add scan

README.md

@@ -38,6 +38,8 @@ This dataset contains 5 different word analogy questions used in [Analogy Langua
 | `t_rex_relational_similarity`       | 496/183             | 74/48                        | 60/19                                | [relbert/t_rex_relational_similarity](https://huggingface.co/datasets/relbert/t_rex_relational_similarity)             |
 | `conceptnet_relational_similarity`  | 1112/1192           | 19/17                        | 18/16                                | [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity)   |
 | `nell_relational_similarity`        | 400/600             | 5/7                          | 4/6                                  | [relbert/nell_relational_similarity](https://huggingface.co/datasets/relbert/nell_relational_similarity)               |
+| `scan` | 178/1616            | 3,36,136,10,45,78,15,21,55,120,153,91,28/3,36,136,10,45,78,15,21,55,120,153,91,28 | 2/2                                  | [relbert/scientific_and_creative_analogy](https://huggingface.co/datasets/relbert/scientific_and_creative_analogy) |
+
 
 ## Dataset Structure
 ### Data Instances

add_new_analogy_2.py

@@ -0,0 +1,66 @@
+import json
+import os
+from itertools import combinations
+from random import seed, randint, shuffle
+
+import pandas as pd
+from datasets import load_dataset
+
+
+def get_stats(filename):
+    with open(filename) as f:
+        _data = [json.loads(i) for i in f.read().splitlines()]
+    return len(_data), list(set([len(i['choice']) for i in _data])), len(list(set([i['prefix'] for i in _data])))
+
+
+def create_analogy(_data):
+    analogy_data = []
+    seed(12)
+    for i in _data:
+        source = []
+        target = []
+        for s, t in zip(i['source'], i['target']):
+            if s not in source and t not in target:
+                source.append(s)
+                target.append(t)
+        assert len(source) == len(target), f"{len(source)} != {len(target)}"
+        all_combinations = list(combinations(range(len(source)), 2))
+        for n, (q_h_id, q_t_id) in enumerate(all_combinations):
+            choice = [[target[x], target[y]] for m, (x, y) in enumerate(all_combinations) if m != n]
+            answer_id = randint(0, len(source) - 1)
+            choice = choice[:answer_id] + [[target[q_h_id], target[q_t_id]]] + choice[answer_id:]
+            assert choice[answer_id] == [target[q_h_id], target[q_t_id]]
+            analogy_data.append({
+                "stem": [source[q_h_id], source[q_t_id]],
+                "choice": choice,
+                "answer": answer_id,
+                "prefix": i["type"]
+            })
+    return analogy_data
+
+data = load_dataset("relbert/scientific_and_creative_analogy", split='test')
+data = create_analogy(data)
+data_m = [i for i in data if i['prefix'] == 'metaphor']
+data_s = [i for i in data if i['prefix'] != 'metaphor']
+seed(12)
+shuffle(data_m)
+shuffle(data_s)
+validation = data_s[:int(0.1 * len(data_s))] + data_m[:int(0.1 * len(data_m))]
+test = data_s[int(0.1 * len(data_s)):] + data_m[int(0.1 * len(data_m)):]
+os.makedirs("dataset/scan", exist_ok=True)
+with open("dataset/scan/valid.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in validation]))
+
+with open("dataset/scan/test.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in test]))
+
+t_size, t_num_choice, t_relation_type = get_stats("dataset/scan/test.jsonl")
+v_size, v_num_choice, v_relation_type = get_stats("dataset/scan/valid.jsonl")
+stat = [{
+    "name": "`scan`",
+    "Size (valid/test)": f"{v_size}/{t_size}",
+    "Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/{','.join([str(n) for n in t_num_choice])}",
+    "Num of relation group (valid/test)": f"{v_relation_type}/{t_relation_type}",
+    "Original Reference": "[relbert/scientific_and_creative_analogy](https://huggingface.co/datasets/relbert/scientific_and_creative_analogy)"
+}]
+print(pd.DataFrame(stat).to_markdown(index=False))

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 = "2.0.3"
+_VERSION = "2.0.5"
 _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?",
@@ -31,13 +31,14 @@ _URLS = {
             'bats': [f'{_URL}/bats/test.jsonl'],
             'google': [f'{_URL}/google/test.jsonl'],
             'sat': [f'{_URL}/sat/test.jsonl'],
-            'sat_metaphor': [f'{_URL}/sat_metaphor/test.jsonl'],
+            # 'sat_metaphor': [f'{_URL}/sat_metaphor/test.jsonl'],
             '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_relational_similarity": [f'{_URL}/t_rex_relational_similarity/test.jsonl'],
             "conceptnet_relational_similarity": [f'{_URL}/conceptnet_relational_similarity/test.jsonl'],
-            "nell_relational_similarity": [f'{_URL}/nell_relational_similarity/test.jsonl']
+            "nell_relational_similarity": [f'{_URL}/nell_relational_similarity/test.jsonl'],
+            'scan': [f'{_URL}/scan/test.jsonl'],
         },
     str(datasets.Split.VALIDATION): {
             'bats': [f'{_URL}/bats/valid.jsonl'],
@@ -48,7 +49,8 @@ _URLS = {
             "semeval2012_relational_similarity": [f'{_URL}/semeval2012_relational_similarity/valid.jsonl'],
             "t_rex_relational_similarity": [f'{_URL}/t_rex_relational_similarity/valid.jsonl'],
             "conceptnet_relational_similarity": [f'{_URL}/conceptnet_relational_similarity/valid.jsonl'],
-            "nell_relational_similarity": [f'{_URL}/nell_relational_similarity/valid.jsonl']
+            "nell_relational_similarity": [f'{_URL}/nell_relational_similarity/valid.jsonl'],
+            'scan': [f'{_URL}/scan/valid.jsonl'],
         }
 }