File size: 9,106 Bytes
4d7378e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
'''
Adapted from https://github.com/lupantech/ScienceQA
'''
from dataclasses import dataclass
from typing import List, Optional
def get_question_text(problem):
question = problem['question']
return question
def get_context_text(problem, use_caption):
txt_context = problem['hint']
img_context = problem['caption'] if use_caption else ""
context = " ".join([txt_context, img_context]).strip()
if context == "":
context = "N/A"
return context
def get_choice_text(probelm, options):
choices = probelm['choices']
choice_list = []
for i, c in enumerate(choices):
choice_list.append("({}) {}".format(options[i], c))
choice_txt = " ".join(choice_list)
#print(choice_txt)
return choice_txt
def get_origin_answer(problem, options):
return problem['choices'][problem['answer']]
def get_answer(problem, options):
return options[problem['answer']]
def get_lecture_text(problem):
# \\n: GPT-3 can generate the lecture with more tokens.
lecture = problem['lecture'].replace("\n", "\\n")
return lecture
def get_solution_text(problem):
# \\n: GPT-3 can generate the solution with more tokens
solution = problem['solution'].replace("\n", "\\n")
return solution
def create_one_example(format, question, context, choice, answer, lecture, solution, test_example=True, WithOutput = False, curr_le_data=None):
input_format, output_format = format.split("-")
## Inputs
if input_format == "CQM":
input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
elif input_format == "QCM":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
elif input_format == "QM":
input = f"Question: {question}\nOptions: {choice}\n"
elif input_format == "QC":
input = f"Question: {question}\nContext: {context}\n"
elif input_format == "QCMG":
if curr_le_data is not None:
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n{curr_le_data}\n"
else:
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nSolution: {lecture} {solution}\n"
elif input_format == "CQMG":
if curr_le_data is not None:
input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n{curr_le_data}\n"
else:
input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\nSolution: {lecture} {solution}\n"
# upper bound experiment
elif input_format == "QCML":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
elif input_format == "QCME":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
elif input_format == "QCMLE":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
elif input_format == "QCLM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
elif input_format == "QCEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
elif input_format == "QCLEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
elif input_format == "QCMA":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nAnswer: The answer is {answer}.\n"
elif input_format == "QCA":
input = f"Question: {question}\nContext: {context}\nAnswer: The answer is {answer}. \nBECAUSE:"
# Outputs
if test_example:
if output_format == 'A':
output = "Answer:"
elif output_format == 'E':
output = "Solution:"
else:
output = "Solution:"
elif output_format == 'A':
output = f"Answer: The answer is {answer}."
elif output_format == 'AL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
elif output_format == 'AE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
elif output_format == 'ALE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
elif output_format == 'AEL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
elif output_format == 'LA':
output = f"Answer: {lecture} The answer is {answer}."
elif output_format == 'EA':
output = f"Answer: {solution} The answer is {answer}."
elif output_format == 'LEA':
output = f"Answer: {lecture} {solution} The answer is {answer}."
elif output_format == 'ELA':
output = f"Answer: {solution} {lecture} The answer is {answer}."
elif output_format == 'LE':
output = f"Solution: {lecture} {solution}."
elif output_format == 'E':
output = f"Solution: {solution}"
if WithOutput:
if output.endswith("BECAUSE:"):
output = output.replace("BECAUSE:", "").strip()
if output_format == 'E':
text = input + f'Solution:'
elif output_format == 'A':
text = input + f'Answer:'
else:
text = input + f'Solution:'
text = text.replace(" ", " ").strip()
output = output.replace(" ", " ").strip()
return text, output
text = input + output
text = text.replace(" ", " ").strip()
if text.endswith("BECAUSE:"):
text = text.replace("BECAUSE:", "").strip()
return text
def build_prompt(problems, shot_qids, test_qid, args):
examples = []
# n-shot training examples
for qid in shot_qids:
question = get_question_text(problems[qid])
context = get_context_text(problems[qid], args.use_caption)
choice = get_choice_text(problems[qid], args.options)
answer = get_answer(problems[qid], args.options)
lecture = get_lecture_text(problems[qid])
solution = get_solution_text(problems[qid])
train_example = create_one_example(args.prompt_format,
question,
context,
choice,
answer,
lecture,
solution,
test_example=False)
examples.append(train_example)
# test example
question = get_question_text(problems[test_qid])
context = get_context_text(problems[test_qid], args.use_caption)
choice = get_choice_text(problems[test_qid], args.options)
answer = get_answer(problems[test_qid], args.options)
lecture = get_lecture_text(problems[test_qid])
solution = get_solution_text(problems[test_qid])
test_example = create_one_example(args.prompt_format,
question,
context,
choice,
answer,
lecture,
solution,
test_example=True)
examples.append(test_example)
# create the prompt input
prompt_input = '\n\n'.join(examples)
return prompt_input
def build_train_pair(problems, test_qid, args, curr_le_data=None):
examples = []
# test example
question = get_question_text(problems[test_qid])
context = get_context_text(problems[test_qid], args.use_caption)
choice = get_choice_text(problems[test_qid], args.options)
lecture = get_lecture_text(problems[test_qid])
solution = get_solution_text(problems[test_qid])
# answer_text = get_origin_answer(problems[test_qid], args.options)
answer_option = get_answer(problems[test_qid], args.options)
answer = "(" + answer_option + ")"
test_example, target = create_one_example(args.prompt_format,
question,
context,
choice,
answer,
lecture,
solution,
test_example=False,WithOutput = True, curr_le_data=curr_le_data)
examples.append(test_example)
target = target.replace("Answer:", "").strip()
# create the prompt input
prompt_input = '\n\n'.join(examples)
return prompt_input, target
@dataclass(frozen=True)
class InputFeatures:
"""
A single set of features of data.
Property names are the same names as the corresponding inputs to a model.
"""
input_ids: List[List[int]]
attention_mask: Optional[List[List[int]]]
token_type_ids: Optional[List[List[int]]]
le_input_ids: List[List[int]]
le_attention_mask: Optional[List[List[int]]]
le_token_type_ids: Optional[List[List[int]]]
label: Optional[int] |