Datasets:
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import json
from typing import List
import datasets
_VERSION = "1.0.0"
_CITATION = """\
@inproceedings{decao2021autoregressive,
author = {Nicola {De Cao} and
Gautier Izacard and
Sebastian Riedel and
Fabio Petroni},
title = {Autoregressive Entity Retrieval},
booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
Virtual Event, Austria, May 3-7, 2021},
publisher = {OpenReview.net},
year = {2021},
url = {https://openreview.net/forum?id=5k8F6UU39V},
}"""
class EntityDisambiguationConfig(datasets.BuilderConfig):
"""BuilderConfig for EntityDisambiguation."""
def __init__(self, **kwargs):
"""BuilderConfig for EntityDisambiguation.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(EntityDisambiguationConfig, self).__init__(**kwargs)
self.features = datasets.Features(
{
"id": datasets.Value("string"),
"input": datasets.Value("string"),
"meta": {
"left_context": datasets.Value("string"),
"mention": datasets.Value("string"),
"right_context": datasets.Value("string"),
},
"candidates": datasets.features.Sequence(datasets.Value("string")),
"answer": datasets.Value("string")
}
)
class EntityDisambiguation(datasets.GeneratorBasedBuilder):
"""Entity Disambiguation dataset."""
VERSION = datasets.Version(_VERSION)
BUILDER_CONFIGS = [
EntityDisambiguationConfig(name="ace2004", version=VERSION, description="ACE2004 dataset"),
EntityDisambiguationConfig(name="aida", version=VERSION, description="AIDA dataset"),
EntityDisambiguationConfig(name="aquaint", version=VERSION, description="AQUAINT dataset"),
EntityDisambiguationConfig(name="blink", version=VERSION, description="BLINK dataset"),
EntityDisambiguationConfig(name="clueweb", version=VERSION, description="CWEB dataset"),
EntityDisambiguationConfig(name="msnbc", version=VERSION, description="MSNBC dataset"),
EntityDisambiguationConfig(name="wiki", version=VERSION, description="WIKI dataset"),
]
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
if self.config.name == "blink":
available_splits = ["train", "dev"]
elif self.config.name == "aida":
available_splits = ["train", "dev", "test"]
else:
available_splits = ["test"]
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"filepath": dl_manager.download_and_extract(
f"http://dl.fbaipublicfiles.com/{'KILT' if self.config.name.lower() == 'blink' else 'GENRE'}"
f"/{self.config.name.lower()}-{split}-kilt.jsonl"),
"split": split,
},
)
for split in available_splits
]
def _info(self) -> datasets.DatasetInfo:
return datasets.DatasetInfo(description="Entity Disambiguation dataset", features=self.config.features,
citation=_CITATION)
def _generate_examples(self, filepath: str, split: str):
with open(filepath, encoding="utf-8") as f:
for line in f:
row = json.loads(line)
row["answer"] = row["output"][0]["answer"]
del row["output"]
yield row["id"], row
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