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c0b67e7 d122bf7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | # 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
"""test testaunic23"""
import logging
import datasets
_CITATION = """\
test testaunic23
"""
_DESCRIPTION = """\
test testaunic23
"""
_URL = "https://raw.githubusercontent.com/andreyokamura-unicamp/test_dataset/refs/heads/main/"
_TRAINING_FILE = "train.txt"
_DEV_FILE = "dev.txt"
_TEST_FILE = "test.txt"
class testaunic23Config(datasets.BuilderConfig):
"""BuilderConfig for testaunic23"""
def __init__(self, **kwargs):
"""BuilderConfig for testaunic23.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(testaunic23Config, self).__init__(**kwargs)
class testaunic23(datasets.GeneratorBasedBuilder):
"""testaunic23 dataset."""
BUILDER_CONFIGS = [
testaunic23Config(name="testaunic23", version=datasets.Version("1.0.0"), description="testaunic23 dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("int32"),
"sentence_id": datasets.Value("int32"),
"tokens": datasets.Sequence(datasets.Value("string")),
"pos_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"-X-"
]
)
),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-ENTITY",
"I-ENTITY",
"B-SYSTEM",
"I-SYSTEM",
"B-DOCUMENT",
"I-DOCUMENT",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC"
]
)
),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_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["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logging.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
document_id = 0
sentence_id = 0
tokens = []
pos_tags = []
chunk_tags = []
ner_tags = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if line.startswith("-DOCSTART-"):
document_id += 1
sentence_id = 0
if tokens:
yield guid, {
"id": str(guid),
"document_id": document_id,
"sentence_id": sentence_id,
"tokens": tokens,
"pos_tags": pos_tags,
"chunk_tags": chunk_tags,
"ner_tags": ner_tags,
}
sentence_id += 1
guid += 1
tokens = []
pos_tags = []
chunk_tags = []
ner_tags = []
else:
# conll2003 tokens are space separated
splits = line.split(" ")
tokens.append(splits[0])
pos_tags.append(splits[1])
chunk_tags.append(splits[2])
ner_tags.append(splits[3].rstrip())
# last example
if tokens:
yield guid, {
"id": str(guid),
"document_id": document_id,
"sentence_id": sentence_id,
"tokens": tokens,
"pos_tags": pos_tags,
"chunk_tags": chunk_tags,
"ner_tags": ner_tags,
} |