Delete loading script

lr-sum.py

@@ -1,160 +0,0 @@
-"""LR-Sum summarization dataset"""
-
-import json
-import os
-
-import datasets
-
-_CITATION = """\
-@inproceedings{palen-michel-lignos-2023-lr,
-    title = "{LR}-Sum: Summarization for Less-Resourced Languages",
-    author = "Palen-Michel, Chester  and
-      Lignos, Constantine",
-    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
-    month = jul,
-    year = "2023",
-    address = "Toronto, Canada",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2023.findings-acl.427",
-    doi = "10.18653/v1/2023.findings-acl.427",
-    pages = "6829--6844",
-    abstract = "We introduce LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages.LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022).The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets. We describe abstractive and extractive summarization experiments to establish baselines and discuss the limitations of this dataset.",
-}
-"""
-
-_DESCRIPTION = """\
-We introduce LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages. 
-LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. 
-We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022).
-The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets. 
-We describe abstractive and extractive summarization experiments to establish baselines and discuss the limitations of this dataset.
-"""
-
-_HOMEPAGE = "https://github.com/bltlab"
-
-_LICENSE = "Creative Commons Attribution 4.0 International (CC-BY 4.0)"
-
-_URL = "https://huggingface.co/datasets/bltlab/lr-sum/resolve/main/data/{}.tar.bz2"
-
-_LANGUAGES = [
-    "amh",
-    "aze",
-    "ben",
-    "bod",
-    "bos",
-    "ckb",
-    "cmn_t",
-    "cmn_s",
-    "ell",
-    "eng",
-    "fas",
-    "fra",
-    "hat",
-    "hau",
-    "hye",
-    "ind",
-    "kat",
-    "khm",
-    "kin",
-    "kor",
-    "kmr",
-    "lao",
-    "mkd",
-    "mya",
-    "nde",
-    "por",
-    "prs",
-    "pus",
-    "rus",
-    "sna",
-    "som",
-    "spa",
-    "sqi",
-    "srp",
-    "swh",
-    "tha",
-    "tir",
-    "tur",
-    "ukr",
-    "urd",
-    "uzb",
-    "vie",
-]
-
-
-class Lrsum(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="{}".format(lang),
-            version=datasets.Version("1.0.0")
-        )
-        for lang in _LANGUAGES
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "url": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "summary": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-            version=self.VERSION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        lang = str(self.config.name)
-        url = _URL.format(lang)
-
-        data_dir = dl_manager.download_and_extract(url)
-        ret = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, lang, lang + "_test.jsonl"),
-                },
-            )
-        ]
-        if os.path.exists(os.path.join(data_dir, lang,  lang + "_train.jsonl")):
-            ret.append(datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, lang, lang + "_train.jsonl"),
-                },
-            )
-            )
-        if os.path.exists(os.path.join(data_dir, lang,  lang + "_val.jsonl")):
-            ret.append(
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, lang, lang + "_val.jsonl"),
-                    },
-                )
-            )
-
-        return ret
-
-    def _generate_examples(self, filepath):
-        """Yields examples as (key, example) tuples."""
-        with open(filepath, encoding="utf-8") as f:
-            for idx_, row in enumerate(f):
-                data = json.loads(row)
-                yield idx_, {
-                    "id": data["id_"],
-                    "url": data["url"],
-                    "title": data["title"],
-                    "summary": data["summary"],
-                    "text": data["text"],
-                }