# 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_configs import SCIREPEVAL_CONFIGS #from datasets.packaged_modules.json import json from datasets.utils.logging import get_logger logger = get_logger(__name__) # 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=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" data_urls = dict() data_dir = self.config.url if self.config.url else self.config.name if self.config.is_training: data_urls = {"train": f"{base_url}/train/{data_dir}/train.jsonl"} if "refresh" not in self.config.name: data_urls.update({"val": f"{base_url}/train/{data_dir}/val.jsonl"}) if "cite_prediction" not in self.config.name: data_urls.update({"test": f"{base_url}/test/{data_dir}/meta.jsonl"}) # print(data_urls) downloaded_files = dl_manager.download_and_extract(data_urls) # print(downloaded_files) 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.append( datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": downloaded_files["train"], "split": "train", }, )) if "val" in downloaded_files: splits.append(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"]) logger.warning(filepath) 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 "cite_prediction_" not in self.config.name 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