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dataset_loader.py
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dataset_loader.py
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import json
import pandas as pd
import tiktoken
from src.constructions import ChatGPTSchema, ResultsForHumanSchema
import os
import ast
from tqdm import tqdm
from src.utils import read_jsonl, save_jsonl, extract_answer
# define the datasets
english_qa_datasets = ["lsat-ar", "lsat-lr", "lsat-rc", "logiqa-en", "sat-math", "sat-en", "aqua-rat",
"sat-en-without-passage", "gaokao-english"]
chinese_qa_datasets = ["logiqa-zh", "jec-qa-kd", "jec-qa-ca", "gaokao-chinese", "gaokao-geography", "gaokao-history",
"gaokao-biology", "gaokao-chemistry", "gaokao-physics", "gaokao-mathqa"]
english_cloze_datasets = ["math"]
chinese_cloze_datasets = ["gaokao-mathcloze"]
multi_choice_datasets = ["jec-qa-kd", "jec-qa-ca", "gaokao-physics"]
math_output_datasets = {"gaokao-mathcloze", "math"}
def convert_zero_shot(line, dataset_name):
try:
passage = line["passage"] if line["passage"] is not None else ""
if dataset_name in english_qa_datasets:
option_string = "ABCDEFG"
count = len(line["options"])
if count == 1:
count = 5
return passage + "Q: " + line["question"] + " " \
+ "Answer Choices: " + " ".join(line["options"]) + "\n" + \
"A: Among A through {}, the answer is".format(option_string[count - 1])
elif dataset_name in chinese_qa_datasets:
option_string = "ABCDEFG"
count = len(line["options"])
if count == 1:
count = 4
return passage + "问题:" + line["question"] + " " \
+ "选项:" + " ".join(line["options"]) + "\n" + \
"答案:从A到{}, 我们应选择".format(option_string[count - 1])
elif dataset_name in english_cloze_datasets:
return passage + "Q: " + line["question"] + "\n" \
"A: The answer is"
elif dataset_name in chinese_cloze_datasets:
return passage + "问题:" + line["question"] + "\n" \
"答案:"
except NameError:
print("Dataset not defined.")
prefix = "该问题为单选题,所有选项中必有一个正确答案,且只有一个正确答案。\n"
def convert_zero_shot_CoT_stage1(line, dataset_name):
try:
passage = line["passage"] if line["passage"] is not None else ""
if dataset_name in english_qa_datasets:
return passage + "Q: " + line["question"] + " " \
+ "Answer Choices: " + " ".join(line["options"]) + "\n" + \
"Let's think step by step."
elif dataset_name in chinese_qa_datasets:
option_string = "ABCDEFG"
count = len(line["options"])
if count == 1:
count = 4
return passage + "问题:" + line["question"] + " " \
+ "选项:" + " ".join(line["options"]) + "\n" + \
"从A到{}, 我们应选择什么?让我们逐步思考:".format(option_string[count - 1])
elif dataset_name in english_cloze_datasets:
return passage + "Q: " + line["question"] + "\n" \
"A: Let's think step by step."
elif dataset_name in chinese_cloze_datasets:
return passage + "问题:" + line["question"] + "\n" \
"答案:让我们逐步思考:"
except NameError:
print("Dataset not defined.")
# process few-shot raw_prompts
def combine_prompt(prompt_path, dataset_name, load_explanation=True, chat_mode=False):
skip_passage = False
if dataset_name == 'sat-en-without-passage':
skip_passage = True
dataset_name = "sat-en"
demostrations = []
# read the prompts by context and explanation
context_row = [0, 1, 3, 5, 7, 9]
explanation_row = [0, 2, 4, 6, 8, 10]
raw_prompts_context = pd.read_csv(prompt_path, header=0, skiprows=lambda x: x not in context_row,
keep_default_na=False)
raw_prompts_explanation = pd.read_csv(prompt_path, header=0, skiprows=lambda x: x not in explanation_row,
keep_default_na=False).replace(r'\n\n', '\n', regex=True)
contexts = []
for line in list(raw_prompts_context[dataset_name]):
if line:
# print(line)
contexts.append(ast.literal_eval(line))
explanations = [exp for exp in raw_prompts_explanation[dataset_name] if exp]
for idx, (con, exp) in enumerate(zip(contexts, explanations)):
passage = con["passage"] if con["passage"] is not None and not skip_passage else ""
question = con["question"]
options = con["options"] if con["options"] is not None else ""
label = con["label"] if con["label"] is not None else ""
answer = con["answer"] if "answer" in con and con["answer"] is not None else ""
if dataset_name in english_qa_datasets:
question_input = "Problem {}. ".format(idx + 1) + passage + " " + question + "\n" \
+ "Choose from the following options: " + " ".join(options) + "\n"
question_output = (("Explanation for Problem {}: ".format(idx + 1) + exp + "\n") if load_explanation else "") \
+ "The answer is therefore {}".format(label)
elif dataset_name in chinese_qa_datasets:
question_input = "问题 {}. ".format(idx + 1) + passage + " " + question + "\n" \
+ "从以下选项中选择: " + " ".join(options) + "\n"
question_output = (("问题 {}的解析: ".format(idx + 1) + exp + "\n") if load_explanation else "") \
+ "答案是 {}".format(label)
elif dataset_name in english_cloze_datasets:
question_input = "Problem {}. ".format(idx + 1) + question + "\n"
question_output = (("Explanation for Problem {}: ".format(idx + 1) + exp + "\n") if load_explanation else "") \
+ "The answer is therefore {}".format(answer)
elif dataset_name in chinese_cloze_datasets:
question_input = "问题 {}. ".format(idx + 1) + question + "\n"
question_output = (("问题 {}的解析: ".format(idx + 1) + exp + "\n") if load_explanation else "") \
+ "答案是 {}".format(answer)
else:
raise ValueError(f"During loading few-sot examples, found unknown dataset: {dataset_name}")
if chat_mode:
demostrations.append((question_input, question_output))
else:
demostrations.append(question_input + question_output + '\n')
return demostrations
# cut prompt if reach max token length
def concat_prompt(demos, dataset_name, max_tokens, end_of_example="\n", verbose=False):
demostration_en = "Here are the answers for the problems in the exam.\n"
demostration_zh = "以下是考试中各个问题的答案。\n"
for i in range(len(demos)):
# print(len(enc.encode(demostration_en)), len(enc.encode(demostration_zh)))
if dataset_name in english_qa_datasets:
demostration_en = demostration_en + demos[i] + end_of_example
elif dataset_name in chinese_qa_datasets:
demostration_zh = demostration_zh + demos[i] + end_of_example
elif dataset_name in english_cloze_datasets:
demostration_en = demostration_en + demos[i] + end_of_example
elif dataset_name in chinese_cloze_datasets:
demostration_zh = demostration_zh + demos[i] + end_of_example
# break if reach max token limit
if len(enc.encode(demostration_en)) < max_tokens and len(enc.encode(demostration_zh)) < max_tokens:
output = demostration_en if len(demostration_en) > len(demostration_zh) else demostration_zh
prompt_num = i + 1
else:
break
if verbose:
print("max_tokens set as ", max_tokens, "actual_tokens is", len(enc.encode(output)), "num_shot is", prompt_num)
return output, prompt_num
def concat_prompt_chat_mode(demos, dataset_name, max_tokens, end_of_example="\n", verbose=False):
answers = []
sentences = ""
for i in range(len(demos)):
answers += [
{"role": "user", "content": demos[i][0]},
{"role": "assistant", "content": demos[i][1]},
]
sentences += json.dumps(answers[-1])
# break if reach max token limit
if len(enc.encode(sentences)) > max_tokens:
answers.pop()
answers.pop()
break
if verbose:
print("max_tokens set as ", max_tokens, "actual_tokens is", len(enc.encode(sentences)), "num_shot is", len(answers)//2)
return answers, len(answers)//2
def convert_few_shot(line, dataset_name, demo, n_shot, chat_mode=False):
passage = line["passage"] if line["passage"] is not None else ""
question = line["question"]
options = line["options"] if line["options"] is not None else ""
if dataset_name in english_qa_datasets:
question_input = "Problem {}. ".format(n_shot + 1) + passage + " " + question + "\n" \
+ "Choose from the following options: " + " ".join(options) + "\n"
# + "Explanation for Problem {}: ".format(n_shot + 1)
if dataset_name in chinese_qa_datasets:
question_input = "问题 {}. ".format(n_shot + 1) + passage + " " + question + "\n" \
+ "从以下选项中选择: " + " ".join(options) + "\n"
# + "问题 {}的解析: ".format(n_shot + 1)
if dataset_name in english_cloze_datasets:
question_input = "Problem {}. ".format(n_shot + 1) + question + "\n"
# + "Explanation for Problem {}: ".format(n_shot + 1)
if dataset_name in chinese_cloze_datasets:
question_input = "问题 {}. ".format(n_shot + 1) + question + "\n"
# + "问题 {}的解析: ".format(n_shot + 1)
if chat_mode:
return demo + [
{"role": "user", "content": question_input},
]
else:
return demo + question_input
def load_dataset(dataset_name, setting_name, parent_path, prompt_path=None, max_tokens=None, end_of_example="\n",
chat_mode=False, verbose=False):
test_path = os.path.join(parent_path, dataset_name + ".jsonl")
loaded_jsonl = read_jsonl(test_path)
processed = []
if setting_name == "few-shot-CoT" or setting_name == "few-shot":
# process demo once if it is few-shot-CoT
processed_demos = combine_prompt(prompt_path, dataset_name, load_explanation=setting_name=='few-shot-CoT', chat_mode=chat_mode)
if chat_mode:
chosen_prompt, n_shot = concat_prompt_chat_mode(
processed_demos, dataset_name, max_tokens, end_of_example, verbose=verbose)
else:
chosen_prompt, n_shot = concat_prompt(
processed_demos, dataset_name, max_tokens, end_of_example, verbose=verbose)
if verbose:
loaded_jsonl = tqdm(loaded_jsonl)
for meta_idx, line in enumerate(loaded_jsonl):
if setting_name == "zero-shot":
ctxt = convert_zero_shot(line, dataset_name)
elif setting_name == "zero-shot-CoT":
ctxt = convert_zero_shot_CoT_stage1(line, dataset_name)
elif setting_name == "few-shot-CoT" or setting_name == "few-shot":
ctxt = convert_few_shot(line, dataset_name, chosen_prompt, n_shot, chat_mode)
try:
new_instance = ChatGPTSchema(context=ctxt, metadata=meta_idx)
processed.append(new_instance.to_dict())
except NameError:
print("Dataset not defined.")
return processed
def generate_second_stage_input(dataset_name, input_list, output_list, with_format_prompt=False):
try:
english_format_prompt = "Based on the previous results, your task is to extract the final answer and provide the output enclosed in brackets【】, such as 【0】 or 【A】."
chinese_format_prompt = "根据以上内容,你的任务是把最终的答案提取出来并填在【】中,例如【0】或者【A】。"
if dataset_name in english_qa_datasets:
prompt_suffix = "Therefore, among A through E, the answer is"
if with_format_prompt:
prompt_suffix = english_format_prompt + prompt_suffix
elif dataset_name in chinese_qa_datasets:
prompt_suffix = "因此,从A到D, 我们应选择"
if with_format_prompt:
prompt_suffix = chinese_format_prompt + prompt_suffix
elif dataset_name in english_cloze_datasets:
prompt_suffix = "Therefore, the answer is"
if with_format_prompt:
prompt_suffix = english_format_prompt + prompt_suffix
elif dataset_name in chinese_cloze_datasets:
prompt_suffix = "因此,答案是"
if with_format_prompt:
prompt_suffix = chinese_format_prompt + prompt_suffix
except NameError:
print("Dataset not defined.")
processed = []
for i in range(len(input_list)):
ctxt = "{0}\n{1}\n{2}".format(input_list[i]["context"], extract_answer(output_list[i]), prompt_suffix)
new_instance = ChatGPTSchema(context=ctxt, metadata=input_list[i]["metadata"])
processed.append(new_instance.to_dict())
return processed
def load_dataset_as_result_schema(dataset_name, parent_path):
test_path = os.path.join(parent_path, dataset_name + ".jsonl")
loaded_jsonl = read_jsonl(test_path)
processed = []
for i, line in enumerate(loaded_jsonl):
problem_input = convert_zero_shot(line, dataset_name)
processed.append(ResultsForHumanSchema(
index=i,
problem_input=problem_input,
label=line["label"] if line["label"] else line["answer"],
))
return processed
# define the encoder
enc = tiktoken.get_encoding("cl100k_base")
enc = tiktoken.encoding_for_model("gpt-4")
if __name__ == "__main__":
# set variables
parent_dir = "../../data/V1_1/"
raw_prompt_path = "../data/few_shot_prompts.csv"
# set dataset name to process
setting_name = "few-shot-CoT" # setting_name can be chosen from ["zero-shot", "zero-shot-CoT", "few-shot-CoT"]
data_name = "jec-qa-kd"
save_dir = "../../experiment_input/{}/".format(setting_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
processed_data = load_dataset(data_name, setting_name, parent_dir, prompt_path=raw_prompt_path, max_tokens=2048)
save_jsonl(processed_data, os.path.join(save_dir, "{}.jsonl".format(data_name)))