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+ import xml.etree.ElementTree as ET
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+ from pathlib import Path
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+ from typing import Dict, List, Tuple
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+
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+ import datasets
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+ import pandas as pd
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+
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+ from seacrowd.utils import schemas
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+ from seacrowd.utils.configs import SEACrowdConfig
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+ from seacrowd.utils.constants import Licenses, Tasks
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+
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+ _CITATION = """\
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+ @inproceedings{nguyen-etal-2016-vsolscsum,
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+ title = "{VS}o{LSCS}um: Building a {V}ietnamese Sentence-Comment Dataset for Social Context Summarization",
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+ author = "Nguyen, Minh-Tien and
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+ Lai, Dac Viet and
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+ Do, Phong-Khac and
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+ Tran, Duc-Vu and
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+ Nguyen, Minh-Le",
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+ editor = "Hasida, Koiti and
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+ Wong, Kam-Fai and
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+ Calzorari, Nicoletta and
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+ Choi, Key-Sun",
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+ booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
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+ month = dec,
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+ year = "2016",
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+ address = "Osaka, Japan",
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+ publisher = "The COLING 2016 Organizing Committee",
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+ url = "https://aclanthology.org/W16-5405",
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+ pages = "38--48",
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+ }
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+ """
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+
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+ _DATASETNAME = "vsolscsum"
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+
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+ _DESCRIPTION = """
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+ The Vietnamese dataset for social context summarization \
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+ The dataset contains 141 open-domain articles along with \
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+ 3,760 sentences, 2,448 extracted standard sentences and \
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+ comments as standard summaries and 6,926 comments in 12 \
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+ events. This dataset was manually annotated by human. \
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+ Note that the extracted standard summaries also include comments.\
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+ The label of a sentence or comment was generated based on the \
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+ voting among social annotators. For example, given a sentence, \
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+ each annotator makes a binary decision in order to indicate \
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+ that whether this sentence is a summary candidate (YES) or not \
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+ (NO). If three annotators agree yes, this sentences is labeled by 3. \
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+ Therefore, the label of each sentence or comment ranges from 1 to 5\
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+ (1: very poor, 2: poor, 3: fair, 4: good; 5: perfect). The standard \
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+ summary sentences are those which receive at least three agreements \
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+ from annotators. The inter-agreement calculated by Cohen's Kappa \
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+ after validation among annotators is 0.685.
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+ """
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+
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+ _HOMEPAGE = "https://github.com/nguyenlab/VSoLSCSum-Dataset"
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+
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+ _LANGUAGES = ["vie"]
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+
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+ _LICENSE = Licenses.CC_BY_4_0.value
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+
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+ _LOCAL = False
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+
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+ _URLS = {
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+ _DATASETNAME: "https://raw.githubusercontent.com/nguyenlab/VSoLSCSum-Dataset/master/VSoSLCSum.xml",
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+ }
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+
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+ _SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
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+
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+ _SOURCE_VERSION = "1.0.0"
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+
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+ _SEACROWD_VERSION = "2024.06.20"
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+
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+
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+ class VSolSCSumDataset(datasets.GeneratorBasedBuilder):
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+ """
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+ The Vietnamese dataset for social context summarization includes 141 articles
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+ with a total of 3,760 sentences. It also contains 2,448 standard sentences
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+ extracted along with comments serving as standard summaries, and 6,926 c
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+ omments across 12 events. Human annotators manually curated this dataset.
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+ Each sentence or comment received a label from 1 to 5 based on annotators'
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+ agreement (1: very poor, 2: poor, 3: fair, 4: good, 5: perfect). Standard
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+ summary sentences are those with at least three agreements. The inter-agreement
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+ among annotators, measured by Cohen's Kappa, is 0.685.
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+ """
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+
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+ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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+ SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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+
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+ BUILDER_CONFIGS = [
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+ SEACrowdConfig(
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+ name=f"{_DATASETNAME}_source",
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+ version=SOURCE_VERSION,
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+ description=f"{_DATASETNAME} source schema",
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+ schema="source",
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+ subset_id=f"{_DATASETNAME}",
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+ ),
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+ SEACrowdConfig(
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+ name=f"{_DATASETNAME}_seacrowd_t2t",
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+ version=SEACROWD_VERSION,
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+ description=f"{_DATASETNAME} SEACrowd schema",
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+ schema="seacrowd_t2t",
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+ subset_id=f"{_DATASETNAME}",
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+ ),
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+ ]
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+
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+ DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
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+
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+ def _info(self) -> datasets.DatasetInfo:
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+
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+ if self.config.schema == "source":
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+ features = datasets.Features(
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+ {
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+ "post_id": datasets.Value("string"),
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+ "title": datasets.Value("string"),
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+ "summary": datasets.Value("string"),
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+ "document_and_comment": datasets.Value("string"),
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+ }
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+ )
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+
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+ elif self.config.schema == "seacrowd_t2t":
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+ features = schemas.text2text_features
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+
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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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+
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+ data_path = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME]))
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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": data_path,
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+ "split": "train",
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+ },
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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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+ """Yields examples as (key, example) tuples."""
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+
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+ with open(filepath, "r", encoding="utf-8") as file:
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+ xml_content = file.read()
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+
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+ root = ET.fromstring(xml_content)
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+
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+ def extract_data_from_xml(root):
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+ data = []
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+
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+ for post in root.findall(".//post"):
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+ post_id = post.get("id")
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+ title = post.find("title").text
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+ summary_sentences = [sentence.find("content").text for sentence in post.find(".//summary").find("sentences").findall("sentence")]
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+ document_sentences = [sentence.find("content").text for sentence in post.find(".//document").find("sentences").findall("sentence")]
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+ comment_sentences = [sentence.find("content").text for sentence in post.find(".//comments").find(".//comment").find("sentences").findall("sentence")]
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+
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+ summary_text = " ".join(summary_sentences)
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+ document_text = " ".join(document_sentences)
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+ comment_text = " ".join(comment_sentences)
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+
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+ data.append(
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+ {
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+ "post_id": post_id,
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+ "title": title,
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+ "summary": summary_text,
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+ "document_and_comment": f"{document_text} | {comment_text}",
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+ }
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+ )
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+
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+ return data
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+
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+ extracted_data = extract_data_from_xml(root)
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+ df = pd.DataFrame(extracted_data)
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+
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+ for index, row in df.iterrows():
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+
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+ if self.config.schema == "source":
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+ example = row.to_dict()
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+
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+ elif self.config.schema == "seacrowd_t2t":
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+
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+ example = {
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+ "id": str(row["post_id"]),
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+ "text_1": str(row["summary"]),
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+ "text_2": str(row["document_and_comment"]),
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+ "text_1_name": "summary",
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+ "text_2_name": "document_and_comment",
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+ }
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+
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+ yield index, example