Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Javanese
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
machine-generated
Annotations Creators:
found
Source Datasets:
original
Tags:
License:
| """Javanese IMDB movie reviews dataset.""" | |
| from __future__ import absolute_import, division, print_function | |
| import csv | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @InProceedings{maas-EtAl:2011:ACL-HLT2011, | |
| author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher}, | |
| title = {Learning Word Vectors for Sentiment Analysis}, | |
| booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, | |
| month = {June}, | |
| year = {2011}, | |
| address = {Portland, Oregon, USA}, | |
| publisher = {Association for Computational Linguistics}, | |
| pages = {142--150}, | |
| url = {http://www.aclweb.org/anthology/P11-1015} | |
| } | |
| """ | |
| _DESCRIPTION = """ | |
| Large Movie Review Dataset translated to Javanese. | |
| This is a dataset for binary sentiment classification containing substantially | |
| more data than previous benchmark datasets. We provide a set of 25,000 highly | |
| polar movie reviews for training, and 25,000 for testing. There is additional | |
| unlabeled data for use as well. We translated the original IMDB Dataset to | |
| Javanese using the multi-lingual MarianMT Transformer model from | |
| `Helsinki-NLP/opus-mt-en-mul`. | |
| """ | |
| _URL = "https://huggingface.co/datasets/w11wo/imdb-javanese/resolve/main/javanese_imdb_csv.zip" | |
| _HOMEPAGE = "https://github.com/w11wo/nlp-datasets#javanese-imdb" | |
| class JavaneseImdbReviews(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "text": datasets.Value("string"), | |
| "label": datasets.ClassLabel(names=["0", "1", "-1"]), | |
| } | |
| ), | |
| citation=_CITATION, | |
| homepage=_HOMEPAGE, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| dl_path = dl_manager.download_and_extract(_URL) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(dl_path, "javanese_imdb_train.csv") | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": os.path.join(dl_path, "javanese_imdb_test.csv") | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split("unsupervised"), | |
| gen_kwargs={ | |
| "filepath": os.path.join(dl_path, "javanese_imdb_unsup.csv") | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """Yields examples.""" | |
| with open(filepath, encoding="utf-8") as f: | |
| reader = csv.reader(f, delimiter=",") | |
| for id_, row in enumerate(reader): | |
| if id_ == 0: | |
| continue | |
| yield id_, {"label": row[0], "text": row[1]} | |