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xlsum-fi.py

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+"""XL-Sum-FI Finnish abstractive summarization dataset based on machine translation of the XL-Sum dataset"""
+
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+
+Please cite the article and also acknowledge Filip Ginter / TurkuNLP for the machine translated version
+
+@inproceedings{hasan-etal-2021-xl,
+    title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
+    author = "Hasan, Tahmid  and
+      Bhattacharjee, Abhik  and
+      Islam, Md. Saiful  and
+      Mubasshir, Kazi  and
+      Li, Yuan-Fang  and
+      Kang, Yong-Bin  and
+      Rahman, M. Sohel  and
+      Shahriyar, Rifat",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.413",
+    pages = "4693--4703",
+}
+"""
+
+
+_DESCRIPTION = """\
+This dataset is a DeepL -based machine translation of a part of the English section of the XLSum dataset:[https://github.com/csebuetnlp/xl-sum](https://github.com/csebuetnlp/xl-sum) In the present version, only examples where the full version is at most 10x the summary in length are included. We might translate more later.
+"""
+
+_HOMEPAGE = "https://github.com/TurkuNLP/xlsum-fi"
+
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
+
+_URL = "https://huggingface.co/datasets/TurkuNLP/xlsum-fi/resolve/main/data/{}_XLSum-fi_v{}.tar.bz2"
+
+_LANGUAGES = [
+    "finnish",
+]
+
+
+class Xlsum(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("2.0.0")
+    
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="{}".format(lang),
+            version=datasets.Version("2.0.0")
+        )
+        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, self.VERSION.version_str[:-2])
+
+        data_dir = dl_manager.download_and_extract(url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, lang + "_train.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, lang + "_test.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, lang + "_val.jsonl"),
+                },
+            ),
+        ]
+
+    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"],
+                }