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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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
import glob
import datasets
from datasets.data_files import DataFilesDict
from .scirepeval_test_configs import SCIREPEVAL_CONFIGS
#from datasets.packaged_modules.json import json
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2021}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""
# 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 = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
"second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class Scirepeval(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = SCIREPEVAL_CONFIGS
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(self.config.features), # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
base_url = "https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval"
data_urls = dict()
data_dir = self.config.url if self.config.url else self.config.name
if self.config.task_type in set(["classification", "regression"]):
data_urls.update({"train": f"{base_url}/test/{data_dir}/train.csv"})
data_urls.update({"test": f"{base_url}/test/{data_dir}/test.csv"})
elif self.config.task_type == "metadata":
data_urls.update({"metadata": f"{base_url}/test/{data_dir}/reviewer_metadata.jsonl"})
elif "reviewer_matching" in self.config.name:
data_urls.update({"test_hard": f"{base_url}/test/{data_dir}/test_hard_qrel.jsonl",
"test_soft": f"{base_url}/test/{data_dir}/test_soft_qrel.jsonl"})
else:
data_urls.update({"test": f"{base_url}/test/{data_dir}/test_qrel.jsonl"})
downloaded_files = dl_manager.download_and_extract(data_urls)
splits = []
if self.config.task_type == "metadata":
splits = [datasets.SplitGenerator(
name=datasets.Split("metadata"),
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["metadata"],
"split": "metadata"
},
),
]
elif "reviewer_matching" in self.config.name:
splits = [datasets.SplitGenerator(
name=datasets.Split("test_hard"),
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test_hard"],
"split": "test"
},
),
datasets.SplitGenerator(
name=datasets.Split("test_soft"),
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test_soft"],
"split": "test"
},
)
]
else:
splits = [datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test"],
"split": "test"
},
),
]
if "train" in downloaded_files:
splits += [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["train"],
"split": "train",
},
)]
return splits
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
# data = read_data(filepath)
if self.config.task_type in set(["classification", "regression"]):
import csv
import ast
with open(filepath, encoding="utf-8") as f:
reader = csv.reader(f)
for id_, row in enumerate(reader):
if id_ == 0:
continue
yield id_, {
"paper_id": row[0],
"label": ast.literal_eval(",".join(row[1:])) if self.config.name=="fos" else row[1]
}
elif self.config.task_type == "metadata":
with open(filepath, encoding="utf-8") as f:
for line in f:
d = json.loads(line)
yield d["r_id"], d
else:
with open(filepath, encoding="utf-8") as f:
for i, line in enumerate(f):
d = json.loads(line)
yield i, d
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