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# coding=utf-8
# Copyright 2020 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.
import logging
import datasets
_CITATION = """\
@inproceedings{,
title = "",
author = "Garagiola, Nazareno",
year = "2022",
url = ""
}
"""
_DESCRIPTION = """Dataset used to train a NER model"""
_URL = "https://raw.githubusercontent.com/NazaGara/betoNER/main/data/wikiner/"
_TRAINING_FILE = "train.conllu"
class WikinerConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(WikinerConfig, self).__init__(**kwargs)
class Wikiner(datasets.GeneratorBasedBuilder):
"""Wikiner dataset."""
BUILDER_CONFIGS = [
WikinerConfig(
name="wikiner",
version=datasets.Version("1.1.0"),
description="wikiner dataset",
),
]
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=[
"ACRNM",
"ADJ",
"ADV",
"ALFS",
"ART",
"BACKSLASH",
"CARD",
"CC",
"CCAD",
"CCNEG",
"CM",
"CODE",
"COLON",
"CQUE",
"CSUBF",
"CSUBI",
"CSUBX",
"DM",
"DOTS",
"FS",
"INT",
"LP",
"NC",
"NEG",
"NMEA",
"NMON",
"NP",
"ORD",
"PAL",
"PDEL",
"PE",
"PERCT",
"PPC",
"PPO",
"PPX",
"PREP",
"QT",
"QU",
"REL",
"RP",
"SE",
"SEMICOLON",
"SLASH",
"SYM",
"UMMX",
"VCLIfin",
"VCLIger",
"VCLIinf",
"VEadj",
"VEfin",
"VEger",
"VEinf",
"VHadj",
"VHfin",
"VHger",
"VHinf",
"VLadj",
"VLfin",
"VLger",
"VLinf",
"VMadj",
"VMfin",
"VMger",
"VMinf",
"VSadj",
"VSfin",
"VSger",
"VSinf",
]
)
),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
"B-MISC",
"I-MISC",
]
)
),
}
),
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": downloaded_files["train"]},
),
]
def _generate_examples(self, filepath):
logging.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
pos_tags = []
ner_tags = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
pos_tags = []
ner_tags = []
else:
splits = line.split(" ")
tokens.append(splits[0])
pos_tags.append(splits[1])
ner_tags.append(splits[2].rstrip())
# last example
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
"ner_tags": ner_tags,
}
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