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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/afrixnli/anli prompt/en-direct/utils.py
from sklearn.metrics import f1_score
def doc_to_target(doc):
replacements = {0: 'True', 1: 'Neither', 2: 'False'}
return replacements[doc['label']]
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/afrixnli/anli prompt/translate/utils.py
from sklearn.metrics import f1_score
def doc_to_target(doc):
replacements = {0: 'True', 1: 'Neither', 2: 'False'}
return replacements[doc['label']]
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/afrixnli/lai prompt/direct/utils.py
from sklearn.metrics import f1_score
def doc_to_text(doc):
output = 'Please identify whether the premise entails or contradicts the hypothesis in the following premise\n and hypothesis. The answer should be exact entailment, contradiction, or neutral.\n\n Premise: {premise}\n Hypothesis: {hypothesis}\n\n Is it entailment, contradiction, or neutral?'
text = output.format(premise=doc['premise'], hypothesis=doc['hypothesis'])
return text
def doc_to_target(doc):
replacements = {0: 'entailment', 1: 'neutral', 2: 'contradiction'}
return replacements[doc['label']]
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/afrixnli/lai prompt/translate/utils.py
from sklearn.metrics import f1_score
def doc_to_text(doc):
output = 'Please identify whether the premise entails or contradicts the hypothesis in the following premise\n and hypothesis. The answer should be exact entailment, contradiction, or neutral.\n\n Premise: {premise}\n Hypothesis: {hypothesis}\n\n Is it entailment, contradiction, or neutral?'
text = output.format(premise=doc['premise'], hypothesis=doc['hypothesis'])
return text
def doc_to_target(doc):
replacements = {0: 'entailment', 1: 'neutral', 2: 'contradiction'}
return replacements[doc['label']]
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/afrixnli/utils.py
import argparse
import yaml
class FunctionTag:
def __init__(self, value):
self.value = value
LANGUAGES = {'amh': {'QUESTION_WORD': 'ትክክል', 'ENTAILMENT_LABEL': 'አዎ', 'NEUTRAL_LABEL': 'እንዲሁም', 'CONTRADICTION_LABEL': 'አይ'}, 'eng': {'QUESTION_WORD': 'Right', 'ENTAILMENT_LABEL': 'Yes', 'NEUTRAL_LABEL': 'Also', 'CONTRADICTION_LABEL': 'No'}, 'ewe': {'QUESTION_WORD': 'Esɔ gbe', 'ENTAILMENT_LABEL': 'Ɛ̃', 'NEUTRAL_LABEL': 'Hã', 'CONTRADICTION_LABEL': 'Ao'}, 'fra': {'QUESTION_WORD': 'correct', 'ENTAILMENT_LABEL': 'Oui', 'NEUTRAL_LABEL': 'Aussi', 'CONTRADICTION_LABEL': 'Non'}, 'hau': {'QUESTION_WORD': 'Daidai', 'ENTAILMENT_LABEL': 'Ee', 'NEUTRAL_LABEL': 'Haka kuma', 'CONTRADICTION_LABEL': "A'a"}, 'ibo': {'QUESTION_WORD': 'Ziri ezi', 'ENTAILMENT_LABEL': 'Éè', 'NEUTRAL_LABEL': 'Ọzọkwa', 'CONTRADICTION_LABEL': 'Mba'}, 'kin': {'QUESTION_WORD': 'Nibyo', 'ENTAILMENT_LABEL': 'Yego', 'NEUTRAL_LABEL': 'Na none', 'CONTRADICTION_LABEL': 'Oya'}, 'lin': {'QUESTION_WORD': 'Malamu', 'ENTAILMENT_LABEL': 'Iyo', 'NEUTRAL_LABEL': 'Lisusu', 'CONTRADICTION_LABEL': 'Te'}, 'lug': {'QUESTION_WORD': 'Kituufu', 'ENTAILMENT_LABEL': 'Yee', 'NEUTRAL_LABEL': 'N’ekirala', 'CONTRADICTION_LABEL': 'Nedda'}, 'orm': {'QUESTION_WORD': 'Sirrii', 'ENTAILMENT_LABEL': 'Eeyyee', 'NEUTRAL_LABEL': 'Akkasumas', 'CONTRADICTION_LABEL': 'Lakki'}, 'sna': {'QUESTION_WORD': 'Chokwadi', 'ENTAILMENT_LABEL': 'Hongu', 'NEUTRAL_LABEL': 'Uye', 'CONTRADICTION_LABEL': 'Kwete'}, 'sot': {'QUESTION_WORD': 'Nepile', 'ENTAILMENT_LABEL': 'E', 'NEUTRAL_LABEL': 'Hape', 'CONTRADICTION_LABEL': 'Tjhe'}, 'swa': {'QUESTION_WORD': 'Sahihi', 'ENTAILMENT_LABEL': 'Ndiyo', 'NEUTRAL_LABEL': 'Pia', 'CONTRADICTION_LABEL': 'Hapana'}, 'twi': {'QUESTION_WORD': 'Nifa', 'ENTAILMENT_LABEL': 'Aane', 'NEUTRAL_LABEL': 'Anaasɛ', 'CONTRADICTION_LABEL': 'Daabi'}, 'wol': {'QUESTION_WORD': 'Dëgg', 'ENTAILMENT_LABEL': 'Waaw', 'NEUTRAL_LABEL': 'Itam', 'CONTRADICTION_LABEL': 'Déet'}, 'xho': {'QUESTION_WORD': 'Ichanekile', 'ENTAILMENT_LABEL': 'Ewe', 'NEUTRAL_LABEL': 'Kananjalo', 'CONTRADICTION_LABEL': 'Hayi'}, 'yor': {'QUESTION_WORD': 'Òótọ́', 'ENTAILMENT_LABEL': 'Bẹ́ẹ̀ni', 'NEUTRAL_LABEL': 'Àti pé', 'CONTRADICTION_LABEL': 'Rárá'}, 'zul': {'QUESTION_WORD': 'Kulungile', 'ENTAILMENT_LABEL': 'Yebo', 'NEUTRAL_LABEL': 'Futhi', 'CONTRADICTION_LABEL': 'Cha'}}
def gen_lang_yamls(output_dir: str, overwrite: bool, mode: str) -> None:
err = []
languages = ['eng', 'amh', 'ibo', 'fra', 'sna', 'wol', 'ewe', 'lin', 'lug', 'xho', 'kin', 'twi', 'zul', 'orm', 'yor', 'hau', 'sot', 'swa']
for lang in languages:
try:
if mode == 'native-direct':
QUESTION_WORD = LANGUAGES[lang]['QUESTION_WORD']
ENTAILMENT_LABEL = LANGUAGES[lang]['ENTAILMENT_LABEL']
NEUTRAL_LABEL = LANGUAGES[lang]['NEUTRAL_LABEL']
CONTRADICTION_LABEL = LANGUAGES[lang]['CONTRADICTION_LABEL']
file_name = f'afrixnli_native_direct_{lang}.yaml'
task_name = f'afrixnli_native_direct_{lang}'
yaml_template = 'afrixnli_native_direct_yaml'
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, 'task': task_name, 'dataset_name': lang, 'doc_to_choice': f'{{{{[premise+", {QUESTION_WORD}? {ENTAILMENT_LABEL}, "+hypothesis,premise+", {QUESTION_WORD}? {NEUTRAL_LABEL}, "+hypothesis,premise+", {QUESTION_WORD}? {CONTRADICTION_LABEL}, "+hypothesis]}}}}'}, f, allow_unicode=True)
else:
file_name = f'afrixnli_{mode}_{lang}.yaml'
task_name = f'afrixnli_{mode}_{lang}'