First version of imdb-javanese.

imdb-javanese.py

@@ -0,0 +1,87 @@
+"""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://github.com/w11wo/javanese-nlp/blob/main/imdb-javanese/javanese_imdb_csv.zip?raw=true"
+
+_HOMEPAGE = "https://github.com/w11wo/javanese-nlp"
+
+
+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]}