Datasets:
Tasks:
Text Retrieval
Sub-tasks:
entity-linking-retrieval
Languages:
Chinese
Size:
1M - 10M
ArXiv:
License:
File size: 5,329 Bytes
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# coding=utf-8
"""Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark"""
import json
import os
import datasets
_HANSEL_CITATION = """\
@misc{xu2022hansel,
title = {Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark},
author = {Xu, Zhenran and Shan, Zifei and Li, Yuxin and Hu, Baotian and Qin, Bing},
publisher = {arXiv},
year = {2022},
url = {https://arxiv.org/abs/2207.13005}
}
"""
_HANSEL_DESCRIPTION = """\
Hansel is a high-quality human-annotated Chinese entity linking (EL) dataset, used for testing Chinese EL systems' generalization ability to tail entities and emerging entities.
The test set contains Few-shot (FS) and zero-shot (ZS) slices, has 10K examples and uses Wikidata as the corresponding knowledge base.
The training and validation sets are from Wikipedia hyperlinks, useful for large-scale pretraining of Chinese EL systems.
"""
_URLS = {
"train": "hansel-train.jsonl",
"val": "hansel-val.jsonl",
"hansel-fs": "hansel-few-shot-v1.jsonl",
"hansel-zs": "hansel-zero-shot-v1.jsonl",
}
logger = datasets.logging.get_logger(__name__)
class HanselConfig(datasets.BuilderConfig):
"""BuilderConfig for HanselConfig."""
def __init__(self, features, data_url, citation, url, **kwargs):
"""BuilderConfig for Hansel.
Args:
features: `list[string]`, list of the features that will appear in the
feature dict. Should not include "label".
data_url: `string`, url to download the zip file from.
citation: `string`, citation for the data set.
url: `string`, url for information about the data set.
**kwargs: keyword arguments forwarded to super.
"""
super(HanselConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.features = features
self.data_url = data_url
self.citation = citation
self.url = url
class Hansel(datasets.GeneratorBasedBuilder):
"""The Hansel benchmark."""
BUILDER_CONFIGS = [
HanselConfig(
name="wiki",
description=_HANSEL_DESCRIPTION,
features=["id", "text", "start", "end", "mention", "gold_id"],
data_url="https://huggingface.co./datasets/HIT-TMG/Hansel/blob/main/",
citation=_HANSEL_CITATION,
url="https://github.com/HITsz-TMG/Hansel",
),
HanselConfig(
name="hansel-few-shot",
description=_HANSEL_DESCRIPTION,
features=["id", "text", "start", "end", "mention", "gold_id", "source", "domain"],
data_url="https://huggingface.co./datasets/HIT-TMG/Hansel/blob/main/",
citation=_HANSEL_CITATION,
url="https://github.com/HITsz-TMG/Hansel",
),
HanselConfig(
name="hansel-zero-shot",
description=_HANSEL_DESCRIPTION,
features=["id", "text", "start", "end", "mention", "gold_id", "source", "domain"],
data_url="https://huggingface.co./datasets/HIT-TMG/Hansel/blob/main/",
citation=_HANSEL_CITATION,
url="https://github.com/HITsz-TMG/Hansel",
)
]
def _info(self):
features = {feature: datasets.Value("string") for feature in self.config.features}
features["start"] = datasets.Value("int64")
features["end"] = datasets.Value("int64")
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage=self.config.url,
citation=self.config.citation
)
def _split_generators(self, dl_manager):
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
if "hansel-few" in self.config.name:
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": downloaded_files["hansel-fs"],
"split": datasets.Split.TEST,
},
),
]
if "hansel-zero" in self.config.name:
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": downloaded_files["hansel-zs"],
"split": datasets.Split.TEST,
},
),
]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": downloaded_files["train"],
"split": datasets.Split.TRAIN,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": downloaded_files["val"],
"split": datasets.Split.VALIDATION,
},
),
]
def _generate_examples(self, data_file, split):
logger.info("generating examples from = %s", data_file)
with open(data_file, encoding="utf-8") as f:
for idx, line in enumerate(f):
temDict = json.loads(line)
yield idx, temDict
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