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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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_DATASETNAME = "mdia" |
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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] |
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_SEACROWD_SCHEMA = {task.value: f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS} |
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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"]: |
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features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]] |
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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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"""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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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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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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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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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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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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