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import json |
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import os |
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from itertools import combinations |
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from random import shuffle, seed |
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import pandas as pd |
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from datasets import load_dataset |
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def get_stats(filename): |
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with open(filename) as f: |
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_data = [json.loads(i) for i in f.read().splitlines()] |
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return len(_data), list(set([len(i['choice']) for i in _data])), len(list(set([i['prefix'] for i in _data]))) |
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def lexical_overlap(word_a, word_b): |
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for a in word_a.split(" "): |
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for b in word_b.split(" "): |
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if a.lower() == b.lower(): |
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return True |
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return False |
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def create_analogy(_data, output_path, negative_per_relation, instance_per_relation=100): |
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df = _data.to_pandas() |
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analogy_data = [] |
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for _, i in df.iterrows(): |
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target = [(q.tolist(), c.tolist()) for q, c in combinations(i['positives'], 2) |
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if not any(lexical_overlap(c[0], y) or lexical_overlap(c[1], y) for y in q)] |
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if len(target) == 0: |
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continue |
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if len(target) > instance_per_relation: |
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seed(42) |
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shuffle(target) |
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target = target[:instance_per_relation] |
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for m, (q, c) in enumerate(target): |
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negative = [] |
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for r in df['relation_type']: |
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if r == i['relation_type']: |
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continue |
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target_per_relation = [y.tolist() for y in df[df['relation_type'] == r]['positives'].values[0]] |
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shuffle(target_per_relation) |
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negative += target_per_relation[:negative_per_relation] |
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analogy_data.append({ |
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"stem": q, |
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"choice": [c, c[::-1]] + negative, |
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"answer": 0, |
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"prefix": i["relation_type"] |
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}) |
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os.makedirs(os.path.dirname(output_path), exist_ok=True) |
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with open(output_path, "w") as f: |
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f.write("\n".join([json.dumps(i) for i in analogy_data])) |
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stat = [] |
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if not os.path.exists("dataset/semeval2012_relational_similarity/valid.jsonl"): |
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data = load_dataset("relbert/semeval2012_relational_similarity", split="validation") |
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analogy_data = [{ |
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"stem": i['positives'][0], "choice": i["negatives"] + [i['positives'][1]], "answer": 2, "prefix": i["relation_type"] |
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} for i in data] |
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os.makedirs("dataset/semeval2012_relational_similarity", exist_ok=True) |
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with open("dataset/semeval2012_relational_similarity/valid.jsonl", "w") as f: |
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f.write("\n".join([json.dumps(i) for i in analogy_data])) |
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v_size, v_num_choice, v_relation_type = get_stats("dataset/semeval2012_relational_similarity/valid.jsonl") |
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stat.append({ |
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"name": "`semeval2012_relational_similarity`", |
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"Size (valid/test)": f"{v_size}/-", |
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"Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/-", |
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"Num of relation group (valid/test)": f"{v_relation_type}/-", |
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"Original Reference": "[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity)" |
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}) |
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data = load_dataset("relbert/t_rex_relational_similarity", "filter_unified.min_entity_1_max_predicate_100", split="test") |
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create_analogy(data, "dataset/t_rex_relational_similarity/test.jsonl", negative_per_relation=2) |
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data = load_dataset("relbert/t_rex_relational_similarity", "filter_unified.min_entity_4_max_predicate_100", split="validation") |
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create_analogy(data, "dataset/t_rex_relational_similarity/valid.jsonl", negative_per_relation=1) |
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t_size, t_num_choice, t_relation_type = get_stats("dataset/t_rex_relational_similarity/test.jsonl") |
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v_size, v_num_choice, v_relation_type = get_stats("dataset/t_rex_relational_similarity/valid.jsonl") |
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stat.append({ |
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"name": "`t_rex_relational_similarity`", |
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"Size (valid/test)": f"{v_size}/{t_size}", |
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"Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/{','.join([str(n) for n in t_num_choice])}", |
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"Num of relation group (valid/test)": f"{v_relation_type}/{t_relation_type}", |
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"Original Reference": "[relbert/t_rex_relational_similarity](https://huggingface.co/datasets/relbert/t_rex_relational_similarity)" |
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}) |
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data = load_dataset("relbert/conceptnet_relational_similarity", split="test") |
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create_analogy(data, "dataset/conceptnet_relational_similarity/test.jsonl", negative_per_relation=1) |
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data = load_dataset("relbert/conceptnet_relational_similarity", split="validation") |
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create_analogy(data, "dataset/conceptnet_relational_similarity/valid.jsonl", negative_per_relation=1) |
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t_size, t_num_choice, t_relation_type = get_stats("dataset/conceptnet_relational_similarity/test.jsonl") |
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v_size, v_num_choice, v_relation_type = get_stats("dataset/conceptnet_relational_similarity/valid.jsonl") |
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stat.append({ |
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"name": "`conceptnet_relational_similarity`", |
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"Size (valid/test)": f"{v_size}/{t_size}", |
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"Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/{','.join([str(n) for n in t_num_choice])}", |
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"Num of relation group (valid/test)": f"{v_relation_type}/{t_relation_type}", |
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"Original Reference": "[relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity)" |
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}) |
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data = load_dataset("relbert/nell_relational_similarity", split="test") |
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create_analogy(data, "dataset/nell_relational_similarity/test.jsonl", negative_per_relation=1) |
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data = load_dataset("relbert/nell_relational_similarity", split="validation") |
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create_analogy(data, "dataset/nell_relational_similarity/valid.jsonl", negative_per_relation=1) |
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t_size, t_num_choice, t_relation_type = get_stats("dataset/nell_relational_similarity/test.jsonl") |
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v_size, v_num_choice, v_relation_type = get_stats("dataset/nell_relational_similarity/valid.jsonl") |
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stat.append({ |
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"name": "`nell_relational_similarity`", |
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"Size (valid/test)": f"{v_size}/{t_size}", |
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"Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/{','.join([str(n) for n in t_num_choice])}", |
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"Num of relation group (valid/test)": f"{v_relation_type}/{t_relation_type}", |
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"Original Reference": "[relbert/nell_relational_similarity](https://huggingface.co/datasets/relbert/nell_relational_similarity)" |
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}) |
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print(pd.DataFrame(stat).to_markdown(index=False)) |
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