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import hashlib
import re
import datasets
def hash_string(string: str) -> str:
return hashlib.sha256(string.encode("utf-8")).hexdigest()
def process_arc(dataset: datasets.Dataset) -> datasets.Dataset:
def _subprocess(doc):
long_prompt = ""
for shot in range(1, 26):
question = doc[f"arc_question_shot_{shot}"]
doc.pop(f"arc_question_shot_{shot}")
answer_lab = doc[f"arc_answerKey_shot_{shot}"]
doc.pop(f"arc_answerKey_shot_{shot}")
answer_idx = doc[f"arc_choices_shot_{shot}"]["label"].index(answer_lab)
answer = doc[f"arc_choices_shot_{shot}"]["text"][answer_idx]
doc.pop(f"arc_choices_shot_{shot}")
doc.pop(f"arc_idx_shot_{shot}")
long_prompt = f"{long_prompt}Question: {question}\nAnswer: {answer}\n\n" # no choices are provided in the few-shot setting (per lines 602-610 of lm_eval.api.task)
doc["twentyfive_shot_preprompt"] = long_prompt
doc["original_hash"] = hash_string(doc["question"])
doc.pop("alltwentyfiveshot_longprompt")
return doc
return dataset.map(_subprocess)
def process_gsm8k(dataset: datasets.Dataset) -> datasets.Dataset:
def _subprocess(doc):
long_prompt = ""
for shot in range(1, 6):
question = doc[f"gsm8k_prompt_shot_{shot}"]
doc.pop(f"gsm8k_prompt_shot_{shot}")
answer = doc[f"gsm8k_answer_shot_{shot}"]
doc.pop(f"gsm8k_answer_shot_{shot}")
doc.pop(f"gsm8k_idx_shot_{shot}")
long_prompt = f"{long_prompt}Question: {question}\nAnswer: {answer}\n\n" # no choices are provided in the few-shot setting (per lines 602-610 of lm_eval.api.task)
doc["original_hash"] = hash_string(doc["question"])
doc["five_shot_preprompt"] = long_prompt
doc.pop("allfiveshot_longprompt")
return doc
return dataset.map(_subprocess)
def process_hellaswag(dataset: datasets.Dataset) -> datasets.Dataset:
def process_txt(text): # mirrored from hellaswag task
text = text.strip()
# NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
text = text.replace(" [title]", ". ")
text = re.sub("\\[.*?\\]", "", text)
text = text.replace(" ", " ")
return text
def _preprocess(doc):
ctx = doc["ctx_a"] + " " + doc["ctx_b"].capitalize()
doc.pop("ctx_a")
doc.pop("ctx_b")
doc.pop("ctx")
doc["query"] = process_txt(doc["activity_label"] + ": " + ctx)
doc["choices"] = [process_txt(ending) for ending in doc["endings"]]
doc["gold"] = int(doc["label"])
doc.pop("activity_label")
doc.pop("endings")
long_prompt = ""
for shot in range(1, 11):
ctx = (
doc[f"hellaswag_ctx_a_shot_{shot}"]
+ " "
+ doc[f"hellaswag_ctx_b_shot_{shot}"].capitalize()
)
doc.pop(f"hellaswag_ctx_a_shot_{shot}")
doc.pop(f"hellaswag_ctx_b_shot_{shot}")
doc.pop(f"hellaswag_ctx_shot_{shot}")
question = process_txt(
doc[f"hellaswag_activity_labels_shot_{shot}"] + ": " + ctx
)
ending = process_txt(
doc[f"hellaswag_endings_shot_{shot}"][
int(doc[f"hellaswag_label_shot_{shot}"])
]
)
doc.pop(f"hellaswag_activity_labels_shot_{shot}")
doc.pop(f"hellaswag_endings_shot_{shot}")
doc.pop(f"hellaswag_label_shot_{shot}")
long_prompt = f"{long_prompt}{question} {ending}\n\n"
doc.pop(f"hellaswag_ind_shot_{shot}")
doc.pop(f"hellaswag_source_id_shot_{shot}")
doc.pop(f"hellaswag_split_shot_{shot}")
doc.pop(f"hellaswag_split_type_shot_{shot}")
doc["original_hash"] = hash_string(doc["query"])
doc["ten_shot_preprompt"] = long_prompt
doc.pop("alltenshot_longprompt")
return doc
return dataset.map(_preprocess)
def process_mmlu(dataset: datasets.Dataset) -> datasets.Dataset:
def _subprocess(doc):
choices = ["A", "B", "C", "D"]
long_prompt = f"The following are multiple choice questions (with answers) about {' '.join(doc['subject'].split('_'))}.\n\n"
for shot in range(1, 6):
question = doc[f"mmlu_question_shot_{shot}"].strip()
doc.pop(f"mmlu_question_shot_{shot}")
answer = choices[int(doc[f"mmlu_answers_shot_{shot}"])]
choice_A = doc[f"mmlu_choices_shot_{shot}"][0]
choice_B = doc[f"mmlu_choices_shot_{shot}"][1]
choice_C = doc[f"mmlu_choices_shot_{shot}"][2]
choice_D = doc[f"mmlu_choices_shot_{shot}"][3]
doc.pop(f"mmlu_choices_shot_{shot}")
doc.pop(f"mmlu_answers_shot_{shot}")
doc.pop(f"mmlu_ind_shot_{shot}")
long_prompt = f"{long_prompt}{question}\nA. {choice_A}\nB. {choice_B}\nC. {choice_C}\nD. {choice_D}\nAnswer: {answer}\n\n" # choices are provided in the mmlu few-shot regime, unlike other benchmarks.
doc["original_hash"] = hash_string(doc["question"])
doc["five_shot_preprompt"] = long_prompt
doc.pop("allfiveshot_longprompt")
return doc
return dataset.map(_subprocess)
def process_truthfulqa(dataset: datasets.Dataset) -> datasets.Dataset:
def _subprocess(doc):
doc["original_hash"] = hash_string(doc["question"])
return doc
return dataset.map(_subprocess)
def process_winogrande(dataset: datasets.Dataset) -> datasets.Dataset:
def _subprocess(doc):
long_prompt = ""
for shot in range(1, 6):
if doc[f"winogrande_answer_shot_{shot}"] == "1":
answer = doc[f"winogrande_option1_shot_{shot}"]
elif doc[f"winogrande_answer_shot_{shot}"] == "2":
answer = doc[f"winogrande_option2_shot_{shot}"]
else:
raise ValueError("Answer not recognised.")
question = doc[f"winogrande_prompt_shot_{shot}"].replace("_", answer)
doc.pop(f"winogrande_prompt_shot_{shot}")
doc.pop(f"winogrande_answer_shot_{shot}")
doc.pop(f"winogrande_idx_shot_{shot}")
doc.pop(f"winogrande_option1_shot_{shot}")
doc.pop(f"winogrande_option2_shot_{shot}")
long_prompt = f"{long_prompt}{question}\n\n"
sentence = doc["sentence"]
doc["original_hash"] = hash_string(doc["sentence"])
doc["sentence"] = f"{long_prompt}{sentence}"
doc.pop("allfiveshot_longprompt")
return doc
return dataset.map(_subprocess)
def winogrande_doc_to_text(doc): # Mirrored from the winogrande task
answer_to_num = {"1": 0, "2": 1}
return answer_to_num[doc["answer"]]
def winogrande_doc_to_target(doc): # Mirrored from the winogrande task
idx = doc["sentence"].index("_") + 1
return doc["sentence"][idx:].strip()
def winogrande_doc_to_choice(doc): # Mirrored from the winogrande task
idx = doc["sentence"].index("_")
options = [doc["option1"], doc["option2"]]
return [doc["sentence"][:idx] + opt for opt in options]
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