Update script

cnn_dailymail_dutch.py

@@ -0,0 +1,135 @@
+# 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/abisee/cnn-dailymail"
+
+_DESCRIPTION = """\
+CNN/DailyMail non-anonymized summarization dataset, translated to Dutch with ccmatrix.
+There are two features:
+  - article: text of news article, used as the document to be summarized
+  - highlights: joined text of highlights with <s> and </s> around each
+    highlight, which is the target summary
+"""
+
+_LICENSE = "Open Data Commons Attribution License (ODC-By) v1.0"
+
+_DATA_URL_NL = "https://huggingface.co/datasets/yhavinga/cnn_dailymail_dutch/resolve/main/{config}/{split}.json.gz"
+
+# The second citation introduces the source data, while the first
+# introduces the specific form (non-anonymized) we use here.
+_CITATION = """\
+@article{DBLP:journals/corr/SeeLM17,
+  author    = {Abigail See and
+               Peter J. Liu and
+               Christopher D. Manning},
+  title     = {Get To The Point: Summarization with Pointer-Generator Networks},
+  journal   = {CoRR},
+  volume    = {abs/1704.04368},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1704.04368},
+  archivePrefix = {arXiv},
+  eprint    = {1704.04368},
+  timestamp = {Mon, 13 Aug 2018 16:46:08 +0200},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/SeeLM17},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+@inproceedings{hermann2015teaching,
+  title={Teaching machines to read and comprehend},
+  author={Hermann, Karl Moritz and Kocisky, Tomas and Grefenstette, Edward and Espeholt, Lasse and Kay, Will and Suleyman, Mustafa and Blunsom, Phil},
+  booktitle={Advances in neural information processing systems},
+  pages={1693--1701},
+  year={2015}
+}
+"""
+
+_HIGHLIGHTS = "highlights"
+_ARTICLE = "article"
+
+_SUPPORTED_VERSIONS = [
+    # Using cased version.
+    datasets.Version("3.0.0", "Using cased version."),
+]
+
+
+class CnnDailymailDutchConfig(datasets.BuilderConfig):
+    """BuilderConfig for CnnDailymail Dutch."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for CnnDailymail Dutch.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(**kwargs)
+
+
+class CnnDailymailDutch(datasets.GeneratorBasedBuilder):
+    """CNN/DailyMail non-anonymized summarization dataset in Dutch."""
+
+    BUILDER_CONFIGS = [
+        CnnDailymailDutchConfig(
+            name=str(version), description=version.description
+        )
+        for version in _SUPPORTED_VERSIONS
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    _ARTICLE: datasets.Value("string"),
+                    _HIGHLIGHTS: datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            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

dataset_infos.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ecbc6257b41c04d197b4d8c51416193e620319a178bf6d5559f65905831b1e2
+size 2960

test_cnn_dailymail_dutch.py

@@ -5,18 +5,23 @@ from datasets import load_dataset
 
 
 def test_streaming_dataset():
-    datasets = load_dataset('./3.0.0', streaming=True)
-    assert set(datasets.keys()) == {'train', 'validation', 'test'}
+    datasets = load_dataset("./cnn_dailymail_dutch.py", streaming=True)
+    assert set(datasets.keys()) == {"train", "validation", "test"}
 
-    for split in ['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
+            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('./3.0.0')
-    assert set(datasets.keys()) == {'train', 'validation', 'test'}
+    datasets = load_dataset("./cnn_dailymail_dutch.py")
+    assert set(datasets.keys()) == {"train", "validation", "test"}