Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
natural-language-inference
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1M - 10M
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""XNLI: The Cross-Lingual NLI Corpus."""
# import collections
# import csv
import os
import json
# from contextlib import ExitStack
import datasets
_CITATION = """\
@misc{https://doi.org/10.48550/arxiv.2204.08776,
doi = {10.48550/ARXIV.2204.08776},
url = {https://arxiv.org/abs/2204.08776},
author = {Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {IndicXNLI: Evaluating Multilingual Inference for Indian Languages},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
}"""
_DESCRIPTION = """\
IndicXNLI is a translated version of XNLI to 11 Indic Languages. As with XNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""
_LANGUAGES = (
'hi',
'bn',
'mr',
'as',
'ta',
'te',
'or',
'ml',
'pa',
'gu',
'kn'
)
class IndicxnliConfig(datasets.BuilderConfig):
"""BuilderConfig for XNLI."""
def __init__(self, language: str, **kwargs):
"""BuilderConfig for XNLI.
Args:
language: One of hi, bn, mr, as, ta, te, or, ml, pa, gu, kn
**kwargs: keyword arguments forwarded to super.
"""
super(IndicxnliConfig, self).__init__(**kwargs)
self.language = language
self.languages = _LANGUAGES
class Indicxnli(datasets.GeneratorBasedBuilder):
"""XNLI: The Cross-Lingual NLI Corpus. Version 1.0."""
VERSION = datasets.Version("1.1.0", "")
BUILDER_CONFIG_CLASS = IndicxnliConfig
BUILDER_CONFIGS = [
IndicxnliConfig(
name=lang,
language=lang,
version=datasets.Version("1.1.0", ""),
description=f"Plain text import of IndicXNLI for the {lang} language",
)
for lang in _LANGUAGES
]
def _info(self):
features = datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
# No default supervised_keys (as we have to pass both premise
# and hypothesis as input).
supervised_keys=None,
homepage="https://www.nyu.edu/projects/bowman/xnli/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_dir = 'forward/train'
dev_dir = 'forward/dev'
test_dir = 'forward/test'
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": [
os.path.join(train_dir, f"xnli_{lang}.json") for lang in self.config.languages
],
"data_format": "IndicXNLI",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepaths": [os.path.join(
test_dir, f"xnli_{lang}.json") for lang in self.config.languages], "data_format": "IndicXNLI"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepaths": [os.path.join(
dev_dir, f"xnli_{lang}.json") for lang in self.config.languages], "data_format": "XNLI"},
),
]
def _generate_examples(self, data_format, filepaths):
"""This function returns the examples in the raw (text) form."""
file_path = ""
for path in filepaths:
if path[-7:-5] == self.config.language:
file_path = path
break
with open(file_path, "r") as f:
data = json.load(f)
data = data[data.keys()[0]]
for idx, row in enumerate(data):
yield idx, {
"premise": row["sentence1"],
"hypothesis": row["sentence2"],
"label": row["gold_label"],
}
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