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