Datasets:
Tasks:
Text Classification
Formats:
json
Languages:
English
Size:
1K - 10K
Tags:
sentiment-analysis
License:
File size: 2,700 Bytes
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import os
import datasets
from typing import List
import json
logger = datasets.logging.get_logger(__name__)
_CITATION = """
"""
_DESCRIPTION = """
This is the dataset repository for BESSTIE Dataset.
The dataset can help build text classification models for sarcasm detection and sentiment analysis for low resource languages.
"""
class BESSTIEConfig(datasets.BuilderConfig):
"""BuilderConfig for BESSTIE"""
def __init__(self, **kwargs):
"""BuilderConfig for BESSTIE.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(BESSTIEConfig, self).__init__(**kwargs)
class BESSTIEConfig(datasets.GeneratorBasedBuilder):
"""BESSTIE dataset."""
BUILDER_CONFIGS = [
BESSTIEConfig(name="BESSTIE", version=datasets.Version("0.0.1"), description="BESSTIE dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"text": datasets.Value("string"),
"sentiment_label": datasets.Value("int32"),
}
),
supervised_keys=None,
citation=_CITATION,
)
_URL = "https://huggingface.co./datasets/mindhunter23/BESSTIE-google-sentiment-au/blob/main/data/"
_URLS = {
"train": _URL + "google-sentiment-au-train.jsonl",
"dev": _URL + "google-sentiment-au-valid.jsonl",
# "test": _URL + "google-sentiment-au-test.jsonl"
}
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls_to_download = self._URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
# datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
]
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) as f:
for line in f:
object = json.loads(line.strip())
id_ = int(object['id'])
yield id_, {
"id": id_,
"text": object['text'],
"sentiment_label": int(object['sentiment_label']),
}
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