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"""A dataset script that will hit Snowflake DB and return the results."""
import snowflake.connector as connector
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great 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: Name of the dataset usually match the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']),
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
conn = connector.connect(
user='rajiv',
password='Password123!',
account='VUA92284',
warehouse='RAJIV',
database='HUGGINGFACE',
schema='PUBLIC',
role = 'RAJIV'
)
curr = conn.cursor()
sql = "select * from EMOTION"
curr = curr.execute(sql)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"cursor": curr},
)
]
def _generate_examples(self, cursor):
for i, ex in enumerate(cursor):
yield str(i), {
"text": ex[0],
"label": ex[1],
}
# Probably not necessary but just in case...we close the connection which we can find within the cursor object
cursor.connection.close()
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