|
|
|
import json |
|
import datasets |
|
import pandas as pd |
|
|
|
logger = datasets.logging.get_logger(__name__) |
|
|
|
_CITATION = """ |
|
ADD CITATION |
|
""" |
|
|
|
_DESCRIPTION = """ |
|
professional translation into Spanish of Winograd NLI dataset as published in GLUE Benchmark. |
|
The Winograd NLI dataset presents 855 sentence pairs, |
|
in which the first sentence contains an ambiguity and the second one a possible interpretation of it. |
|
The label indicates if the interpretation is correct (1) or not (0). |
|
""" |
|
|
|
_HOMEPAGE = """https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html""" |
|
|
|
|
|
_URL = "./" |
|
_TRAINING_FILE = "wnli-train-es.tsv" |
|
_DEV_FILE = "wnli-dev-es.tsv" |
|
_TEST_FILE = "wnli-test-shuffled-es.tsv" |
|
|
|
|
|
class WinogradConfig(datasets.BuilderConfig): |
|
""" Builder config for the Winograd-CA dataset """ |
|
|
|
def __init__(self, **kwargs): |
|
"""BuilderConfig for Winograd-CA. |
|
Args: |
|
**kwargs: keyword arguments forwarded to super. |
|
""" |
|
super(WinogradConfig, self).__init__(**kwargs) |
|
|
|
|
|
class Winograd(datasets.GeneratorBasedBuilder): |
|
""" Winograd Dataset """ |
|
|
|
BUILDER_CONFIGS = [ |
|
WinogradConfig( |
|
name="winograd", |
|
version=datasets.Version("1.0.0"), |
|
description="Winograd dataset", |
|
), |
|
] |
|
|
|
def __init__(self, **kwargs): |
|
super(Winograd, self).__init__(**dict(kwargs, column_names = ['index','sentence1','sentence2','label'])) |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
"sentence1": datasets.Value("string"), |
|
"sentence2": datasets.Value("string"), |
|
"label": datasets.features.ClassLabel |
|
(names= |
|
[ |
|
"not_entailment", |
|
"entailment" |
|
] |
|
), |
|
} |
|
), |
|
homepage=_HOMEPAGE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
urls_to_download = { |
|
"train": f"{_URL}{_TRAINING_FILE}", |
|
"dev": f"{_URL}{_DEV_FILE}", |
|
"test": f"{_URL}{_TEST_FILE}", |
|
} |
|
downloaded_files = dl_manager.download_and_extract(urls_to_download) |
|
|
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
|
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
|
] |
|
|
|
def _generate_examples(self, filepath): |
|
"""This function returns the examples in the raw (text) form.""" |
|
logger.info("generating examples from = %s", filepath) |
|
df = pd.read_csv(filepath, sep='\t') |
|
header = df.keys() |
|
process_label = {0: "not_entailment", 1: "entailment", -1:-1} |
|
for id_, (ref, sentence1, sentence2, score) in df.iterrows(): |
|
yield id_, { |
|
"sentence1": sentence1, |
|
"sentence2": sentence2, |
|
"label": process_label[int(score)], |
|
} |
|
|
|
|
|
|
