| # 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 datasets | |
| from datasets.data_files import DataFilesDict | |
| from .super_scirep_config import SUPERSCIREPEVAL_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 SuperSciRep(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 = SUPERSCIREPEVAL_CONFIGS | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=self.config.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="", | |
| # License for the dataset if available | |
| license=self.config.license, | |
| # Citation for the dataset | |
| citation=self.config.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" | |
| base_url = "https://hhy-tue.s3.eu-central-1.amazonaws.com/data/super_scirep" | |
| data_urls = dict() | |
| # data_dir = self.config.url if self.config.url else self.config.name | |
| data_dir = self.config.name | |
| if self.config.is_training: | |
| data_urls = {"train": f"{base_url}/{data_dir}/train.jsonl", | |
| "val": f"{base_url}/{data_dir}/validation.jsonl"} | |
| if "cite_prediction" not in self.config.name: | |
| data_urls.update({"test": f"{base_url}/{data_dir}/evaluation.jsonl"}) | |
| print(data_urls) | |
| downloaded_files = dl_manager.download_and_extract(data_urls) | |
| splits = [] | |
| if "test" in downloaded_files: | |
| splits = [datasets.SplitGenerator( | |
| name=datasets.Split("evaluation"), | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": downloaded_files["test"], | |
| "split": "evaluation" | |
| }, | |
| ), | |
| ] | |
| 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", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": downloaded_files["val"], | |
| "split": "validation", | |
| }) | |
| ] | |
| return splits | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| def read_data(data_path): | |
| task_data = [] | |
| try: | |
| task_data = json.load(open(data_path, "r", encoding="utf-8")) | |
| except: | |
| with open(data_path) as f: | |
| task_data = [json.loads(line) for line in f] | |
| if type(task_data) == dict: | |
| task_data = list(task_data.values()) | |
| return task_data | |
| # 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) | |
| seen_keys = set() | |
| IGNORE = set(["n_key_citations", "session_id", "user_id", "user"]) | |
| with open(filepath, encoding="utf-8") as f: | |
| for line in f: | |
| d = json.loads(line) | |
| d = {k: v for k, v in d.items() if k not in IGNORE} | |
| key = "doc_id" if self.config.name != "cite_prediction_new" else "corpus_id" | |
| if self.config.task_type == "proximity": | |
| if "cite_prediction" in self.config.name: | |
| if "arxiv_id" in d["query"]: | |
| for item in ["query", "pos", "neg"]: | |
| del d[item]["arxiv_id"] | |
| del d[item]["doi"] | |
| if "fos" in d["query"]: | |
| del d["query"]["fos"] | |
| if "score" in d["pos"]: | |
| del d["pos"]["score"] | |
| yield str(d["query"][key]) + str(d["pos"][key]) + str(d["neg"][key]), d | |
| else: | |
| if d["query"][key] not in seen_keys: | |
| seen_keys.add(d["query"][key]) | |
| yield str(d["query"][key]), d | |
| else: | |
| if d[key] not in seen_keys: | |
| seen_keys.add(d[key]) | |
| if self.config.task_type != "search": | |
| if "corpus_id" not in d: | |
| d["corpus_id"] = None | |
| if "scidocs" in self.config.name: | |
| if "cited by" not in d: | |
| d["cited_by"] = [] | |
| if type(d["corpus_id"]) == str: | |
| d["corpus_id"] = None | |
| yield d[key], d |