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# 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.
"""personalized_passkey_retrieval: a synthetic dataset to evaluate long-context embeddings"""
import json
import gzip
import datasets
logger = datasets.logging.get_logger(__name__)
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{Wang2023ImprovingTE,
title={Improving Text Embeddings with Large Language Models},
author={Liang Wang and Nan Yang and Xiaolong Huang and Linjun Yang and Rangan Majumder and Furu Wei},
year={2023},
}
"""
# You can copy an official description
_DESCRIPTION = """\
This dataset contains synthetic data for personalized passkey retrieval.
It is only intended for evaluation purposes, you should not use it for training.
"""
_URLS = {
"train": "personalized_passkey_retrieval.jsonl.gz"
}
class Query2docMsmarco(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name='plain_text', version=VERSION, description='plain text')
]
def _info(self):
features = datasets.Features(
{
"query": datasets.Value("string"),
"candidates": datasets.features.Sequence(datasets.Value("string")),
"label": datasets.Value("int32"),
"context_length": datasets.Value("int32"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download(_URLS)
print(downloaded_files)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["train"],
"split": "train",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
id_ = 0
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
for line in f:
if line:
example = json.loads(line)
yield id_, {
"query": example["query"],
"candidates": example["candidates"],
"label": example["label"],
"context_length": example["context_length"],
}
id_ += 1
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