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return letters[:num_choices]
# File: lm-evaluation-harness-main/lm_eval/tasks/eus_trivia/utils.py
from typing import List
letters = ['A', 'B', 'C', 'D']
def doc_to_text(doc) -> str:
candidates = doc['candidates']
num_choices = len(candidates)
if num_choices < 2:
raise ValueError('Invalid number of candidates')
choices = letters[:num_choices]
formatted_choices = '\n'.join([f'{choice}: {candidates[i]}' for (i, choice) in enumerate(choices)])
return f"Galdera: {doc['question']}\n{formatted_choices}\nErantzuna:"
def doc_to_choice(doc) -> List[str]:
num_choices = len(doc['candidates'])
if num_choices < 2:
raise ValueError('Invalid number of candidates')
return letters[:num_choices]
# File: lm-evaluation-harness-main/lm_eval/tasks/fda/task.py
import re
from typing import List
import numpy as np
from lm_eval.api.instance import Instance
from lm_eval.api.task import ConfigurableTask
class FDA(ConfigurableTask):
VERSION = 0
DATASET_PATH = 'hazyresearch/based-fda'
DATASET_NAME = 'default'
def __init__(self, **kwargs):
super().__init__(config={'metadata': {'version': self.VERSION}})
def has_training_docs(self):
return False
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def validation_docs(self):
return self.dataset['validation']
def doc_to_text(self, doc):
return doc['text']
def doc_to_target(self, doc):
return doc['value']
def construct_requests(self, doc, ctx, **kwargs):
return [Instance(request_type='generate_until', doc=doc, arguments=(ctx, {'until': ['\n'], 'max_gen_toks': 48}), idx=0, **kwargs)]
def process_results(self, doc, results):
continuation = results
return {'contains': contains_score(continuation[0], [doc['value']])}
def aggregation(self):
return {'contains': np.mean}
def higher_is_better(self):
return {'contains': True}
def contains_score(prediction: str, labels: List[str]):
return max((int(bool(re.search(re.compile(re.escape(label), re.IGNORECASE), prediction))) for label in labels))
# File: lm-evaluation-harness-main/lm_eval/tasks/french_bench/preprocess_wikitext.py
import re
def wikitext_detokenizer(doc):
string = doc['paragraph']
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