# 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 itertools from itertools import islice import datasets # 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={2020} } """ # 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 ProofPile(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 = [ datasets.BuilderConfig(name="arxiv", version=VERSION, description="All of English arxiv.math up to 03/22"), datasets.BuilderConfig(name="books", version=VERSION, description="Open source math textbooks"), datasets.BuilderConfig(name="formal", version=VERSION, description="Formal math libraries"), datasets.BuilderConfig(name="stack-exchange", version=VERSION, description="math overflow and math stack exchange"), datasets.BuilderConfig(name="wiki", version=VERSION, description="wikipedia articles and proofwiki."), datasets.BuilderConfig(name="math-dataset", version=VERSION, description="the MATH dataset."), ] def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset features = datasets.Features( { "config": datasets.Value("string"), "file": datasets.Value("string"), "text": datasets.Value("string"), # These are the features of your dataset like images, labels ... } ) 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=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 # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive self.archived_configs = ["arxiv", "stack-exchange", "math-dataset", "wiki"] if self.config.name in self.archived_configs: if self.config.name=="arxiv": train_paths = [] val_paths = [] for f in os.listdir("arxiv"): f_path = os.path.join("./arxiv", f) # validation set is june of years divisible by 4 if int(f[1])%4==0 and int(f[3])==6: val_paths.append(f_path) else: train_paths.append(f_path) if self.config.name=="stack-exchange": train_paths = [os.path.join("./stack-exchange", x) for x in ["math_overflow.tar.gz", "math_stack_exchange.tar.gz"]] val_paths = [os.path.join("./stack-exchange", x) for x in ["math_overflow_val.tar.gz", "math_stack_exchange_val.tar.gz"]] if self.config.name=="math-dataset": train_paths = ["math-dataset/train.tar.gz"] val_paths = ["math-dataset/val.tar.gz"] if self.config.name=="wiki": train_paths = ["wiki/proofwiki.tar.gz", "wiki/wikipedia.tar.gz"] val_paths = ["wiki/proofwiki_val.tar.gz"] train_files = itertools.chain.from_iterable(dl_manager.iter_archive(x) for x in train_paths) val_files = itertools.chain.from_iterable(dl_manager.iter_archive(x) for x in val_paths) if self.config.name=="math-dataset": train_files = dl_manager.download_and_extract("math-dataset/train.tar.gz") val_files = dl_manager.download_and_extract("math-datset/val.tar.gz") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "data_files": train_files, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "data_files": val_files, }, ), ] else: with open("splits.json") as f: splits = json.load(f) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "data_files": splits[self.config.name + "-train"], }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "data_files": splits[self.config.name + "-valid"], }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, data_files): # 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. key = 0 if self.config.name in self.archived_configs: for name, obj in data_files: text = obj.read() yield key, { "config": self.config.name, "file": name, "text": text, } key += 1 else: for name in data_files: with open(name, encoding="utf-8") as f: text = f.read() # Yields examples as (key, example) tuples yield key, { "config": self.config.name, "file": name, "text": text, } key += 1