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2021_49/dedup/oscar_2201_10_dedup.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 Indonesian split of the mC4 corpus."""
+import json
+import glob
+import gzip
+import textwrap
+import datasets
+logger = datasets.logging.get_logger(__name__)
+
+file = sorted(glob.glob('/data/feb_oscar_2201_10_dedup/data/*'))
+_CITATION = """
+@article{JMLR:v21:20-074,
+  author  = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
+  title   = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
+  journal = {Journal of Machine Learning Research},
+  year    = {2020},
+  volume  = {21},
+  number  = {140},
+  pages   = {1-67},
+  url     = {http://jmlr.org/papers/v21/20-074.html}
+}
+"""
+_DESCRIPTION = """\
+A thoroughly cleaned version of the Italian portion of the multilingual 
+colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI.
+Based on Common Crawl dataset: "https://commoncrawl.org".
+This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning
+detailed in the repository README file.
+"""
+_HOMEPAGE = "https://github.com/allenai/allennlp/discussions/5056"
+_LICENSE = "Open Data Commons Attribution License (ODC-By) v1.0"
+_BASE_URL = "https://huggingface.co/datasets/munggok/mc4-id/resolve/main/mc4-id-filter/c4-id{split_suffix}.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"
+_CONFIGS = {
+    "tiny": {"train": 100, "validation": 1},
+    "small": {"train": 250, "validation": 2},
+    "medium": {"train": 500, "validation": 4},
+    "large": {"train": 750, "validation": 6},
+    "full": {"train": 1016, "validation": 8}
+}
+class OscarConfig(datasets.BuilderConfig):
+    """BuilderConfig for the Clean mC4 Italian."""
+    def __init__(self, **kwargs):
+        """BuilderConfig for Clean mC4 Italian.
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(**kwargs)
+class Oscar(datasets.GeneratorBasedBuilder):
+    """mC4, a colossal, cleaned version of Common Crawl's web crawl corpus."""
+    BUILDER_CONFIGS = [
+        OscarConfig(
+            name="full",
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                f"""\
+                The full cleaned version of the Italian portion of the multilingual C4 corpus.
+                Estimated size of compressed files: 103GB
+                """
+            )
+        )
+    ]
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "timestamp": datasets.Value("string"),
+                    "meta": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        data_urls = {}
+        train_downloaded_files = file
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
+        ]
+    def _generate_examples(self, filepaths):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        id_ = 0
+        for filepath in filepaths:
+            logger.info(f"Generating examples from {filepath}")
+            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for line in f:
+                    if line:
+                        example = json.loads(line)
+                        yield id_, {'text':example['text'],'url':example['url'],'timestamp':example['timestamp'],'meta': example['meta']}
+                        id_ += 1

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