diff --git a/seacrowd/sea_datasets/emotes_3k/__init__.py b/seacrowd/sea_datasets/emotes_3k/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/seacrowd/sea_datasets/emotes_3k/emotes_3k.py b/seacrowd/sea_datasets/emotes_3k/emotes_3k.py new file mode 100644 index 000000000..f980cbd03 --- /dev/null +++ b/seacrowd/sea_datasets/emotes_3k/emotes_3k.py @@ -0,0 +1,238 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +English-Tagalog Parallel Dataset intended for two tasks: +1. Moral Text Classification +2. Instruction Tuning +""" +import json +from pathlib import Path +from typing import Dict, List, Tuple + +import datasets + +from seacrowd.utils import schemas +from seacrowd.utils.configs import SEACrowdConfig +from seacrowd.utils.constants import Licenses, Tasks + +_CITATION = """\ +@inproceedings{Catapang:2023, + author = {Catapang, Jasper Kyle and Visperas, Moses}, + title = {Emotion-based Morality in Tagalog and English Scenarios (EMoTES-3K): A Parallel Corpus for Explaining (Im)morality of Actions}, + booktitle = {Proceedings of the Joint 3rd NLP4DH and 8th IWCLUL}, + pages = {1--6}, + month = {December 1-3}, + year = {2023}, + organization = {Association for Computational Linguistics}, +} +""" + +_DATASETNAME = "emotes_3k" + +_DESCRIPTION = """\ +This dataset is used on the paper "Emotion-based Morality in Tagalog and English Scenarios (EMoTES-3K): A Parallel Corpus for Explaining (Im)morality of Actions" +This dataset is designed for for two tasks: +1. Moral Text Classification +2. Instruction Tuning +""" + +_HOMEPAGE = "https://huggingface.co/datasets/NLPinas/EMoTES-3K" + +_LANGUAGES = ["tgl"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) + +_LICENSE = Licenses.UNKNOWN.value + +_LOCAL = False + +_URLS = { + _DATASETNAME: "https://huggingface.co/datasets/NLPinas/EMoTES-3K/resolve/main/EMoTES-3K.jsonl?download=true", +} + +_SUPPORTED_TASKS = [Tasks.MORALITY_CLASSIFICATION, Tasks.INSTRUCTION_TUNING] # Roberta moral or immoral classification # FLAN-T5 Training + +_SOURCE_VERSION = "1.0.0" + +_SEACROWD_VERSION = "1.0.0" + + +class Emotes3KDatasets(datasets.GeneratorBasedBuilder): + """ + Emotes3K consists of one human annotated dataset for the purpose of morality classification and instruction tuning. + """ + + SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) + SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) + + BUILDER_CONFIGS = [ + SEACrowdConfig( + name=f"{_DATASETNAME}_source", + version=SOURCE_VERSION, + description=f"{_DATASETNAME} source schema", + schema="source", + subset_id=_DATASETNAME, + ), + SEACrowdConfig( + name=f"{_DATASETNAME}_seacrowd_text", + version=SEACROWD_VERSION, + description=f"{_DATASETNAME} SEACrowd schema", + schema="seacrowd_text", + subset_id=_DATASETNAME, + ), + SEACrowdConfig( + name=f"{_DATASETNAME}_eng_seacrowd_text", + version=SEACROWD_VERSION, + description=f"{_DATASETNAME} SEACrowd schema", + schema="seacrowd_text", + subset_id=_DATASETNAME, + ), + SEACrowdConfig( + name=f"{_DATASETNAME}_tgl_seacrowd_text", + version=SEACROWD_VERSION, + description=f"{_DATASETNAME} SEACrowd schema", + schema="seacrowd_text", + subset_id=_DATASETNAME, + ), + SEACrowdConfig( + name=f"{_DATASETNAME}_seacrowd_t2t", + version=SEACROWD_VERSION, + description=f"{_DATASETNAME} SEACrowd schema", + schema="seacrowd_t2t", + subset_id=_DATASETNAME, + ), + SEACrowdConfig( + name=f"{_DATASETNAME}_eng_seacrowd_t2t", + version=SEACROWD_VERSION, + description=f"{_DATASETNAME} SEACrowd schema", + schema="seacrowd_t2t", + subset_id=_DATASETNAME, + ), + SEACrowdConfig( + name=f"{_DATASETNAME}_tgl_seacrowd_t2t", + version=SEACROWD_VERSION, + description=f"{_DATASETNAME} SEACrowd schema", + schema="seacrowd_t2t", + subset_id=_DATASETNAME, + ), + ] + + DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" + + def _info(self) -> datasets.DatasetInfo: + if self.config.schema == "source": + features = datasets.Features( + { + "entry_id": datasets.Value("string"), + "Filipino": datasets.Value("string"), + "English": datasets.Value("string"), + "Annotation": datasets.ClassLabel(names=["Moral", "Immoral"]), + "Explanation": datasets.Value("string"), + "Personality Traits": datasets.Value("string"), + "Topic": datasets.Value("string"), + "Topic Name": datasets.Value("string"), + } + ) + # For example seacrowd_kb, seacrowd_t2t + elif self.config.schema == "seacrowd_text": + features = schemas.text.features(["Moral", "Immoral"]) + elif self.config.schema == "seacrowd_t2t": + features = schemas.text_to_text.features + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + """Returns SplitGenerators.""" + urls = _URLS[_DATASETNAME] + path = dl_manager.download_and_extract(urls) + + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": path, + "split": "train", + }, + ), + ] + + def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: + """Yields examples as (key, example) tuples.""" + + with open(filepath, "r", encoding="utf-8") as file: + for line in file: + # Use json.loads to parse each line as a JSON object + data = json.loads(line.strip()) + + if self.config.schema == "source": + yield ( + data["entry_id"], + { + "entry_id": data["entry_id"], + "Filipino": data["Filipino"], + "English": data["English"], + "Annotation": data["Annotation"], + "Explanation": data["Explanation"], + "Personality Traits": data["Personality Traits"], + "Topic": data["Topic"], + "Topic Name": data["Topic Name"], + }, + ) + elif self.config.schema == "seacrowd_text": + if "eng" in self.config.name or self.config.name == "emotes_3k_seacrowd_text": + yield ( + data["entry_id"], + { + "id": data["entry_id"], + "text": data["English"], + "label": data["Annotation"], + }, + ) + elif "tgl" in self.config.name: + yield ( + data["entry_id"], + { + "id": data["entry_id"], + "text": data["Filipino"], + "label": data["Annotation"], + }, + ) + elif self.config.schema == "seacrowd_t2t": + if "eng" in self.config.name or self.config.name == "emotes_3k_seacrowd_t2t": + yield ( + data["entry_id"], + { + "id": data["entry_id"], + "text_1": "Explain the morality of this scenario\n" + data["English"], + "text_2": data["Explanation"], + "text_1_name": "prompt", + "text_2_name": "system", + }, + ) + elif "tgl" in self.config.name: + yield ( + data["entry_id"], + { + "id": data["entry_id"], + "text_1": "Explain the morality of this scenario\n" + data["Filipino"], + "text_2": data["Explanation"], + "text_1_name": "prompt", + "text_2_name": "system", + }, + )