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import json
import os
from itertools import combinations
from random import shuffle, seed
from datasets import load_dataset
# # create analogy from `relbert/semeval2012_relational_similarity`
# data = load_dataset("relbert/semeval2012_relational_similarity", split="validation")
# analogy_data = [{
# "stem": i['positives'][0], "choice": i["negatives"] + [i['positives'][1]], "answer": 2, "prefix": i["relation_type"]
# } for i in data]
# os.makedirs("dataset/semeval2012_relational_similarity", exist_ok=True)
# with open("dataset/semeval2012_relational_similarity/valid.jsonl", "w") as f:
# f.write("\n".join([json.dumps(i) for i in analogy_data]))
# # create analogy from `relbert/t_rex_relational_similarity`
# data = load_dataset("relbert/t_rex_relational_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_relational_similarity", exist_ok=True)
# with open("dataset/t_rex_relational_similarity/test.jsonl", "w") as f:
# f.write("\n".join([json.dumps(i) for i in analogy_data]))
#
# data = load_dataset("relbert/t_rex_relational_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_relational_similarity", exist_ok=True)
# with open("dataset/t_rex_relational_similarity/valid.jsonl", "w") as f:
# f.write("\n".join([json.dumps(i) for i in analogy_data]))
# create analogy from `relbert/conceptnet_relational_similarity`
for s in ['test', 'validation']:
data = load_dataset("relbert/conceptnet_relational_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_relational_similarity", exist_ok=True)
with open(f"dataset/conceptnet_relational_similarity/{s if s == 'test' else 'valid'}.jsonl", "w") as f:
f.write("\n".join([json.dumps(i) for i in analogy_data])) |