import csv import json import os import datasets from typing import List, Any # _SPLIT = ['train', 'test', 'valid'] _CITATION = """\ TBA """ _DESCRIPTION = """\ This new dataset is designed to solve kp NLP task and is crafted with a lot of care. """ _HOMEPAGE = "" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here _URLS = { "test": ["data/test.jsonl"], "train": ["train.jsonl"], "valid": ["data/valid.jsonl"], } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class LDKP3k(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="small", version=VERSION, description="This part of my dataset covers long document"), datasets.BuilderConfig(name="medium", version=VERSION, description="This part of my dataset covers abstract only"), datasets.BuilderConfig(name="large", version=VERSION, description="This part of my dataset covers abstract only") ] DEFAULT_CONFIG_NAME = "small" def _info(self): #print(os.listdir()) #_URLS['train']=[os.path.join('data/'+self.config.name,filename) for filename in os.listdir('data/'+self.config.name+"/") if filename.startswith('train') and filename.endswith('.jsonl')] _URLS['train']=["data/"+self.config.name+"/train.jsonl"] if self.config.name =='large': _URLS['train']+= ["data/"+self.config.name+"/train_"+str(x)+".jsonl" for x in range(1,5)] features = datasets.Features( { "id": datasets.Value("string"), "sections": datasets.features.Sequence(datasets.Value("string")), "sec_text": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))), "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), "sec_bio_tags": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))) } ) 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, homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): print(os.listdir()) 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={ "filepaths": data_dir['train'], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepaths": data_dir['test'], "split": "test" }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepaths": data_dir['valid'], "split": "valid", }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepaths, split): for filepath in filepaths: with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = json.loads(row) yield key, { "id": data['paper_id'], "sections": data["sections"], "sec_text": data["sec_text"], "extractive_keyphrases": data["extractive_keyphrases"], "abstractive_keyphrases": data["abstractive_keyphrases"], "sec_bio_tags": data["sec_bio_tags"] }