Datasets:

danidarko

/

docs

like 0

out_doc = {'questions': doc['question'], 'choices': [doc['A'], doc['B'], doc['C'], doc['D']], 'goal': answer_list.index(doc['answer'])}

return out_doc

return dataset.map(_helper)

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

import argparse

import yaml

try:

import pycountry

except ModuleNotFoundError:

raise Exception('`pycountry` is required for generating translation task prompt templates. please install pycountry via pip install lm-eval[multilingual] or pip install -e .[multilingual]')

gpt3_translation_benchmarks = {'wmt14': ['fr-en'], 'wmt16': ['ro-en', 'de-en']}

LANGUAGES = {**gpt3_translation_benchmarks, 'iwslt2017': ['en-ar']}

def code_to_language(code):

language_tuple = pycountry.languages.get(**{f'alpha_{len(code)}': code})

return language_tuple.name

def gen_lang_yamls(output_dir: str, overwrite: bool) -> None:

err = []

for lang in LANGUAGES.keys():

for dataset_name in LANGUAGES[lang]:

(src_lang, _, tgt_lang) = dataset_name.partition('-')

for (src, tgt) in [[src_lang, tgt_lang], [tgt_lang, src_lang]]:

lang_pair = src + '-' + tgt

file_name = f'{lang}_{lang_pair}.yaml'

try:

(source, target) = (code_to_language(src), code_to_language(tgt))

groups = ['generate_until', 'translation', lang]

if lang in gpt3_translation_benchmarks.keys():

groups += ['gpt3_translation_benchmarks']

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': 'wmt_common_yaml', 'group': groups, 'dataset_path': lang, 'dataset_name': dataset_name if not lang == 'iwslt2017' else 'iwslt2017-' + dataset_name, 'task': f'{lang}-{lang_pair}', 'doc_to_text': f'{source} phrase: ' + '{{translation[' + f'"{src}"' + ']}}\n' + f'{target} phrase:', 'doc_to_target': ' {{' + 'translation[' + f'"{tgt}"]' + '}}'}, f)

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')

args = parser.parse_args()

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

if __name__ == '__main__':

main()

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

import datasets

import numpy as np

import sacrebleu

from rouge_score import rouge_scorer, scoring

ROUGE_SCORER = None

def process_results_mc2(doc, results):

(lls, is_greedy) = zip(*results)

split_idx = list(doc['mc2_targets']['labels']).index(0)

(ll_true, ll_false) = (lls[:split_idx], lls[split_idx:])

(p_true, p_false) = (np.exp(np.array(ll_true)), np.exp(np.array(ll_false)))

p_true = p_true / (sum(p_true) + sum(p_false))

return {'acc': sum(p_true)}

def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset:

return dataset.map(preprocess_function)

def preprocess_function(examples):

def _format_answers(answers):

formatted_answers = []

for answer in answers:

answer = answer.strip()

if len(answer):

if answer[-1] != '.':

formatted_answers.append(answer + '.')

else:

formatted_answers.append(answer)

return formatted_answers

incorrect_answers = _format_answers(examples['incorrect_answers'])

correct_answers = _format_answers(examples['correct_answers'])

if 'I have no comment.' not in correct_answers:

correct_answers.append('I have no comment.')

return {'question': examples['question'].strip(), 'correct_answers': correct_answers, 'incorrect_answers': incorrect_answers}

def process_results_gen(doc, results):

completion = results[0]

(true_refs, false_refs) = (doc['correct_answers'], doc['incorrect_answers'])

all_refs = true_refs + false_refs

bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs]

bleu_correct = np.nanmax(bleu_scores[:len(true_refs)])

bleu_incorrect = np.nanmax(bleu_scores[len(true_refs):])

bleu_max = bleu_correct

bleu_diff = bleu_correct - bleu_incorrect

bleu_acc = int(bleu_correct > bleu_incorrect)

rouge_scores = [rouge([ref], [completion]) for ref in all_refs]

rouge1_scores = [score['rouge1'] for score in rouge_scores]

rouge1_correct = np.nanmax(rouge1_scores[:len(true_refs)])

rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs):])

rouge1_max = rouge1_correct

rouge1_diff = rouge1_correct - rouge1_incorrect