import csv import json import os import datasets from typing import List, Any # _SPLIT = ['train', 'test', 'valid'] _CITATION = """\ author: amardeep """ _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": "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") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="extraction", version=VERSION, description="This part of my dataset covers long document"), datasets.BuilderConfig(name="generation", version=VERSION, description="This part of my dataset covers abstract only"), datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers abstract only"), datasets.BuilderConfig(name="ldkp_generation", version=VERSION, description="This part of my dataset covers abstract only"), datasets.BuilderConfig(name="ldkp_extraction", version=VERSION, description="This part of my dataset covers abstract only"), ] DEFAULT_CONFIG_NAME = "extraction" def _info(self): if self.config.name == "extraction" or self.config.name == "ldkp_extraction": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "id": datasets.Value("int64"), "document": datasets.features.Sequence(datasets.Value("string")), "BIO_tags": datasets.features.Sequence(datasets.Value("string")) } )ˀ elif self.config.name == "generation" or self.config.name == "ldkp_generation": features = datasets.Features( { "id": datasets.Value("int64"), "document": datasets.features.Sequence(datasets.Value("string")), "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")) } ) else: features = datasets.Features( { "id": datasets.Value("int64"), "document": datasets.features.Sequence(datasets.Value("string")), "document_tags": datasets.features.Sequence(datasets.Value("string")), "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")), "other_metadata": datasets.features.Sequence( { "text": datasets.features.Sequence(datasets.Value("string")), "tags":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): 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): with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = json.loads(row) if self.config.name == "extraction": # Yields examples as (key, example) tuples yield key, { "id": data['paper_id'] "document": data["document"], "BIO_tags": data["document_tags"] } elif self.config.name == "ldkp_extraction": yield key, { "id": data['paper_id'] "document": data["document"]+data["other_metadata"]['text'], "BIO_tags": data["document_tags"] + data["other_metadata"]['tags'] } elif self.config.name == "ldkp_generation": yield key, { "id": data['paper_id'] "document": data["document"]+data["other_metadata"]['text'], "extractive_keyphrases": data["extractive_keyphrases"], "abstractive_keyphrases": data["abstractive_keyphrases"] } elif self.config.name == "generation": yield key, { "id": data['paper_id'] "document": data["document"], "extractive_keyphrases": data["extractive_keyphrases"], "abstractive_keyphrases": data["abstractive_keyphrases"] } else: yield key, { "id": data['paper_id'] "document": data["document"], "document_tags": data["document_tags"], "extractive_keyphrases": data["extractive_keyphrases"], "abstractive_keyphrases": data["abstractive_keyphrases"], "other_metadata": data["other_metadata"] }