danidarko/docs · Datasets at Hugging Face

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is_following_list = []
for (index, instruction_id) in enumerate(instruction_list):
instruction_cls = instructions_registry.INSTRUCTION_DICT[instruction_id]
instruction = instruction_cls(instruction_id)
kwargs = {k: v for (k, v) in inp.kwargs[index].items() if v}
instruction.build_description(**kwargs)
args = instruction.get_instruction_args()
if args and 'prompt' in args:
instruction.build_description(prompt=inp.prompt)
if response.strip() and instruction.check_following(response):
is_following_list.append(True)
else:
is_following_list.append(False)
return OutputExample(instruction_id_list=inp.instruction_id_list, prompt=inp.prompt, response=response, follow_all_instructions=all(is_following_list), follow_instruction_list=is_following_list)

def test_instruction_following_loose(inp, response):
r = response.split('\n')
response_remove_first = '\n'.join(r[1:]).strip()
response_remove_last = '\n'.join(r[:-1]).strip()
response_remove_both = '\n'.join(r[1:-1]).strip()
revised_response = response.replace('*', '')
revised_response_remove_first = response_remove_first.replace('*', '')
revised_response_remove_last = response_remove_last.replace('*', '')
revised_response_remove_both = response_remove_both.replace('*', '')
all_responses = [response, revised_response, response_remove_first, response_remove_last, response_remove_both, revised_response_remove_first, revised_response_remove_last, revised_response_remove_both]
instruction_list = inp.instruction_id_list
is_following_list = []
for (index, instruction_id) in enumerate(instruction_list):
instruction_cls = instructions_registry.INSTRUCTION_DICT[instruction_id]
instruction = instruction_cls(instruction_id)
kwargs = {k: v for (k, v) in inp.kwargs[index].items() if v}
instruction.build_description(**kwargs)
args = instruction.get_instruction_args()
if args and 'prompt' in args:
instruction.build_description(prompt=inp.prompt)
is_following = False
for r in all_responses:
if r.strip() and instruction.check_following(r):
is_following = True
break
is_following_list.append(is_following)
return OutputExample(instruction_id_list=inp.instruction_id_list, prompt=inp.prompt, response=response, follow_all_instructions=all(is_following_list), follow_instruction_list=is_following_list)

def process_results(doc, results):
eval_logger.warning('This task is meant for chat-finetuned models, and may not give meaningful results for models other than `openai` or `anthropic` if `doc_to_text` in its YAML is not wrapped in the appropriate chat template string. This warning will be removed when chat templating support is added natively to local models')
inp = InputExample(key=doc['key'], instruction_id_list=doc['instruction_id_list'], prompt=doc['prompt'], kwargs=doc['kwargs'])
response = results[0]
out_strict = test_instruction_following_strict(inp, response)
out_loose = test_instruction_following_loose(inp, response)
return {'prompt_level_strict_acc': out_strict.follow_all_instructions, 'inst_level_strict_acc': out_strict.follow_instruction_list, 'prompt_level_loose_acc': out_loose.follow_all_instructions, 'inst_level_loose_acc': out_loose.follow_instruction_list}

def agg_inst_level_acc(items):
flat_items = [item for sublist in items for item in sublist]
inst_level_acc = sum(flat_items) / len(flat_items)
return inst_level_acc

# File: lm-evaluation-harness-main/lm_eval/tasks/kobest/utils.py
from datasets import Dataset
from sklearn.metrics import f1_score

def copa_doc_to_text(doc: dict) -> str:
connector = {'원인': ' 왜냐하면', '결과': ' 그래서'}[doc['question'].strip()]
return f"{doc['premise']} {connector}"

def copa_doc_to_target(doc: dict) -> str:
correct_choice = doc['alternative_1'] if doc['label'] == 0 else doc['alternative_2']
return f'{correct_choice}'

def copa_doc_to_choice(doc: dict) -> list:
return [f"{doc['alternative_1']}", f"{doc['alternative_2']}"]

def sentineg_doc_to_text(doc: dict):
return f"문장: {doc['sentence']} 긍부정:"

def wic_doc_to_text(doc: dict) -> str:
return f"문장1: {doc['context_1']} 문장2: {doc['context_2']} 두 문장에서 {doc['word']}가 같은 뜻으로 쓰였나?"

def hellaswag_process_doc(doc: Dataset) -> Dataset:

def preprocessor(dataset):
return {'query': f"문장: {dataset['context']}", 'choices': [dataset['ending_1'], dataset['ending_2'], dataset['ending_3'], dataset['ending_4']], 'gold': int(dataset['label'])}
return doc.map(preprocessor)

def macro_f1_score(items):
unzipped_list = list(zip(*items))
golds = unzipped_list[0]
preds = unzipped_list[1]
fscore = f1_score(golds, preds, average='macro')
return fscore

# File: lm-evaluation-harness-main/lm_eval/tasks/leaderboard/gpqa/utils.py
import random
import re
import datasets

def preprocess(text):
if text is None:
return ' '
text = text.strip()
text = text.replace(' [title]', '. ')

is_following_list = []

for (index, instruction_id) in enumerate(instruction_list):

instruction_cls = instructions_registry.INSTRUCTION_DICT[instruction_id]

instruction = instruction_cls(instruction_id)

kwargs = {k: v for (k, v) in inp.kwargs[index].items() if v}

instruction.build_description(**kwargs)

args = instruction.get_instruction_args()

if args and 'prompt' in args:

instruction.build_description(prompt=inp.prompt)

if response.strip() and instruction.check_following(response):

is_following_list.append(True)

else:

is_following_list.append(False)

return OutputExample(instruction_id_list=inp.instruction_id_list, prompt=inp.prompt, response=response, follow_all_instructions=all(is_following_list), follow_instruction_list=is_following_list)

def test_instruction_following_loose(inp, response):

r = response.split('\n')

response_remove_first = '\n'.join(r[1:]).strip()

response_remove_last = '\n'.join(r[:-1]).strip()

response_remove_both = '\n'.join(r[1:-1]).strip()

revised_response = response.replace('*', '')

revised_response_remove_first = response_remove_first.replace('*', '')

revised_response_remove_last = response_remove_last.replace('*', '')

revised_response_remove_both = response_remove_both.replace('*', '')

all_responses = [response, revised_response, response_remove_first, response_remove_last, response_remove_both, revised_response_remove_first, revised_response_remove_last, revised_response_remove_both]

instruction_list = inp.instruction_id_list

is_following_list = []

for (index, instruction_id) in enumerate(instruction_list):

instruction_cls = instructions_registry.INSTRUCTION_DICT[instruction_id]

instruction = instruction_cls(instruction_id)

kwargs = {k: v for (k, v) in inp.kwargs[index].items() if v}

instruction.build_description(**kwargs)

args = instruction.get_instruction_args()

if args and 'prompt' in args:

instruction.build_description(prompt=inp.prompt)

is_following = False

for r in all_responses:

if r.strip() and instruction.check_following(r):

is_following = True

break

is_following_list.append(is_following)

return OutputExample(instruction_id_list=inp.instruction_id_list, prompt=inp.prompt, response=response, follow_all_instructions=all(is_following_list), follow_instruction_list=is_following_list)

def process_results(doc, results):

eval_logger.warning('This task is meant for chat-finetuned models, and may not give meaningful results for models other than `openai` or `anthropic` if `doc_to_text` in its YAML is not wrapped in the appropriate chat template string. This warning will be removed when chat templating support is added natively to local models')

inp = InputExample(key=doc['key'], instruction_id_list=doc['instruction_id_list'], prompt=doc['prompt'], kwargs=doc['kwargs'])

response = results[0]

out_strict = test_instruction_following_strict(inp, response)

out_loose = test_instruction_following_loose(inp, response)

return {'prompt_level_strict_acc': out_strict.follow_all_instructions, 'inst_level_strict_acc': out_strict.follow_instruction_list, 'prompt_level_loose_acc': out_loose.follow_all_instructions, 'inst_level_loose_acc': out_loose.follow_instruction_list}

def agg_inst_level_acc(items):

flat_items = [item for sublist in items for item in sublist]

inst_level_acc = sum(flat_items) / len(flat_items)

return inst_level_acc

# File: lm-evaluation-harness-main/lm_eval/tasks/kobest/utils.py

from datasets import Dataset

from sklearn.metrics import f1_score

def copa_doc_to_text(doc: dict) -> str:

connector = {'원인': ' 왜냐하면', '결과': ' 그래서'}[doc['question'].strip()]

return f"{doc['premise']} {connector}"

def copa_doc_to_target(doc: dict) -> str:

correct_choice = doc['alternative_1'] if doc['label'] == 0 else doc['alternative_2']

return f'{correct_choice}'

def copa_doc_to_choice(doc: dict) -> list:

return [f"{doc['alternative_1']}", f"{doc['alternative_2']}"]

def sentineg_doc_to_text(doc: dict):

return f"문장: {doc['sentence']} 긍부정:"

def wic_doc_to_text(doc: dict) -> str:

return f"문장1: {doc['context_1']} 문장2: {doc['context_2']} 두 문장에서 {doc['word']}가 같은 뜻으로 쓰였나?"

def hellaswag_process_doc(doc: Dataset) -> Dataset:

def preprocessor(dataset):

return {'query': f"문장: {dataset['context']}", 'choices': [dataset['ending_1'], dataset['ending_2'], dataset['ending_3'], dataset['ending_4']], 'gold': int(dataset['label'])}

return doc.map(preprocessor)

def macro_f1_score(items):

unzipped_list = list(zip(*items))

golds = unzipped_list[0]

preds = unzipped_list[1]

fscore = f1_score(golds, preds, average='macro')

return fscore

# File: lm-evaluation-harness-main/lm_eval/tasks/leaderboard/gpqa/utils.py

import random

import re

import datasets

def preprocess(text):

if text is None:

return ' '

text = text.strip()

text = text.replace(' [title]', '. ')