Initial commit

enwiki-yearly-cleaned.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 and split version of the English Wikipedia."""
+
+
+import json
+import gzip
+import textwrap
+import datasets
+import random
+from itertools import zip_longest
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+"""
+
+_DESCRIPTION = """\
+
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+_DATA_URL = "https://huggingface.co/datasets/pdelobelle/enwiki-yearly-cleaned/resolve/main/enwiki-yearly-cleaned/{split}/enwiki{index}-{split}.json.gz"
+
+_CONFIG_NAMES = ["tiny", "small", "medium", "large", "full"]
+
+_CONFIGS = dict(
+    tiny={"train": 2, "validation": 1, "estimate": "0.1GB"},
+    small={"train": 100, "validation": 2, "estimate": "4GB"},
+    medium={"train": 750, "validation": 2, "estimate": "30GB"},
+    large={"train": 1500, "validation": 3, "estimate": "59GB"},
+    full={"train": 3497, "validation": 4, "estimate": "137GB"},
+)
+
+class Wikipedia(datasets.GeneratorBasedBuilder):
+    """Cleaned and split version of the English Wikipedia."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=name,
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                f"""\
+            A {name} version of the English Wikipedia.
+            Estimated size of compressed files: {_CONFIGS[name]['estimate']}
+            """
+            ),
+        )
+        for name in _CONFIG_NAMES
+    ]
+
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "timestamp": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_urls = {}
+        config = _CONFIGS[self.config.name]
+        for split in ["train", "validation"]:
+            start_file = config.get("start", 0) if split == "train" else 0
+            num_files = config.get(split)
+
+            data_urls[split] = []
+            for index in range(start_file, start_file + num_files):
+                data_urls[split].append(
+                    _DATA_URL.format(
+                        split=split,
+                        index=index,
+                        n_shards=4 if split == "validation" else 1024,
+                    )
+                )
+               
+        # Shuffle data in streaming mode, so restarts will not always start with the same data
+        if dl_manager.is_streaming:
+            random.shuffle(data_urls["train"])
+        train_downloaded_files = dl_manager.download(data_urls["train"])
+        validation_downloaded_files = dl_manager.download(data_urls["validation"])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepaths": train_downloaded_files},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepaths": validation_downloaded_files},
+            ),
+        ]
+
+    @staticmethod
+    def grouper(iterable, n, fillvalue=None):
+        """Collect data into fixed-length chunks or blocks"""
+        # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
+        args = [iter(iterable)] * n
+        return zip_longest(*args, fillvalue=fillvalue)
+
+    @staticmethod
+    def gzip_open(filepath):
+        if filepath:
+            return gzip.open(open(filepath, "rb"), "rt", encoding="utf-8")
+
+    def _generate_examples(self, filepaths):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        id_ = 0
+        for files in self.grouper(filepaths, 2, None):
+            logger.info(f"Generating examples from {files}")
+            gzip_iters = [self.gzip_open(file) for file in files if file is not None]
+            for lines in zip(*gzip_iters):
+                for line in lines:
+                    example = json.loads(line)
+                    yield id_, example
+                    id_ += 1

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