Add data and first script

README.md

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+---
+pretty_name: Extreme Summarization (XSum) in Dutch
+language:
+- nl
+paperswithcode_id: xsum_dutch
+task_categories:
+- summarization
+task_ids:
+- news-articles-summarization
+train-eval-index:
+- config: default
+  task: summarization
+  task_id: summarization
+  splits:
+    train_split: train
+    eval_split: test
+  col_mapping:
+    document: text
+    summary: target
+  metrics:
+    - type: rouge
+      name: Rouge
+---
+
+# Dataset Card for "xsum_dutch" 🇳🇱🇧🇪 Dataset
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+The Xsum Dutch 🇳🇱🇧🇪 Dataset is an English-language dataset translated to Dutch.
+
+*This dataset currently (Aug '22) has a single config, which is
+config `default` of [xsum](https://huggingface.co/datasets/xsum) translated to Dutch
+with [yhavinga/t5-base-36L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-base-36L-ccmatrix-multi).*
+
+- **Homepage:** [https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 245.38 MB
+- **Size of the generated dataset:** 507.60 MB
+- **Total amount of disk used:** 752.98 MB
+
+### Dataset Summary
+
+Extreme Summarization (XSum) Dataset.
+
+There are three features:
+  - document: Input news article.
+  - summary: One sentence summary of the article.
+  - id: BBC ID of the article.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### default
+
+- **Size of downloaded dataset files:** 245.38 MB
+- **Size of the generated dataset:** 507.60 MB
+- **Total amount of disk used:** 752.98 MB
+
+An example of 'validation' looks as follows.
+```
+{
+    "document": "some-body",
+    "id": "29750031",
+    "summary": "some-sentence"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### default
+- `document`: a `string` feature.
+- `summary`: a `string` feature.
+- `id`: a `string` feature.
+
+### Data Splits
+
+| name  |train |validation|test |
+|-------|-----:|---------:|----:|
+|default|204045|     11332|11334|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+
+@article{Narayan2018DontGM,
+  title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
+  author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
+  journal={ArXiv},
+  year={2018},
+  volume={abs/1808.08745}
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@jbragg](https://github.com/jbragg), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding the English version of this dataset.
+The dataset was translated on Cloud TPU compute generously provided by Google through the
+[TPU Research Cloud](https://sites.research.google/trc/).

default/test.json.gz

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default/train.json.gz

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default/validation.json.gz

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+size 11080702

test_xsum_dutch.py

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+from itertools import chain
+
+import pytest
+from datasets import load_dataset
+
+
+def test_streaming_dataset():
+    datasets = load_dataset("./cnn_dailymail_dutch.py", streaming=True)
+    assert set(datasets.keys()) == {"train", "validation", "test"}
+
+    for split in ["train", "validation", "test"]:
+        ds = datasets[split]
+        for i, e in enumerate(ds):
+            assert 10 <= len(e["article"]) <= 11000
+            assert 10 <= len(e["highlights"]) <= 500
+            if i == 100:
+                break
+
+
+def test_train_split():
+    ds = load_dataset("./cnn_dailymail_dutch.py", split="train" + f"[:{2000}]")
+    assert len(ds) == 2000
+
+
+def test_batch_dataset():
+    datasets = load_dataset("./cnn_dailymail_dutch.py")
+    assert set(datasets.keys()) == {"train", "validation", "test"}

xsum_dutch.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Cleaned Dutch split of the mC4 corpus."""
+
+
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_HOMEPAGE = "https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset"
+
+_CITATION = """
+@article{Narayan2018DontGM,
+  title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
+  author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
+  journal={ArXiv},
+  year={2018},
+  volume={abs/1808.08745}
+}
+"""
+
+_DESCRIPTION = """
+Extreme Summarization (XSum) Dataset.
+There are three features:
+  - document: Input news article.
+  - summary: One sentence summary of the article.
+  - id: BBC ID of the article.
+
+"""
+
+  
+_DATA_URL_NL = "https://huggingface.co/datasets/yhavinga/xsum_dutch/resolve/main/{config}/{split}.json.gz"
+
+
+_DOCUMENT = "document"
+_SUMMARY = "summary"
+_ID = "id"
+
+_SUPPORTED_VERSIONS = [
+    datasets.Version("default", "Default version."),
+]
+
+
+class XsumDutchConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+class XsumDutch(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        XsumDutchConfig(
+            name=str(version), description=version.description
+        )
+        for version in _SUPPORTED_VERSIONS
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    _DOCUMENT: datasets.Value("string"),
+                    _SUMMARY: datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        result = [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "filepath": dl_manager.download_and_extract(
+                        _DATA_URL_NL.format(split=str(split), config=str(self.config.name))
+                    )
+                },
+            )
+            for split in [
+                datasets.Split.TRAIN,
+                datasets.Split.VALIDATION,
+                datasets.Split.TEST,
+            ]
+        ]
+        return result
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        logger.info(f"Generating examples from {filepath}")
+
+        with open(filepath, "r") as file:
+            for _id, line in enumerate(file):
+                example = json.loads(line)
+                yield _id, example