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import json
import datasets
# _SPLIT = ['train', 'test', 'valid']
_CITATION = """\
@inproceedings{hulth2003improved,
title={Improved automatic keyword extraction given more linguistic knowledge},
author={Hulth, Anette},
booktitle={Proceedings of the 2003 conference on Empirical methods in natural language processing},
pages={216--223},
year={2003}
}
"""
_DESCRIPTION = """\
Benchmark dataset for automatic identification of keyphrases from text published with the work - Improved automatic keyword extraction given more linguistic knowledge. Anette Hulth. In Proceedings of EMNLP 2003. p. 216-223.
"""
_HOMEPAGE = "https://aclanthology.org/W03-1028.pdf"
# The license information was obtained from https://github.com/boudinfl/ake-datasets as the dataset shared over here is taken from here
_LICENSE = "Apache 2.0 License"
# TODO: Add link to the official dataset URLs here
_URLS = {
"test": "test.jsonl",
"train": "train.jsonl",
"valid": "valid.jsonl"
}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class Inspec(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="extraction", version=VERSION,
description="This part of my dataset covers extraction"),
datasets.BuilderConfig(name="generation", version=VERSION,
description="This part of my dataset covers generation"),
datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"),
]
DEFAULT_CONFIG_NAME = "extraction"
def _info(self):
if self.config.name == "extraction": # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"id": datasets.Value("int64"),
"document": datasets.features.Sequence(datasets.Value("string")),
"doc_bio_tags": datasets.features.Sequence(datasets.Value("string"))
}
)
elif self.config.name == "generation":
features = datasets.Features(
{
"id": datasets.Value("int64"),
"document": datasets.features.Sequence(datasets.Value("string")),
"extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
"abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string"))
}
)
else:
features = datasets.Features(
{
"id": datasets.Value("int64"),
"document": datasets.features.Sequence(datasets.Value("string")),
"doc_bio_tags": datasets.features.Sequence(datasets.Value("string")),
"extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
"abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
"other_metadata": datasets.features.Sequence(
{
"text": datasets.features.Sequence(datasets.Value("string")),
"bio_tags": datasets.features.Sequence(datasets.Value("string"))
}
)
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features,
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir['train'],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir['test'],
"split": "test"
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir['valid'],
"split": "valid",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
if self.config.name == "extraction":
# Yields examples as (key, example) tuples
yield key, {
"id": data['paper_id'],
"document": data["document"],
"doc_bio_tags": data.get("doc_bio_tags")
}
elif self.config.name == "generation":
yield key, {
"id": data['paper_id'],
"document": data["document"],
"extractive_keyphrases": data.get("extractive_keyphrases"),
"abstractive_keyphrases": data.get("abstractive_keyphrases")
}
else:
yield key, {
"id": data['paper_id'],
"document": data["document"],
"doc_bio_tags": data.get("doc_bio_tags"),
"extractive_keyphrases": data.get("extractive_keyphrases"),
"abstractive_keyphrases": data.get("abstractive_keyphrases"),
"other_metadata": data["other_metadata"]
}
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