|
|
|
|
|
""" |
|
The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations. |
|
""" |
|
|
|
import csv |
|
import os |
|
from pathlib import Path |
|
from typing import Dict, List, Tuple |
|
|
|
import datasets |
|
|
|
from seacrowd.utils import schemas |
|
from seacrowd.utils.configs import SEACrowdConfig |
|
from seacrowd.utils.constants import Licenses, Tasks |
|
|
|
_CITATION = """\ |
|
@InProceedings{conneau2018xnli, |
|
author = "Conneau, Alexis |
|
and Rinott, Ruty |
|
and Lample, Guillaume |
|
and Williams, Adina |
|
and Bowman, Samuel R. |
|
and Schwenk, Holger |
|
and Stoyanov, Veselin", |
|
title = "XNLI: Evaluating Cross-lingual Sentence Representations", |
|
booktitle = "Proceedings of the 2018 Conference on Empirical Methods |
|
in Natural Language Processing", |
|
year = "2018", |
|
publisher = "Association for Computational Linguistics", |
|
location = "Brussels, Belgium", |
|
} |
|
""" |
|
|
|
_DATASETNAME = "xnli" |
|
|
|
_DESCRIPTION = """\ |
|
XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, 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). |
|
""" |
|
|
|
_HOMEPAGE = "https://github.com/facebookresearch/XNLI" |
|
|
|
|
|
_LANGUAGES = ["tha", "vie"] |
|
_LANGUAGE_MAPPER = {"tha": "th", "vie": "vi"} |
|
|
|
_LICENSE = Licenses.CC_BY_NC_4_0.value |
|
|
|
_LOCAL = False |
|
|
|
_URLS = { |
|
_DATASETNAME: { |
|
"train": "https://dl.fbaipublicfiles.com/XNLI/XNLI-MT-1.0.zip", |
|
"test": "https://dl.fbaipublicfiles.com/XNLI/XNLI-1.0.zip", |
|
} |
|
} |
|
|
|
_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT] |
|
|
|
_SOURCE_VERSION = "1.1.0" |
|
|
|
_SEACROWD_VERSION = "2024.06.20" |
|
|
|
|
|
class XNLIDataset(datasets.GeneratorBasedBuilder): |
|
""" |
|
XNLI is an evaluation corpus for language transfer and cross-lingual sentence classification in 15 languages. |
|
In SeaCrowd, we currently only have Thailand and Vietnam Language. |
|
""" |
|
|
|
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
|
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
|
|
|
subsets = ["xnli.tha", "xnli.vie"] |
|
|
|
BUILDER_CONFIGS = [ |
|
SEACrowdConfig( |
|
name=f"{sub}_source", |
|
version=datasets.Version(_SOURCE_VERSION), |
|
description=f"{sub} source schema", |
|
schema="source", |
|
subset_id=f"{sub}", |
|
) |
|
for sub in subsets |
|
] + [ |
|
SEACrowdConfig( |
|
name=f"{sub}_seacrowd_pairs", |
|
version=datasets.Version(_SEACROWD_VERSION), |
|
description=f"{sub} SEACrowd schema", |
|
schema="seacrowd_pairs", |
|
subset_id=f"{sub}", |
|
) |
|
for sub in subsets |
|
] |
|
|
|
DEFAULT_CONFIG_NAME = "xnli.vie_source" |
|
labels = ["contradiction", "entailment", "neutral"] |
|
|
|
def _info(self) -> datasets.DatasetInfo: |
|
if self.config.schema == "source": |
|
features = datasets.Features( |
|
{ |
|
"premise": datasets.Value("string"), |
|
"hypothesis": datasets.Value("string"), |
|
"label": datasets.ClassLabel(names=self.labels), |
|
} |
|
) |
|
|
|
elif self.config.schema == "seacrowd_pairs": |
|
features = schemas.pairs_features(self.labels) |
|
|
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
homepage=_HOMEPAGE, |
|
license=_LICENSE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
|
"""Returns SplitGenerators.""" |
|
|
|
urls = _URLS[_DATASETNAME] |
|
data_dir = dl_manager.download_and_extract(urls) |
|
|
|
xnli_train = os.path.join(data_dir["train"], "XNLI-MT-1.0", "multinli") |
|
train_data_path = os.path.join(xnli_train, "multinli.train.{}.tsv") |
|
|
|
xnli_test = os.path.join(data_dir["test"], "XNLI-1.0") |
|
val_data_path = os.path.join(xnli_test, "xnli.dev.tsv") |
|
test_data_path = os.path.join(xnli_test, "xnli.test.tsv") |
|
|
|
lang = self.config.name.split("_")[0].split(".")[-1] |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": train_data_path, |
|
"split": "train", |
|
"language": _LANGUAGE_MAPPER[lang], |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": val_data_path, |
|
"split": "dev", |
|
"language": _LANGUAGE_MAPPER[lang], |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": test_data_path, |
|
"split": "test", |
|
"language": _LANGUAGE_MAPPER[lang], |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples(self, filepath: Path, split: str, language: str) -> Tuple[int, Dict]: |
|
"""Yields examples as (key, example) tuples.""" |
|
|
|
if self.config.schema == "source": |
|
if split == "train": |
|
file = open(filepath.format(language), encoding="utf-8") |
|
reader = csv.DictReader(file, delimiter="\t", quoting=csv.QUOTE_NONE) |
|
for row_idx, row in enumerate(reader): |
|
key = str(row_idx) |
|
yield key, { |
|
"premise": row["premise"], |
|
"hypothesis": row["hypo"], |
|
"label": row["label"].replace("contradictory", "contradiction"), |
|
} |
|
else: |
|
with open(filepath, encoding="utf-8") as f: |
|
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
|
for row in reader: |
|
if row["language"] == language: |
|
yield row["pairID"], { |
|
"premise": row["sentence1"], |
|
"hypothesis": row["sentence2"], |
|
"label": row["gold_label"], |
|
} |
|
elif self.config.schema == "seacrowd_pairs": |
|
if split == "train": |
|
file = open(filepath.format(language), encoding="utf-8") |
|
reader = csv.DictReader(file, delimiter="\t", quoting=csv.QUOTE_NONE) |
|
for row_idx, row in enumerate(reader): |
|
yield str(row_idx), { |
|
"id": str(row_idx), |
|
"text_1": row["premise"], |
|
"text_2": row["hypo"], |
|
"label": row["label"].replace("contradictory", "contradiction"), |
|
} |
|
else: |
|
with open(filepath, encoding="utf-8") as f: |
|
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
|
skip = set() |
|
for row in reader: |
|
if row["language"] == language: |
|
if row["pairID"] in skip: |
|
continue |
|
skip.add(row["pairID"]) |
|
yield row["pairID"], { |
|
"id": row["pairID"], |
|
"text_1": row["sentence1"], |
|
"text_2": row["sentence2"], |
|
"label": row["gold_label"], |
|
} |
|
else: |
|
raise ValueError(f"Invalid config: {self.config.name}") |
|
|
