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import json
import pandas as pd
import datasets
import csv
from datasets.tasks import Summarization
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
Aihub Document summarization data
"""
_URL = "https://huggingface.co/datasets/metamong1/summarization_optimization/resolve/main/"
_URLS = {
"train_data": _URL + "train_data.csv",
"validation_data": _URL + "validation_data.csv",
}
class SummarizationOptimization(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="Summarization Part Data",
version=datasets.Version("1.0.0", ""),
description="Text Summarization & Generation Title for optimization",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"doc_id": datasets.Value("string"),
"title": datasets.Value("string"),
"text": datasets.Value("string"),
"doc_type": datasets.Value("string"),
"file": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://huggingface.co/datasets/metamong1/summarization_optimization",
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train_data"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation_data"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath, newline='', encoding="utf-8") as csvfile:
reader = csv.reader(csvfile, delimiter=",")
feature_name = next(reader)
idx = 0
for row in reader:
features = {
"doc_id" : row[1],
"title" : row[2],
"text" : row[3],
"doc_type" : row[4],
"file" : row[5],
}
yield idx, features
idx += 1 |