Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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dataset_infos.json

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+{"de": {"description": "We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. \nObtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. \nTogether with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. \nWe report cross-lingual comparative analyses based on state-of-the-art systems. \nThese highlight existing biases which motivate the use of a multi-lingual dataset. \n", "citation": "@article{scialom2020mlsum,\n  title={MLSUM: The Multilingual Summarization Corpus},\n  author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},\n  journal={arXiv preprint arXiv:2004.14900},\n  year={2020}\n}\n", "homepage": "", "license": "", "features": {"text": {"dtype": "string", 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"447e3b1839ab94d5700cc2aedc0b52521404865b2589656acc90a654ed0de4ff"}, "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_train.zip": {"num_bytes": 311059697, "checksum": "88e788437bae48af6b3d18a554af4b2794cc6143a137df3f56daa91a37e3ea7e"}, "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_val.zip": {"num_bytes": 17771216, "checksum": "732620c32e1d3f393ee3193f57f1217d8549499eb4906e144252aaab39aa910b"}}, "download_size": 346572060, "dataset_size": 940927641, "size_in_bytes": 1287499701}, "es": {"description": "We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. \nObtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. \nTogether with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. \nWe report cross-lingual comparative analyses based on state-of-the-art systems. \nThese highlight existing biases which motivate the use of a multi-lingual dataset. \n", "citation": "@article{scialom2020mlsum,\n  title={MLSUM: The Multilingual Summarization Corpus},\n  author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},\n  journal={arXiv preprint arXiv:2004.14900},\n  year={2020}\n}\n", "homepage": "", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}, "topic": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "mlsum", "config_name": "es", "version": {"version_str": "1.0.0", "description": null, 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"license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}, "topic": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "mlsum", "config_name": "fr", "version": {"version_str": "1.0.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1471965974, "num_examples": 392902, "dataset_name": "mlsum"}, "validation": {"name": "validation", "num_bytes": 70413260, "num_examples": 16059, "dataset_name": "mlsum"}, "test": {"name": "test", "num_bytes": 69660336, "num_examples": 15828, "dataset_name": "mlsum"}}, "download_checksums": 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mlsum.py

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+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{scialom2020mlsum,
+  title={MLSUM: The Multilingual Summarization Corpus},
+  author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
+  journal={arXiv preprint arXiv:2004.14900},
+  year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
+Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.
+Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.
+We report cross-lingual comparative analyses based on state-of-the-art systems.
+These highlight existing biases which motivate the use of a multi-lingual dataset.
+"""
+_URL = "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/"
+_LANG = ["de", "es", "fr", "ru", "tu"]
+
+
+class Mlsum(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=lang,
+            version=datasets.Version("1.0.0"),
+            description="",
+        )
+        for lang in _LANG
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "summary": datasets.Value("string"),
+                    "topic": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "date": datasets.Value("string")
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+
+        lang = str(self.config.name)
+        urls_to_download = {
+            "test": os.path.join(_URL, lang + "_test.zip"),
+            "train": os.path.join(_URL, lang + "_train.zip"),
+            "validation": os.path.join(_URL, lang + "_val.zip"),
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(downloaded_files["train"], lang + "_train.jsonl"),
+                    "lang": lang,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(downloaded_files["validation"], lang + "_val.jsonl"),
+                    "lang": lang,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(downloaded_files["test"], lang + "_test.jsonl"),
+                    "lang": lang,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, lang):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            i = 0
+            for line in f:
+                data = json.loads(line)
+                i += 1
+                yield i, {
+                    "text": data["text"],
+                    "summary": data["summary"],
+                    "topic": data["topic"],
+                    "url": data["url"],
+                    "title": data["title"],
+                    "date": data["date"],
+                }