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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.
# Lint as: python3
"""LugandaPII: PII for Luganda Language"""
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Luganda Ner Dataset},
author={many authors
},
year={2022}
}
"""
_DESCRIPTION = """\
LugandaPII is a named entity dataset consisting of PERSON, ORG, LOCATION, NORP, USERID and DATE entities.
The train/validation/test sets are available for the Luganda language.
"""
# for github, replace "tree" with "raw" for example;
# "https://github.com/conradsuuna/luganda-ner-data/tree/main/data" =>
# "https://github.com/conradsuuna/luganda-ner-data/raw/main/data/"
_URL = "https://github.com/conradsuuna/luganda-ner-data/raw/main/data"
_TRAINING_FILE = "train.txt"
_VAL_FILE = "val.txt"
_TEST_FILE = "test.txt"
class LugPIIConfig(datasets.BuilderConfig):
"""BuilderConfig for Masakhaner"""
def __init__(self, **kwargs):
"""BuilderConfig for Masakhaner.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(LugPIIConfig, self).__init__(**kwargs)
class Masakhaner(datasets.GeneratorBasedBuilder):
"""Masakhaner dataset."""
BUILDER_CONFIGS = [
LugPIIConfig(name="lug", version=datasets.Version("1.0.0"), description="PII NER Luganda dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PERSON",
"I-PERSON",
"L-PERSON",
"U-PERSON",
"B-NORP",
"I-NORP",
"L-NORP",
"U-NORP",
"B-DATE",
"I-DATE",
"L-DATE",
"U-DATE",
"B-USERID",
"I-USERID",
"L-USERID",
"U-USERID",
"B-ORG",
"I-ORG",
"L-ORG",
"U-ORG",
"B-LOCATION",
"I-LOCATION",
"L-LOCATION",
"U-LOCATION",
]
)
),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}/{_TRAINING_FILE}",
"val": f"{_URL}/{_VAL_FILE}",
"test": f"{_URL}/{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
for line in f:
if line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
# since our tokens are space separated
splits = line.split(" ")
tokens.append(splits[0])
ner_tags.append(splits[1].rstrip())
# last example
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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