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# coding=utf-8
# Source: https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py
"""ANTILLES Corpus"""
import datasets
from tqdm import tqdm
logger = datasets.logging.get_logger(__name__)
_CITATION = """
@misc{
universaldependencies,
title={UniversalDependencies/UD_French-GSD},
url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub},
author={UniversalDependencies}
}
@inproceedings{mcdonald-etal-2013-universal,
title = {{U}niversal {D}ependency Annotation for Multilingual Parsing},
author = {
McDonald, Ryan and
Nivre, Joakim and
Quirmbach-Brundage, Yvonne and
Goldberg, Yoav and
Das, Dipanjan and
Ganchev, Kuzman and
Hall, Keith and
Petrov, Slav and
Zhang, Hao and
Tackstrom, Oscar and
Bedini, Claudia and
Bertomeu Castello, Nuria and
Lee, Jungmee
},
booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
month = aug,
year = {2013},
address = {Sofia, Bulgaria},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/P13-2017},
pages = {92--97",
}
@techreport{
LIA_TAGG,
author = {Frédéric Béchet},
title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer},
institution = {Aix-Marseille University & CNRS},
year = {2001}
}
"""
_LICENSE = """
For the following languages
German, Spanish, French, Indonesian, Italian, Japanese, Korean and Brazilian
Portuguese
we will distinguish between two portions of the data.
1. The underlying text for sentences that were annotated. This data Google
asserts no ownership over and no copyright over. Some or all of these
sentences may be copyrighted in some jurisdictions. Where copyrighted,
Google collected these sentences under exceptions to copyright or implied
license rights. GOOGLE MAKES THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY
WARRANTY OF ANY KIND, WHETHER EXPRESS OR IMPLIED.
2. The annotations -- part-of-speech tags and dependency annotations. These are
made available under a CC BY-SA 4.0. GOOGLE MAKES
THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY WARRANTY OF ANY KIND, WHETHER
EXPRESS OR IMPLIED. See attached LICENSE file for the text of CC BY-NC-SA.
Portions of the German data were sampled from the CoNLL 2006 Tiger Treebank
data. Hans Uszkoreit graciously gave permission to use the underlying
sentences in this data as part of this release.
Any use of the data should reference the above plus:
Universal Dependency Annotation for Multilingual Parsing
Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg,
Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang,
Oscar Tackstrom, Claudia Bedini, Nuria Bertomeu Castello and Jungmee Lee
Proceedings of ACL 2013
"""
_DESCRIPTION = "No description"
class ANTILLES(datasets.GeneratorBasedBuilder):
"""ANTILLES dataset."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="ANTILLES", version=VERSION, description="The ANTILLES corpora"),
]
DEFAULT_CONFIG_NAME = "ANTILLES"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"pos_tags": datasets.Sequence(
datasets.features.ClassLabel(
names = ['PART', 'PDEMMP', 'PREFS', 'PINDMP', 'DINTMS', 'NUM', 'PINTFS', 'NFP', 'PUNCT', 'PRELMS', 'NOUN', 'PPER3MS', 'AUX', 'COSUB', 'ADJ', 'VPPRE', 'COCO', 'ADJMP', 'X', 'NMS', 'PINDMS', 'DETFS', 'PPER2S', 'PREFP', 'PPER3MP', 'PRELMP', 'PINDFS', 'PRON', 'PREP', 'PPOBJMP', 'ADJFS', 'DET', 'ADJFP', 'PDEMFP', 'PREL', 'PPER3FS', 'VPPFS', 'PPER3FP', 'CHIF', 'NMP', 'SYM', 'NFS', 'VERB', 'PREF', 'VPPFP', 'PDEMMS', 'XFAMIL', 'PINDFP', 'VPPMP', 'YPFOR', 'ADV', 'PRELFS', 'DINTFS', 'DETMS', 'PPOBJFP', 'PPOBJMS', 'VPPMS', 'INTJ', 'PROPN', 'PDEMFS', 'PPER1S', 'PRELFP', 'MOTINC', 'ADJMS', 'PPOBJFS']
)
),
}
),
supervised_keys=None,
homepage="https://github.com/qanastek/ANTILLES",
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
TRAIN_PATH = 'data/train.conllu'
DEV_PATH = 'data/dev.conllu'
TEST_PATH = 'data/test.conllu'
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": TRAIN_PATH,
"split": "train",
}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": DEV_PATH,
"split": "dev",
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": TEST_PATH,
"split": "test",
}
),
]
def _generate_examples(self, filepath, split):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
pos_tags = []
for line in tqdm(f):
if "#" in line or line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
}
guid += 1
tokens = []
pos_tags = []
else:
splits = line.split('\t')
tokens.append(splits[1])
pos_tags.append(splits[3].rstrip() if "_" not in splits[3] else "X")
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
}
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