File size: 6,670 Bytes
3ecf042 f3eff50 183c5df 3ecf042 f3eff50 3ecf042 f3eff50 3ecf042 f3eff50 3ecf042 f3eff50 3ecf042 f3eff50 3ecf042 f3eff50 3ecf042 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Datasets loading script for wikitext_linked"""
import os
import datasets
import pyarrow as pa
import pyarrow.parquet as pq
logger = datasets.utils.logging.get_logger(__name__)
_CITATION = """\
@misc{merity2016pointer,
title={Pointer Sentinel Mixture Models},
author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},
year={2016},
eprint={1609.07843},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{nguyen2021trankit,
title={Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing},
author={Nguyen, Minh Van and Lai, Viet Dac and Veyseh, Amir Pouran Ben and Nguyen, Thien Huu},
booktitle="Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
year={2021}
}
@misc{entity-fishing,
title = {entity-fishing},
howpublished = {\\url{https://github.com/kermitt2/entity-fishing}},
publisher = {GitHub},
year = {2016--2022},
archivePrefix = {swh},
eprint = {1:dir:cb0ba3379413db12b0018b7c3af8d0d2d864139c}
}
"""
_DESCRIPTION = """\
The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
Good and Featured articles on Wikipedia. Dependency Relations, POS, NER tags are marked with trankit and
entities are linked with entity-fishing.
The dataset is available under the Creative Commons Attribution-ShareAlike License.
"""
_HOMEPAGE = "https://github.com/GabrielKP/svo/"
_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"
FEATURES = datasets.Features(
{
"text": datasets.Value("string"),
"original_id": datasets.Value("int64"),
"tok_span": datasets.Sequence(feature=datasets.Sequence(feature=datasets.Value("int64"))),
"tok_upos": datasets.Sequence(feature=datasets.Value("string")),
"tok_xpos": datasets.Sequence(feature=datasets.Value("string")),
"tok_dephead": datasets.Sequence(feature=datasets.Value("int64")),
"tok_deprel": datasets.Sequence(feature=datasets.Value("string")),
"tok_lemma": datasets.Sequence(feature=datasets.Value("string")),
"tok_ner": datasets.Sequence(feature=datasets.Value("string")),
"ent_span": datasets.Sequence(feature=datasets.Sequence(feature=datasets.Value("int64"))),
"ent_wikipedia_external_ref": datasets.Sequence(feature=datasets.Value("string")),
"ent_ner": datasets.Sequence(feature=datasets.Value("string")),
"ent_domains": datasets.Sequence(
feature=datasets.Sequence(feature=datasets.Value("string"))
),
}
)
_URL = "https://huggingface.co/datasets/gabrielkp/wikitext_linked/resolve/main/"
class WikitextLinked(datasets.ArrowBasedBuilder):
"""wikitext_linked is an annotated and linked version from wikitext. Wikitext is a
collection of over 100 million tokens extracted from the set of verified Good and
Featured articles on Wikipedia.
"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="wikitext2",
version=VERSION,
description="The small version",
data_dir="wikitext2",
),
datasets.BuilderConfig(
name="wikitext103",
version=VERSION,
description="The big version",
data_dir="wikitext103",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
citation=_CITATION,
license=_LICENSE,
features=FEATURES,
version=self.VERSION,
homepage=_HOMEPAGE,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(f"{_URL}{self.config.data_dir}.zip")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.data_dir, "train.parquet"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.data_dir, "validation.parquet"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, self.config.data_dir, "test.parquet"),
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_tables(self, filepath):
schema = pa.schema(FEATURES.type)
with open(filepath, "rb") as f:
parquet_file = pq.ParquetFile(f)
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=10000, columns=None)
):
pa_table = pa.Table.from_batches([record_batch])
pa_table = pa.Table.from_arrays(
[pa_table[field.name] for field in schema], schema=schema
)
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f"{batch_idx}", pa_table
except ValueError as e:
logger.error(f"Failed to read file '{filepath}' with error {type(e)}: {e}")
raise
|