|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""ASSIN 2 dataset.""" |
|
|
|
|
|
import xml.etree.ElementTree as ET |
|
|
|
import datasets |
|
|
|
|
|
_CITATION = """ |
|
@inproceedings{real2020assin, |
|
title={The assin 2 shared task: a quick overview}, |
|
author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, |
|
booktitle={International Conference on Computational Processing of the Portuguese Language}, |
|
pages={406--412}, |
|
year={2020}, |
|
organization={Springer} |
|
} |
|
""" |
|
|
|
_DESCRIPTION = """ |
|
The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1. |
|
The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese, |
|
annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment |
|
classes are either entailment or none. The test data are composed of approximately 3,000 sentence pairs with the same |
|
annotation. All data were manually annotated. |
|
""" |
|
|
|
_HOMEPAGE = "https://sites.google.com/view/assin2" |
|
|
|
_LICENSE = "" |
|
|
|
_URLS = { |
|
"train": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-train-only.xml", |
|
"dev": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-dev.xml", |
|
"test": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-test.xml", |
|
} |
|
|
|
|
|
class Assin2(datasets.GeneratorBasedBuilder): |
|
"""ASSIN 2 dataset.""" |
|
|
|
VERSION = datasets.Version("1.0.0") |
|
|
|
def _info(self): |
|
features = datasets.Features( |
|
{ |
|
"sentence_pair_id": datasets.Value("int64"), |
|
"premise": datasets.Value("string"), |
|
"hypothesis": datasets.Value("string"), |
|
"relatedness_score": datasets.Value("float32"), |
|
"entailment_judgment": datasets.features.ClassLabel(names=["NONE", "ENTAILMENT"]), |
|
} |
|
) |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
supervised_keys=None, |
|
homepage=_HOMEPAGE, |
|
license=_LICENSE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
data_dir = dl_manager.download(_URLS) |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"filepath": data_dir["train"], |
|
"split": "train", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"filepath": data_dir["test"], |
|
"split": "test", |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"filepath": data_dir["dev"], |
|
"split": "dev", |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples(self, filepath, split): |
|
"""Yields examples.""" |
|
|
|
id_ = 0 |
|
|
|
with open(filepath, "rb") as f: |
|
|
|
tree = ET.parse(f) |
|
root = tree.getroot() |
|
|
|
for pair in root: |
|
|
|
yield id_, { |
|
"sentence_pair_id": int(pair.attrib.get("id")), |
|
"premise": pair.find(".//t").text, |
|
"hypothesis": pair.find(".//h").text, |
|
"relatedness_score": float(pair.attrib.get("similarity")), |
|
"entailment_judgment": pair.attrib.get("entailment").upper(), |
|
} |
|
|
|
id_ += 1 |
|
|