Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
machine-generated
Annotations Creators:
machine-generated
Source Datasets:
extended|wikitext
ArXiv:
Tags:
License:
# 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 | |