Upload imdb_jv.py with huggingface_hub

imdb_jv.py

@@ -0,0 +1,149 @@
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+from nusacrowd.utils import schemas
+import pandas as pd
+
+_CITATION = """\
+@inproceedings{wongso2021causal,
+  title={Causal and masked language modeling of Javanese language using transformer-based architectures},
+  author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
+  booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
+  pages={1--7},
+  year={2021},
+  organization={IEEE}
+}
+"""
+
+_DATASETNAME = "imdb_jv"
+
+_DESCRIPTION = """\
+Javanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/w11wo/imdb-javanese"
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_LICENSE = "Unknown"
+
+_URLS = {
+    _DATASETNAME: "https://huggingface.co/datasets/w11wo/imdb-javanese/resolve/main/javanese_imdb_csv.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+class IMDbJv(datasets.GeneratorBasedBuilder):
+    """Javanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="imdb_jv_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="imdb_jv source schema",
+            schema="source",
+            subset_id="imdb_jv",
+        ),
+        NusantaraConfig(
+            name="imdb_jv_nusantara_text",
+            version=datasets.Version(_NUSANTARA_VERSION),
+            description="imdb_jv Nusantara schema",
+            schema="nusantara_text",
+            subset_id="imdb_jv",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "imdb_jv_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+               {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value("string")
+               }
+            )
+        elif self.config.schema == "nusantara_text":
+            features = schemas.text_features(['1', '0', '-1'])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        data_dir = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME]))
+
+        data_files = {
+            "train": "javanese_imdb_train.csv",
+            "unsupervised": "javanese_imdb_unsup.csv",
+            "test": "javanese_imdb_test.csv",
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, data_files["train"]),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="unsupervised",
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, data_files["unsupervised"]),
+                    "split": "unsupervised",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, data_files["test"]),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        if self.config.schema == "source":
+            data = pd.read_csv(filepath)
+            length = len(data['label'])
+            for id in range(length):
+                ex = {
+                    "id": str(id),
+                    "text": data['text'][id],
+                    "label": data['label'][id],
+                }
+                yield id, ex
+
+        elif self.config.schema == "nusantara_text":
+            data = pd.read_csv(filepath)
+            length = len(data['label'])
+            for id in range(length):
+                ex = {
+                    "id": str(id),
+                    "text": data['text'][id],
+                    "label": data['label'][id],
+                }
+                yield id, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")