Datasets:

danidarko

/

docs

like 0

def higher_is_better(self):

return {metric.replace('metrics.', ''): True for metric in self.metrics}

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

from typing import Dict, List

def doc_to_choice(doc: Dict) -> List[str]:

return _remove_prefixes(doc['answers'])

def doc_to_target(doc: Dict) -> List[int]:

remaining = _remove_prefixes(doc['answers'])

return list(range(len(remaining)))

def _remove_prefixes(aliases):

aliases.sort()

ret = [aliases[0]]

for alias in aliases[1:]:

if not alias.startswith(ret[-1]):

ret.append(alias)

return ret

# File: lm-evaluation-harness-main/lm_eval/tasks/wikitext/preprocess_wikitext.py

import re

def wikitext_detokenizer(doc):

string = doc['page']

string = string.replace("s '", "s'")

string = re.sub("/' [0-9]/", "/'[0-9]/", string)

string = string.replace(' @-@ ', '-')

string = string.replace(' @,@ ', ',')

string = string.replace(' @.@ ', '.')

string = string.replace(' : ', ': ')

string = string.replace(' ; ', '; ')

string = string.replace(' . ', '. ')

string = string.replace(' ! ', '! ')

string = string.replace(' ? ', '? ')

string = string.replace(' , ', ', ')

string = re.sub('\\(\\s*([^\\)]*?)\\s*\\)', '(\\1)', string)

string = re.sub('\\[\\s*([^\\]]*?)\\s*\\]', '[\\1]', string)

string = re.sub('{\\s*([^}]*?)\\s*}', '{\\1}', string)

string = re.sub('\\"\\s*([^\\"]*?)\\s*\\"', '"\\1"', string)

string = re.sub("'\\s*([^']*?)\\s*'", "'\\1'", string)

string = string.replace('= = = =', '====')

string = string.replace('= = =', '===')

string = string.replace('= =', '==')

string = string.replace(' ' + chr(176) + ' ', chr(176))

string = string.replace(' \n', '\n')

string = string.replace('\n ', '\n')

string = string.replace(' N ', ' 1 ')

string = string.replace(" 's", "'s")

return string

def process_results(doc, results):

(loglikelihood,) = results

_words = len(re.split('\\s+', doc['page']))

_bytes = len(doc['page'].encode('utf-8'))

return {'word_perplexity': (loglikelihood, _words), 'byte_perplexity': (loglikelihood, _bytes), 'bits_per_byte': (loglikelihood, _bytes)}

# File: lm-evaluation-harness-main/lm_eval/tasks/winogrande/preprocess_winogrande.py

def doc_to_text(doc):

answer_to_num = {'1': 0, '2': 1}

return answer_to_num[doc['answer']]

def doc_to_target(doc):

idx = doc['sentence'].index('_') + 1

return doc['sentence'][idx:].strip()

def doc_to_choice(doc):

idx = doc['sentence'].index('_')

options = [doc['option1'], doc['option2']]

return [doc['sentence'][:idx] + opt for opt in options]

# File: lm-evaluation-harness-main/lm_eval/tasks/wsc273/utils.py

upper_pronouns = ['A', 'An', 'The', 'She', 'He', 'It', 'They', 'My', 'His', 'Her', 'Their']

def process_doc(dataset):

def process_fn(doc):

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

doc['options'][0] = __normalize_option(doc, doc['options'][0])

doc['options'][1] = __normalize_option(doc, doc['options'][1])

return doc

return dataset.map(process_fn)

def __normalize_option(doc, option):

if doc['pronoun'].lower() in ['my', 'his', 'her', 'our', 'their']:

option += "'s"

pronoun = option.split()[0]

start_of_sentence = doc['text'][doc['pronoun_loc'] - 2] == '.'

if not start_of_sentence and pronoun in upper_pronouns:

return option.replace(pronoun, pronoun.lower())

return option

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

from functools import partial

def convert_choice(choice):

return choice[0].lower() + choice[1:]