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create_example_constraint.py
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create_example_constraint.py
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from datasets import load_dataset
from promptsource.templates import DatasetTemplates
import random
import json
from tqdm import tqdm
all_a_templates = [
"{instruction}\n{answer}",
"{instruction}\nA: {answer}",
"{instruction}\nAnswer: {answer}",
"{instruction}\nANSWER: {answer}",
"{instruction}\n[Answer]\n{answer}",
"{instruction}\n#Answer#\n{answer}",
"{instruction}\nThe answer is: {answer}",
"{instruction}\n{{'answer': '{answer}'}}",
"{instruction}\n{{'Answer': '{answer}'}}",
"{instruction}\n<body>{answer}</body>",
"{instruction}\nResponse: {answer}",
"{instruction}\nRESPONSE: {answer}",
"{instruction}\n[Response]\n{answer}",
"{instruction}\n#Response#\n{answer}",
"{instruction}\nThe response is: {answer}",
"{instruction}\n{{'response': '{answer}'}}",
"{instruction}\n{{'Response': '{answer}'}}",
"{instruction}\nBot: {answer}",
"{instruction}\nBOT: {answer}",
"{instruction}\n[Bot]\n{answer}",
"{instruction}\n#Bot#\n{answer}",
"{instruction}\nThe response of the bot is: {answer}",
"{instruction}\n{{'bot': '{answer}'}}",
"{instruction}\n{{'Bot': '{answer}'}}",
"{instruction}\nAI assistant: {answer}",
"{instruction}\n[AI assistant]\n{answer}",
"{instruction}\n#AI assistant#\n{answer}",
"{instruction}\nThe response of the AI assistant is: {answer}",
"{instruction}\n{{'AI assistant': '{answer}'}}"
]
def split_integer(m, n):
if n > m:
print("Error: n should be less than or equal to m")
return None
# 生成n-1个随机整数
random_numbers = sorted(random.sample(range(1, m), n - 1))
# 添加0和m作为端点
random_numbers = [0] + random_numbers + [m]
# 计算相邻数字之间的差值
result = [random_numbers[i+1] - random_numbers[i] for i in range(n)]
return result
def create_example_constraint(all_q_templates, sampled_dataset, sampled_q_templates, sampled_a_templates, n_shot, n_constraint):
selected_q_templates = sampled_q_templates[: n_constraint]
match_q_template = selected_q_templates[0]
selected_a_templates = sampled_a_templates[: n_constraint]
match_a_template = selected_a_templates[0]
split_integers = split_integer(n_shot, n_constraint)
five_q_templates = []
five_a_templates = []
for i in range(len(split_integers)):
for _ in range(split_integers[i]):
five_q_templates.append(selected_q_templates[i]) # a, b, b, c, c
five_a_templates.append(selected_a_templates[i]) # a, b, b, c, c
five_q_a_templates = [{"q_template": q, "a_template": a} for q, a in zip(five_q_templates, five_a_templates)]
random.shuffle(five_q_a_templates)
data = [{'data': d, "q_template": f['q_template'], "a_template": f['a_template']} for d, f in zip(sampled_dataset, five_q_a_templates)]
few_shot = ""
for d in data:
instruction, answer = all_q_templates[d['q_template']].apply(d['data'])
few_shot += d['a_template'].format(instruction=instruction, answer=answer)
few_shot += "\n\n"
instruction, answer = all_q_templates[match_q_template].apply(sampled_dataset[-1])
few_shot += instruction
answer = match_a_template.replace('{instruction}\n', '').format(answer=answer)
return few_shot, match_a_template, answer
dataset_names = [
"aeslc",
"ag_news",
"art",
"billsum",
"bing_coronavirus_query_set",
"biosses",
"common_gen",
"commonsense_qa",
"craigslist_bargains",
"dream",
"drop",
"emotion",
"freebase_qa",
"generated_reviews_enth",
"google_wellformed_query",
"gutenberg_time",
"hans",
"health_fact",
"imdb",
"lambada",
"limit",
"math_qa",
"mwsc",
"narrativeqa",
"newspop",
"numer_sense",
"onestop_english",
"piqa",
"poem_sentiment",
"qa_srl",
"quoref",
"rotten_tomatoes",
"samsum",
"sciq",
"species_800",
"squad",
"web_questions",
"wiqa",
"xsum",
"zest",
]
n_sample = 1
n_shot = 5
samples = []
for i in tqdm(range(len(dataset_names))):
print(dataset_names[i])
dataset = load_dataset(dataset_names[i], split="train")
all_q_templates = DatasetTemplates(dataset_names[i])
assert(len(all_q_templates) >= n_shot)
for j in range(n_sample):
shuffled_dataset = dataset.shuffle()
sampled_dataset = shuffled_dataset.select(range(n_shot+1))
sampled_q_templates = random.sample(all_q_templates.name_to_id_mapping.keys(), n_shot)
sampled_a_templates = random.sample(all_a_templates, n_shot)
for level in range(n_shot):
few_shot, match_a_template, answer = create_example_constraint(all_q_templates, sampled_dataset, sampled_q_templates, sampled_a_templates, n_shot=n_shot, n_constraint=level+1)
samples.append({
"example_id": i + j + 1,
"category": "example",
"source": dataset_names[i],
"level": level+1,
"instruction": few_shot,
"target": match_a_template,
"answer": answer
})
print(f"create {len(samples)} samples.")
json_str = json.dumps(samples, indent=4)
with open('example_constraints.json', mode='w', encoding='utf-8') as json_file:
json_file.write(json_str)