Datasets:
update for dataset name
Browse files- dataset_infos.json +1 -1
- kp20k.py +4 -16
dataset_infos.json
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{"
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{"raw": {"description": "KP20k dataset for keyphrase extraction and generation in scientific paper.\n", "citation": "@InProceedings{meng-EtAl:2017:Long,\n author = {Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu},\n title = {Deep Keyphrase Generation},\n booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n month = {July},\n year = {2017},\n address = {Vancouver, Canada},\n publisher = {Association for Computational Linguistics},\n pages = {582--592},\n url = {http://aclweb.org/anthology/P17-1054}\n}\n", "homepage": "http://memray.me/uploads/acl17-keyphrase-generation.pdf", "license": "MIT LICENSE", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "keyphrases": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "prmu": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "kp20k", "config_name": "raw", "version": {"version_str": "0.0.1", "description": "", "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 654714676, "num_examples": 530809, "dataset_name": "kp20k"}, "test": {"name": "test", "num_bytes": 24675779, "num_examples": 20000, "dataset_name": "kp20k"}, "validation": {"name": "validation", "num_bytes": 24657665, "num_examples": 20000, "dataset_name": "kp20k"}}, "download_checksums": {"test.json": {"num_bytes": 25255559, "checksum": "3b6caeb55eaf941deb11f9e5152494310db2ac5970194e722798e3e035855561"}, "train.json": {"num_bytes": 670087948, "checksum": "59a20765c76126945e9eb298d7837175e886403f113c1a23c0cab7dc3cd9496d"}, "validation.json": {"num_bytes": 25237497, "checksum": "1c0ec7541c24c81b44c11c8cc5a0cbda88956a39bf94552dd03dcdf7fb25dd67"}}, "download_size": 720581004, "post_processing_size": null, "dataset_size": 704048120, "size_in_bytes": 1424629124}}
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kp20k.py
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import csv
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import json
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import os
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logger = datasets.logging.get_logger(__name__)
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import datasets
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_CITATION = """\
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@InProceedings{meng-EtAl:2017:Long,
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author = {Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu},
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"validation": "validation.json"
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}
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class KP20kConfig(datasets.BuilderConfig):
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def __init__(self, **kwargs):
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super(KP20kConfig, self).__init__(**kwargs)
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# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
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class KP20k(datasets.GeneratorBasedBuilder):
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# data = datasets.load_dataset('my_dataset', 'first_domain')
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# data = datasets.load_dataset('my_dataset', 'second_domain')
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BUILDER_CONFIGS = [
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name="raw",
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version=VERSION,
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description="This part of the dataset covers the raw data.",
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),
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]
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#DEFAULT_CONFIG_NAME = "raw" # It's not mandatory to have a default configuration. Just use one if it make sense.
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir
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"split": "train",
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},
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),
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir
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"split": "test"
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},
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),
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir
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"split": "validation",
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},
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),
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import csv
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import json
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import os
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import datasets
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_CITATION = """\
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@InProceedings{meng-EtAl:2017:Long,
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author = {Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu},
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"validation": "validation.json"
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}
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# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
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class KP20k(datasets.GeneratorBasedBuilder):
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# data = datasets.load_dataset('my_dataset', 'first_domain')
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# data = datasets.load_dataset('my_dataset', 'second_domain')
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"),
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]
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#DEFAULT_CONFIG_NAME = "raw" # It's not mandatory to have a default configuration. Just use one if it make sense.
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir["train"]),
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"split": "train",
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},
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),
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir["test"]),
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"split": "test"
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},
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),
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir["validation"]),
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"split": "validation",
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},
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),
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