danidarko/docs · Datasets at Hugging Face

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yaml_template = 'direct_yaml'
if mode == 'direct-native':
ANSWER = LANGUAGES[lang]['DIRECT']
QUESTION = LANGUAGES[lang]['QUESTION']
REGEX = None
task_name = f'afrimgsm_direct_native_{lang}'
yaml_template = 'direct_native_yaml'
elif mode == 'native-cot':
ANSWER = LANGUAGES[lang]['ANSWER']
REGEX = LANGUAGES[lang]['REGEX']
QUESTION = LANGUAGES[lang]['QUESTION']
task_name = f'afrimgsm_native_cot_{lang}'
filter_list = add_regex_pattern(REGEX)
DELIMITER = '' if lang in ['zh', 'ja'] else None
elif mode == 'en-cot':
ANSWER = LANGUAGES['eng']['ANSWER']
REGEX = LANGUAGES['eng']['REGEX']
QUESTION = LANGUAGES['eng']['QUESTION']
task_name = f'afrimgsm_en_cot_{lang}'
elif mode == 'translate-direct':
ANSWER = LANGUAGES['eng']['DIRECT']
QUESTION = LANGUAGES['eng']['QUESTION']
REGEX = None
task_name = f'afrimgsm_translate_direct_{lang}'
yaml_template = 'translate_direct_yaml'
file_name = f'{task_name}.yaml'
ANSWER_TO_SKIP = len(LANGUAGES[lang]['ANSWER']) + 1
with open(f'{output_dir}/{file_name}', 'w' if overwrite else 'x', encoding='utf8') as f:
f.write('# Generated by utils.py\n')
yaml.dump({'include': yaml_template, 'dataset_name': lang, 'task': f'{task_name}', 'doc_to_text': f'{{% if answer is not none %}}{{{{question+"\\n{ANSWER}"}}}}{{% else %}}{{{{"{QUESTION} "+question+"\\n{ANSWER}"}}}}{{% endif %}}', 'doc_to_target': f'{{% if answer is not none %}}{{{{answer[{ANSWER_TO_SKIP}:]}}}}{{% else %}}{{{{answer_number\|string}}}}{{% endif %}}', filter_list, 'generation_kwargs': {'until': [QUESTION, '</s>', '<\|im_end\|>'], 'do_sample': False}, ({'target_delimiter': DELIMITER} if DELIMITER else {})}, f, allow_unicode=True, width=float('inf'))
except FileExistsError:
err.append(file_name)
if len(err) > 0:
raise FileExistsError(f"Files were not created because they already exist (use --overwrite flag): {', '.join(err)}")

def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument('--overwrite', default=False, action='store_true', help='Overwrite files if they already exist')
parser.add_argument('--output-dir', default='.', help='Directory to write yaml files to')
parser.add_argument('--mode', default='native-cot', choices=['direct', 'direct-native', 'native-cot', 'en-cot', 'translate-direct'], help='Mode of chain-of-thought')
args = parser.parse_args()
gen_lang_yamls(output_dir=args.output_dir, overwrite=args.overwrite, mode=args.mode)
if __name__ == '__main__':
main()

# File: lm-evaluation-harness-main/lm_eval/tasks/afrimmlu/direct/utils.py
from sklearn.metrics import f1_score

def doc_to_choice(doc):
choices = eval(doc['choices'])
return choices

def doc_to_text(doc):
output = 'You are a highly knowledgeable and intelligent artificial intelligence\n model answers multiple-choice questions about {subject}\n\n Question: {question}\n\n Choices:\n A: {choice1}\n B: {choice2}\n C: {choice3}\n D: {choice4}\n\n Answer: '
choices = eval(doc['choices'])
text = output.format(subject=doc['subject'], question=doc['question'], choice1=choices[0], choice2=choices[1], choice3=choices[2], choice4=choices[3])
return text

def weighted_f1_score(items):
unzipped_list = list(zip(*items))
golds = unzipped_list[0]
preds = unzipped_list[1]
fscore = f1_score(golds, preds, average='weighted')
return fscore

# File: lm-evaluation-harness-main/lm_eval/tasks/afrimmlu/translate/utils.py
from sklearn.metrics import f1_score

def doc_to_choice(doc):
choices = eval(doc['choices'])
return choices

def doc_to_text(doc):
output = "You are a highly knowledgeable and intelligent artificial intelligence\n model answers multiple-choice questions about '{subject}'\n\n Question: '''{question}'''\n\n Choices:\n A: ''{choice1}'''\n B: ''{choice2}'''\n C: ''{choice3}'''\n D: ''{choice4}'''\n\n Answer: "
choices = eval(doc['choices'])
text = output.format(subject=doc['subject'], question=doc['question'], choice1=choices[0], choice2=choices[1], choice3=choices[2], choice4=choices[3])
return text

def weighted_f1_score(items):
unzipped_list = list(zip(*items))
golds = unzipped_list[0]
preds = unzipped_list[1]
fscore = f1_score(golds, preds, average='weighted')
return fscore

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

def doc_to_choice(doc):
choices = eval(doc['choices'])
return choices

def doc_to_text(doc):
output = "You are a highly knowledgeable and intelligent artificial intelligence\n model answers multiple-choice questions about '{subject}'\n\n Question: '''{question}'''\n\n Choices:\n A: ''{choice1}'''\n B: ''{choice2}'''\n C: ''{choice3}'''\n D: ''{choice4}'''\n\n Answer: "
choices = eval(doc['choices'])
text = output.format(subject=doc['subject'], question=doc['question'], choice1=choices[0], choice2=choices[1], choice3=choices[2], choice4=choices[3])
return text

def weighted_f1_score(items):
unzipped_list = list(zip(*items))

yaml_template = 'direct_yaml'

if mode == 'direct-native':

ANSWER = LANGUAGES[lang]['DIRECT']

QUESTION = LANGUAGES[lang]['QUESTION']

REGEX = None

task_name = f'afrimgsm_direct_native_{lang}'

yaml_template = 'direct_native_yaml'

elif mode == 'native-cot':

ANSWER = LANGUAGES[lang]['ANSWER']

REGEX = LANGUAGES[lang]['REGEX']

QUESTION = LANGUAGES[lang]['QUESTION']

task_name = f'afrimgsm_native_cot_{lang}'

filter_list = add_regex_pattern(REGEX)

DELIMITER = '' if lang in ['zh', 'ja'] else None

elif mode == 'en-cot':

ANSWER = LANGUAGES['eng']['ANSWER']

REGEX = LANGUAGES['eng']['REGEX']

QUESTION = LANGUAGES['eng']['QUESTION']

task_name = f'afrimgsm_en_cot_{lang}'

elif mode == 'translate-direct':

ANSWER = LANGUAGES['eng']['DIRECT']

QUESTION = LANGUAGES['eng']['QUESTION']

REGEX = None

task_name = f'afrimgsm_translate_direct_{lang}'

yaml_template = 'translate_direct_yaml'

file_name = f'{task_name}.yaml'

ANSWER_TO_SKIP = len(LANGUAGES[lang]['ANSWER']) + 1

with open(f'{output_dir}/{file_name}', 'w' if overwrite else 'x', encoding='utf8') as f:

f.write('# Generated by utils.py\n')

yaml.dump({'include': yaml_template, 'dataset_name': lang, 'task': f'{task_name}', 'doc_to_text': f'{{% if answer is not none %}}{{{{question+"\\n{ANSWER}"}}}}{{% else %}}{{{{"{QUESTION} "+question+"\\n{ANSWER}"}}}}{{% endif %}}', 'doc_to_target': f'{{% if answer is not none %}}{{{{answer[{ANSWER_TO_SKIP}:]}}}}{{% else %}}{{{{answer_number|string}}}}{{% endif %}}', **filter_list, 'generation_kwargs': {'until': [QUESTION, '</s>', '<|im_end|>'], 'do_sample': False}, **({'target_delimiter': DELIMITER} if DELIMITER else {})}, f, allow_unicode=True, width=float('inf'))

except FileExistsError:

err.append(file_name)

if len(err) > 0:

raise FileExistsError(f"Files were not created because they already exist (use --overwrite flag): {', '.join(err)}")

def main() -> None:

parser = argparse.ArgumentParser()

parser.add_argument('--overwrite', default=False, action='store_true', help='Overwrite files if they already exist')

parser.add_argument('--output-dir', default='.', help='Directory to write yaml files to')

parser.add_argument('--mode', default='native-cot', choices=['direct', 'direct-native', 'native-cot', 'en-cot', 'translate-direct'], help='Mode of chain-of-thought')

args = parser.parse_args()

gen_lang_yamls(output_dir=args.output_dir, overwrite=args.overwrite, mode=args.mode)

if __name__ == '__main__':

main()

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

from sklearn.metrics import f1_score

def doc_to_choice(doc):

choices = eval(doc['choices'])

return choices

def doc_to_text(doc):

output = 'You are a highly knowledgeable and intelligent artificial intelligence\n model answers multiple-choice questions about {subject}\n\n Question: {question}\n\n Choices:\n A: {choice1}\n B: {choice2}\n C: {choice3}\n D: {choice4}\n\n Answer: '

choices = eval(doc['choices'])

text = output.format(subject=doc['subject'], question=doc['question'], choice1=choices[0], choice2=choices[1], choice3=choices[2], choice4=choices[3])

return text

def weighted_f1_score(items):

unzipped_list = list(zip(*items))

golds = unzipped_list[0]

preds = unzipped_list[1]

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

return fscore

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

from sklearn.metrics import f1_score

def doc_to_choice(doc):

choices = eval(doc['choices'])

return choices

def doc_to_text(doc):

output = "You are a highly knowledgeable and intelligent artificial intelligence\n model answers multiple-choice questions about '{subject}'\n\n Question: '''{question}'''\n\n Choices:\n A: ''{choice1}'''\n B: ''{choice2}'''\n C: ''{choice3}'''\n D: ''{choice4}'''\n\n Answer: "

choices = eval(doc['choices'])

text = output.format(subject=doc['subject'], question=doc['question'], choice1=choices[0], choice2=choices[1], choice3=choices[2], choice4=choices[3])

return text

def weighted_f1_score(items):

unzipped_list = list(zip(*items))

golds = unzipped_list[0]

preds = unzipped_list[1]

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

return fscore

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

from sklearn.metrics import f1_score

def doc_to_choice(doc):

choices = eval(doc['choices'])

return choices

def doc_to_text(doc):

output = "You are a highly knowledgeable and intelligent artificial intelligence\n model answers multiple-choice questions about '{subject}'\n\n Question: '''{question}'''\n\n Choices:\n A: ''{choice1}'''\n B: ''{choice2}'''\n C: ''{choice3}'''\n D: ''{choice4}'''\n\n Answer: "

choices = eval(doc['choices'])

text = output.format(subject=doc['subject'], question=doc['question'], choice1=choices[0], choice2=choices[1], choice3=choices[2], choice4=choices[3])

return text

def weighted_f1_score(items):

unzipped_list = list(zip(*items))