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mdia.py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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+
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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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import pandas as pd
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from seacrowd.utils.configs import SEACrowdConfig
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from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
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Licenses, Tasks)
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_CITATION = """\
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@misc{zhang2022mdia,
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title={MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages},
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author={Qingyu Zhang and Xiaoyu Shen and Ernie Chang and Jidong Ge and Pengke Chen},
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year={2022},
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eprint={2208.13078},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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"""
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+
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_DATASETNAME = "mdia"
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+
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_DESCRIPTION = """\
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This is a multilingual benchmark for dialogue generation containing real-life
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Reddit conversations (parent and response comment pairs) in 46 languages,
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including Indonesian, Tagalog and Vietnamese. English translations are also
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provided for comments.
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"""
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_HOMEPAGE = "https://github.com/DoctorDream/mDIA"
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_LANGUAGES = ["ind", "tgl", "vie"]
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_LICENSE = Licenses.CC_BY_4_0.value
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_LOCAL = False
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_URLS = {
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"raw": "https://github.com/DoctorDream/mDIA/raw/master/datasets/raw.zip",
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"translated": "https://github.com/DoctorDream/mDIA/raw/master/datasets/translated.zip",
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}
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_SUPPORTED_TASKS = [Tasks.DIALOGUE_SYSTEM, Tasks.MACHINE_TRANSLATION] # DS, MT
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_SEACROWD_SCHEMA = {task.value: f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS} # t2t
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_SUBSETS = [
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"ind_dialogue",
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"ind_eng",
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"tgl_dialogue",
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"tgl_eng",
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"vie_dialogue",
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"vie_eng",
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]
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_SOURCE_VERSION = "1.0.0"
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_SEACROWD_VERSION = "2024.06.20"
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class MdiaDataset(datasets.GeneratorBasedBuilder):
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"""Multilingual benchmark for dialogue generation containing real-life Reddit conversations"""
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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BUILDER_CONFIGS = []
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for subset in _SUBSETS:
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if "dialogue" in subset:
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BUILDER_CONFIGS += [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{subset}_source",
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version=SOURCE_VERSION,
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description=f"{_DATASETNAME} {subset} source schema",
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schema="source",
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subset_id=subset,
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),
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA['DS']}",
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version=SEACROWD_VERSION,
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description=f"{_DATASETNAME} {subset} SEACrowd schema",
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schema=_SEACROWD_SCHEMA["DS"],
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subset_id=subset,
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),
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]
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else:
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BUILDER_CONFIGS += [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{subset}_source",
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version=SOURCE_VERSION,
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description=f"{_DATASETNAME} {subset} source schema",
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schema="source",
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subset_id=subset,
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),
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA['MT']}",
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version=SEACROWD_VERSION,
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description=f"{_DATASETNAME} {subset} SEACrowd schema",
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schema=_SEACROWD_SCHEMA["MT"],
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subset_id=subset,
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),
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]
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{_SUBSETS[0]}_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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"lang": datasets.Value("string"),
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"title": datasets.Value("string"),
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"source_body": datasets.Value("string"),
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"target_body": datasets.Value("string"),
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"link_id": datasets.Value("string"),
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"source_id": datasets.Value("string"),
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"target_id": datasets.Value("string"),
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"translated_source_body": datasets.Value("string"),
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"translated_target_body": datasets.Value("string"),
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}
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)
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elif self.config.schema == _SEACROWD_SCHEMA["DS"]: # same schema with _SEACROWD_SCHEMA["MT"]
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features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]] # text2text_features
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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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+
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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"""Returns SplitGenerators."""
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lang_map = {"ind": "id", "tgl": "tl", "vie": "vi"}
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lang = lang_map[self.config.subset_id.split("_")[0]]
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+
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data_url = _URLS["translated"]
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data_dir = Path(dl_manager.download_and_extract(data_url)) / "translated"
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data_path = "{split}_data/{lang}2en_{split}.csv"
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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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"data_path": data_dir / data_path.format(split="train", lang=lang),
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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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"data_path": data_dir / data_path.format(split="test", lang=lang),
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+
},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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+
gen_kwargs={
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"data_path": data_dir / data_path.format(split="eval", lang=lang),
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},
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),
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]
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+
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def _generate_examples(self, data_path: Path) -> Tuple[int, Dict]:
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"""Yields examples as (key, example) tuples."""
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df = pd.read_csv(data_path)
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# source schema
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if self.config.schema == "source":
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for i, row in df.iterrows():
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yield i, {
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"lang": row["lang"],
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"title": row["title"],
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"source_body": row["source_body"],
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"target_body": row["target_body"],
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"link_id": row["link_id"],
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"source_id": row["source_id"],
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"target_id": row["target_id"],
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"translated_source_body": row["translated_source_body"],
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"translated_target_body": row["translated_target_body"],
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}
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+
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# t2t schema for dialogue
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elif "dialogue" in self.config.subset_id:
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for i, row in df.iterrows():
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yield i, {
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"id": str(i),
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"text_1": row["source_body"],
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"text_2": row["target_body"],
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"text_1_name": "source_body",
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"text_2_name": "target_body",
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}
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# t2t schema for machine translation
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elif "eng" in self.config.subset_id:
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for i, row in df.iterrows():
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for j in range(2):
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idx = i * 2 + j
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if j == 0:
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yield idx, {
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"id": str(idx),
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"text_1": row["source_body"],
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"text_2": row["translated_source_body"],
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"text_1_name": "source_body",
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"text_2_name": "translated_source_body",
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}
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else:
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yield idx, {
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"id": str(idx),
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"text_1": row["target_body"],
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"text_2": row["translated_target_body"],
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"text_1_name": "target_body",
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"text_2_name": "translated_target_body",
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}
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