inspec.py

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"))
                    
                }
            )
        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"]
                    }