Commit 753e8670 authored by JessicaOjo's avatar JessicaOjo
Browse files

add manual xnli prompt, add multichoice for openai models, and adapt...

add manual xnli prompt, add multichoice for openai models, and adapt multichoice metric for openai model
parent f720ce81
# Generated by utils.py
dataset_name: twi
include: afrixnli_manual_direct_yaml
task: afrixnli_manual_direct_twi
# Generated by utils.py
dataset_name: wol
include: afrixnli_manual_direct_yaml
task: afrixnli_manual_direct_wol
# Generated by utils.py
dataset_name: xho
include: afrixnli_manual_direct_yaml
task: afrixnli_manual_direct_xho
group:
- xnli
- afrixnli
- afrixnli-manual
dataset_path: masakhane/afrixnli
dataset_name: null
output_type: multiple_choice_gpt
validation_split: validation
test_split: test
fewshot_split: validation
doc_to_text: !function utils.doc_to_text
doc_to_target: !function utils.doc_to_target
doc_to_choice:
- "entailment"
- "neutral"
- "contradiction"
should_decontaminate: true
doc_to_decontamination_query: premise
metric_list:
- metric: f1
aggregation: !function utils.weighted_f1_score
average: weighted
higher_is_better: True
ignore_case: true
ignore_punctuation: true
- metric: acc
aggregation: acc_gpt
higher_is_better: true
ignore_case: true
ignore_punctuation: true
metadata:
version: 1.0
# Generated by utils.py
dataset_name: yor
include: afrixnli_manual_direct_yaml
task: afrixnli_manual_direct_yor
# Generated by utils.py
dataset_name: zul
include: afrixnli_manual_direct_yaml
task: afrixnli_manual_direct_zul
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
and hypothesis. The answer should be exact entailment, contradiction, or neutral.
Premise: {premise}
Hypothesis: {hypothesis}
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
# Generated by utils.py
dataset_name: amh
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_amh
# Generated by utils.py
dataset_name: ewe
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_ewe
# Generated by utils.py
dataset_name: fra
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_fra
# Generated by utils.py
dataset_name: hau
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_hau
# Generated by utils.py
dataset_name: ibo
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_ibo
# Generated by utils.py
dataset_name: kin
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_kin
# Generated by utils.py
dataset_name: lin
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_lin
# Generated by utils.py
dataset_name: lug
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_lug
# Generated by utils.py
dataset_name: orm
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_orm
# Generated by utils.py
dataset_name: sna
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_sna
# Generated by utils.py
dataset_name: sot
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_sot
# Generated by utils.py
dataset_name: swa
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_swa
# Generated by utils.py
dataset_name: twi
include: afrixnli_manual_translate_yaml
task: afrixnli_manual_translate_twi
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