# 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