Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
English
Size:
1M - 10M
File size: 4,283 Bytes
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"""Causal QA : """
import os
import sys
import json
import csv
import yaml
import urllib3
import datasets
class CausalqaConfig(datasets.BuilderConfig):
"""BuilderConfig for causalqa."""
def __init__(
self,
data_features,
data_url,
citation,
**kwargs
):
"""BuilderConfig for GLUE.
Args:
data_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the tsv file
data_url: `dict[string, string]`, url to download the zip file from
citation: `string`, citation for the data set
process_label: `Function[string, any]`, function taking in the raw value
of the label and processing it to the form required by the label feature
**kwargs: keyword arguments forwarded to super.
"""
super(CausalqaConfig, self).__init__(**kwargs)
self.data_features = data_features
self.data_url = data_url
self.citation = citation
def OneBuild(data_info, feat_meta):
main_name = [*data_info][0]
submain_name = data_info[main_name].keys()
all_config = []
for k in submain_name:
fm_temp = feat_meta[main_name][k]
one_data_info = data_info[main_name][k]
cqa_config = CausalqaConfig(
name="{}.{}".format(main_name,k),
description=one_data_info["description"],
version=datasets.Version(one_data_info["version"], ""),
data_features=fm_temp,
data_url=one_data_info["url_data"],
citation=one_data_info["citation"]
)
all_config.append(cqa_config)
return all_config
class CausalQA(datasets.GeneratorBasedBuilder):
"""CausalQA: An QA causal type dataset."""
http = urllib3.PoolManager()
_PATH_METADATA_RES = http.request('GET', 'https://huggingface.co/datasets/jakartaresearch/causalqa/raw/main/source/features_metadata.yaml')
_FILE_URL_RES = http.request('GET', 'https://huggingface.co/datasets/jakartaresearch/causalqa/raw/main/source/dataset_info.json')
_FILE_URL = json.loads(_FILE_URL_RES.data.decode("utf-8"))
_PATH_DESCRIPTION_RES = http.request('GET', 'https://huggingface.co/datasets/jakartaresearch/causalqa/raw/main/source/dataset_description.txt')
_CAUSALQA_DESCRIPTION = _PATH_DESCRIPTION_RES.data.decode("utf-8")
_HOMEPAGE = _FILE_URL['homepage']
all_files = _FILE_URL['files']
try:
fmeta = yaml.safe_load(_PATH_METADATA_RES.data)
except yaml.YAMLError as exc:
print(exc)
BUILDER_CONFIGS = []
for f in all_files:
BUILDER_CONFIGS += (OneBuild(f, fmeta))
def _info(self):
self.features = {feat: datasets.Value(self.config.data_features[feat])
for feat in self.config.data_features}
return datasets.DatasetInfo(
description=self._CAUSALQA_DESCRIPTION,
features=datasets.Features(self.features),
homepage=self._HOMEPAGE
)
def _split_generators(self, dl_manager):
data_train = dl_manager.download(self.config.data_url['train'])
data_val = dl_manager.download(self.config.data_url['val'])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_train
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_val ## keys (as parameters) is used during generate example
},
)
]
def _generate_examples(self, filepath):
"""Generate examples."""
csv.field_size_limit(1000000000)
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(csv_file, delimiter=",")
next(csv_reader)
## the yield depends on files features
for id_, row in enumerate(csv_reader):
existing_values = row
feature_names = [*self.features]
one_example_row = dict(zip(feature_names, existing_values))
yield id_, one_example_row
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