in data:
+        if len(i['positives']) < 2:
+            continue
+        for m, (q, c) in enumerate(combinations(i['positives'], 2)):
+            if m > 5:
+                break
+            negative = i['negatives']
+            for n in range(6):
+                seed(n)
+                shuffle(negative)
+                analogy_data.append({
+                    "stem": q, "choice": [c] + negative[:5], "answer": 0, "prefix": i["relation_type"]
+                })
+    print(len(analogy_data))
+    os.makedirs("dataset/conceptnet_relation_similarity", exist_ok=True)
+    with open(f"dataset/conceptnet_relation_similarity/{s if s == 'test' else 'valid'}.jsonl", "w") as f:
+        f.write("\n".join([json.dumps(i) for i in analogy_data]))

analogy_questions.py

@@ -5,7 +5,7 @@ import datasets
 logger = datasets.logging.get_logger(__name__)
 _DESCRIPTION = """[Analogy Question](https://aclanthology.org/2021.acl-long.280/)"""
 _NAME = "analogy_questions"
-_VERSION = "1.0.4"
+_VERSION = "1.0.6"
 _CITATION = """
 @inproceedings{ushio-etal-2021-bert,
     title = "{BERT} is to {NLP} what {A}lex{N}et is to {CV}: Can Pre-Trained Language Models Identify Analogies?",
@@ -34,7 +34,8 @@ _URLS = {
             'sat_full': [f'{_URL}/sat/test.jsonl', f'{_URL}/sat/valid.jsonl'],
             'u2': [f'{_URL}/u2/test.jsonl'],
             'u4': [f'{_URL}/u4/test.jsonl'],
-            "t_rex_relation_similarity": [f'{_URL}/t_rex_relation_similarity/test.jsonl']
+            "t_rex_relation_similarity": [f'{_URL}/t_rex_relation_similarity/test.jsonl'],
+            "conceptnet_relation_similarity": [f'{_URL}/conceptnet_relation_similarity/test.jsonl']
         },
     str(datasets.Split.VALIDATION): {
             'bats': [f'{_URL}/bats/valid.jsonl'],
@@ -43,7 +44,8 @@ _URLS = {
             'u2': [f'{_URL}/u2/valid.jsonl'],
             'u4': [f'{_URL}/u4/valid.jsonl'],
             "semeval2012_relational_similarity": [f'{_URL}/u4/valid.jsonl'],
-            "t_rex_relation_similarity": [f'{_URL}/t_rex_relation_similarity/valid.jsonl']
+            "t_rex_relation_similarity": [f'{_URL}/t_rex_relation_similarity/valid.jsonl'],
+            "conceptnet_relation_similarity": [f'{_URL}/conceptnet_relation_similarity/valid.jsonl']
         }
 }