import csv import json import os import datasets from typing import List, Any # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ author: amardeep """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This new dataset is designed to solve kp 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 = { "test": "test.jsonl", "train": "train.jsonl", "valid": "valid.jsonl" } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class TestLDKP(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="ldkp", version=VERSION, description="This part of my dataset covers long document"), datasets.BuilderConfig(name="normal", version=VERSION, description="This part of my dataset covers abstract only"), ] DEFAULT_CONFIG_NAME = "normal" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset if self.config.name == "ldkp": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "text": datasets.features.Sequence(datasets.Value("string")), "BIO_tags": datasets.features.Sequence(datasets.Value("string")) #"text": datasets.features.Sequence({'txt':datasets.Value("string")}), #"BIO_tags": datasets.features.Sequence({'tag':datasets.Value("string")}) #"text": datasets.Value("string"), #"BIO_tags": datasets.Value("string") # "answer": datasets.Value("string") # These are the features of your dataset like images, labels ... } ) else: # This is an example to show how to have different features for "first_domain" and "second_domain" features = datasets.Features( { "text": datasets.features.Sequence(datasets.Value("string")), "BIO_tags": datasets.features.Sequence(datasets.Value("string")) # "second_domain_answer": 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 # urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['train'], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['test'], "split": "test" }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['valid'], "split": "valid", }, ), ] # 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. with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = json.loads(row) if self.config.name == "ldkp": # Yields examples as (key, example) tuples yield key, { #"text": {"txt":data["abstract"]+data["other_sec"]}, #"BIO_tags": {'tag':data["abstract_tags"] + data["other_sec_tags"]} "text": data["abstract"]+data["other_sec"], "BIO_tags": data["abstract_tags"] + data["other_sec_tags"] # "answer": "" if split == "test" else data["answer"], } else: yield key, { "text": data["abstract"], "BIO_tags": data["abstract_tags"] # "second_domain_answer": "" if split == "test" else data["second_domain_answer"], }