File size: 3,552 Bytes
3d4e2ee 03496d5 3d4e2ee 03496d5 3d4e2ee 03496d5 3d4e2ee 03496d5 3d4e2ee 03496d5 3d4e2ee 03496d5 3d4e2ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | # 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.
"""AI-MO Olympiad Reference Dataset"""
import re
import json
from pathlib import Path
import datasets
from huggingface_hub import HfApi
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
class OlympiadReferenceDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.1")
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._hfapi = HfApi()
self.pattern = re.compile(r'.*/segmented/[^/]+\.jsonl$')
def _info(self):
features = datasets.Features(
{
"problem_type": datasets.Value("string"),
"problem_label": datasets.Value("string"),
"problem": datasets.Value("string"),
"solution": datasets.Value("string"),
"year": datasets.Value("int32"),
"tier": datasets.Value("int32"),
"resource_path": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_root_path = Path(dl_manager._base_path)
repo_files = self._hfapi.list_repo_files(repo_id="AI-MO/olympiads-ref", repo_type="dataset")
seg_jsonl_files = [s for s in repo_files if self.pattern.match(s)]
data_files = [(sjf, dl_manager.extract(dl_manager.download(data_root_path / sjf))) for sjf in seg_jsonl_files]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_files": data_files,
"split": "train",
},
)
]
def _generate_examples(self, data_files, split):
key = 0
for resource_path, file in data_files:
with open(file, "r", encoding="utf-8") as f:
for line in f:
data = json.loads(line)
yield key, {
"problem_type": data.get("problem_type"),
"problem_label": data.get("problem_label") or data.get("label"),
"problem": data.get("problem"),
"solution": data.get("solution"),
"year": data.get("year"),
"tier": data.get("tier"),
"resource_path": resource_path
}
key += 1
|