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import os
import pathlib
from typing import overload
import datasets
import json
from datasets.info import DatasetInfo
_VERSION = "0.0.1"
_URL= "https://fcheck.fel.cvut.cz/downloads/NLI/anli_v1.0_cs_google_translate/R3/"
_URLS = {
"train": _URL + "train.jsonl",
"validation": _URL + "dev.jsonl",
"test": _URL + "test.jsonl"
}
_DESCRIPTION = """\
TODO: Anli_cs is a Czech translation of the Adversarial NLI dataset
"""
_CITATION = """\
todo
"""
_LABEL_CONVERSION = {
"n": "NOT ENOUGH INFO",
"e": "SUPPORTS",
"c": "REFUTES"
}
datasets.utils.version.Version
class AnliCs(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"label": datasets.ClassLabel(names=["REFUTES", "NOT ENOUGH INFO", "SUPPORTS"]),
# datasets.features.Sequence({"text": datasets.Value("string"),"answer_start": datasets.Value("int32"),})
"evidence": datasets.Value("string"),
"claim": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
version=_VERSION,
homepage="https://fcheck.fel.cvut.cz/dataset/",
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, {
"filepath": downloaded_files["train"]
}),
datasets.SplitGenerator(datasets.Split.VALIDATION, {
"filepath": downloaded_files["validation"]
}),
datasets.SplitGenerator(datasets.Split.TEST, {
"filepath": downloaded_files["test"]
}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
key = 0
with open(filepath, encoding="utf-8") as f:
for line in f:
datapoint = json.loads(line)
yield key, {
"id": datapoint["uid"],
"evidence": datapoint["context"],
"claim": datapoint["hypothesis"],
"label": _LABEL_CONVERSION[datapoint["label"]]
}
key += 1
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