Add script

data/multiwiki_90k.zip

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 version https://git-lfs.github.com/spec/v1
-oid sha256:2305bc8fea0b7fe5395e2d3c5ec782489b77ce1382ef739eb729126ef67e6953
-size 196806760
+oid sha256:4efa835f9a45779eee3170609e5c6029894687458f00da3ec9245d87000fb085
+size 195744272

multiwiki_90k.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.
+
+import os
+from typing import Generator, Tuple, Dict, List, TextIO
+from pathlib import Path
+
+import numpy as np
+import datasets
+from datasets import DownloadManager
+from datasets.info import SupervisedKeysData
+
+_DESCRIPTION = """Multi-Wiki90k: Multilingual benchmark dataset for paragraph
+segmentation"""
+_HOMEPAGE = "https://clarin-pl.eu"
+_URLs = {
+    "1.0": "https://huggingface.co/datasets/clarin-pl/multiwiki_90k/resolve/main/data/multiwiki_90k.zip",
+}
+_CLASSES = ["O", "1"]
+
+_PARAGRAPH_SEPARATOR = '=========='
+
+
+class MultiWiki(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="1.0",
+            version=VERSION,
+            description="Multilingual benchmark dataset for paragraph segmentation containing 90k of wikipedia "
+                        "articles in 9 languages. The selection of languages was made from the most popular "
+                        "representatives of the Romance, Slavic and Germanic language groups. These languages are: "
+                        "German, English, Spanish, French, Italian, Dutch, Polish, Portuguese and Russian.",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "1.0"
+
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "label": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=_CLASSES, num_classes=len(_CLASSES)
+                        )
+                    ),
+                    "text": datasets.Sequence(
+                        datasets.features.Value("string")
+                    )
+                }
+            ),
+            homepage=_HOMEPAGE,
+            supervised_keys=SupervisedKeysData(input='text', output='label')
+        )
+
+    @staticmethod
+    def _doc_to_vector(doc: TextIO) -> np.array:
+        """
+        Returns a vector of label from a document.
+        eg. [0,1,0,0,0,1] where each number stands for sentence with respective position
+        """
+        vec = []
+        lines = doc.readlines()
+        for i in range(len(lines) - 1):
+            if _PARAGRAPH_SEPARATOR in lines[i]:
+                pass
+            elif _PARAGRAPH_SEPARATOR in lines[i + 1]:
+                vec.append(1)
+            else:
+                vec.append(0)
+        return np.array(vec).reshape(len(vec), 1)
+
+    @staticmethod
+    def _doc_get_sentences(doc: TextIO) -> np.array:
+        """
+        Returns a list of sentences from a document.
+        """
+        sentences = []
+        lines = doc.readlines()
+        for line in lines:
+            if _PARAGRAPH_SEPARATOR not in line:
+                sentences.append(line)
+        return np.array(sentences)
+
+    def _split_generators(
+            self, dl_manager: DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "dirpath": os.path.join(data_dir),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "dirpath": os.path.join(data_dir),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "dirpath": os.path.join(data_dir),
+                    "split": "valid",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+            self,
+            dirpath: str,
+            split: str,
+    ) -> Generator[Tuple[int, Dict[str, str]], None, None]:
+
+        for file_path in Path(f'{dirpath}/{split}').glob('*'):
+            with open(file_path) as f:
+                yield file_path.name, {
+                    "label": self._doc_to_vector(f),
+                    "text": self._doc_get_sentences(f),
+                }