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def _helper(doc):
doc['sentence1'] = process_doc(doc['sentence1']).encode('latin-1').decode('utf-8')
doc['sentence2'] = process_doc(doc['sentence2']).encode('latin-1').decode('utf-8')
return doc
return dataset.map(_helper)
def coref_doc_to_text(x):
def _span_in_context(span_index, span_text):
span_start = span_index
span_end = span_start + len(span_text.split(' ')) - 1
tokens[span_start] = f'*{tokens[span_start]}'
tokens[span_end] = f'{tokens[span_end]}*'
tokens = x['text'].split(' ')
_span_in_context(x['span1_index'], x['span1_text'])
_span_in_context(x['span2_index'] - 1, x['span2_text'])
context = process_doc(' '.join(tokens))
span_1 = process_doc(x['span1_text'])
span_2 = process_doc(x['span2_text'])
text = f'Testua: {context}\n' + f'Galdera: Aurreko testuan, "*{span_1}*" eta "*{span_2}*" gauza bera dira?\n' + 'Erantzuna:'
return text
def micro_f1_score(items):
f1_metric = load_metric('f1')
(golds, preds) = list(zip(*items))
f1_score = f1_metric.compute(references=golds, predictions=preds, average='micro')['f1']
return f1_score
def vaxx_f1_score(items):
f1_metric = load_metric('f1')
(golds, preds) = list(zip(*items))
f1_class = f1_metric.compute(references=golds, predictions=preds, labels=[0, 2], average=None)['f1']
f1_score = sum(f1_class) / len(f1_class)
return f1_score
# File: lm-evaluation-harness-main/lm_eval/tasks/bbh/_generate_configs.py
""""""
import argparse
import os
import re
import datasets
import requests
import yaml
from tqdm import tqdm
from lm_eval import utils
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base_yaml_path', required=True)
parser.add_argument('--save_prefix_path', default='zeroshot')
parser.add_argument('--cot', default=False)
parser.add_argument('--fewshot', default=False)
parser.add_argument('--task_prefix', default='')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
base_yaml_name = os.path.split(args.base_yaml_path)[-1]
with open(args.base_yaml_path, encoding='utf-8') as f:
base_yaml = yaml.full_load(f)
base_doc_to_text = 'Q: {{input}}\nA:'
answer_regex = re.compile('(?<=answer is )(.*)(?=.)')
dataset_path = 'lukaemon/bbh'
for task in tqdm(datasets.get_dataset_infos(dataset_path).keys()):
resp = requests.get(f'https://raw.githubusercontent.com/suzgunmirac/BIG-Bench-Hard/main/cot-prompts/{task}.txt').content.decode('utf-8')
prompt = resp.split('\n-----\n')[-1]
(description, *few_shot) = prompt.split('\n\n')
prefix_doc_to_text = ''
if args.fewshot:
if args.cot:
prefix_doc_to_text = '\n\n'.join(few_shot) + '\n\n'
else:
for shot in few_shot:
try:
answer = answer_regex.search(shot)[0]
except Exception:
print('task', task)
print(shot)
example = shot.split("Let's think step by step.")[0]
prefix_doc_to_text += f'{example}{answer}\n\n'
doc_to_text = prefix_doc_to_text + base_doc_to_text
if args.cot:
doc_to_text = doc_to_text + " Let's think step by step.\n"
yaml_dict = {'include': base_yaml_name, 'task': f'bbh_{args.task_prefix}_{task}', 'dataset_name': task, 'description': description + '\n\n', 'doc_to_text': doc_to_text}
file_save_path = args.save_prefix_path + f'/{task}.yaml'
utils.eval_logger.info(f'Saving yaml for subset {task} to {file_save_path}')
with open(file_save_path, 'w', encoding='utf-8') as yaml_file:
yaml.dump(yaml_dict, yaml_file, width=float('inf'), allow_unicode=True, default_style='"')
# File: lm-evaluation-harness-main/lm_eval/tasks/bbh/cot_zeroshot/utils.py
import collections
import re
import sys
import unicodedata
from lm_eval.filters.extraction import Filter, RegexFilter
class ExtendedRegexFilter(RegexFilter):
punct_tbl = dict.fromkeys((i for i in range(sys.maxunicode) if unicodedata.category(chr(i)).startswith('P')))
def __init__(self, regex_pattern: str='#### (\\-?[0-9\\.\\,]+)', group_select=0, fallback: str='[invalid]', ignore_case=False, ignore_punctuation=False, regexes_to_ignore=None) -> None: