add script

mc4_id.py

@@ -0,0 +1,156 @@
+# 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 gzip
+import textwrap
+import datasets
+logger = datasets.logging.get_logger(__name__)
+_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 CleanMc4IdConfig(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 Mc4(datasets.GeneratorBasedBuilder):
+    """mC4, a colossal, cleaned version of Common Crawl's web crawl corpus."""
+    BUILDER_CONFIGS = [
+        CleanMc4IdConfig(
+            name="tiny",
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                f"""\
+                A tiny cleaned version of the Italian portion of the multilingual C4 corpus.
+                Estimated size of compressed files: 10GB
+                """
+            )
+        ),
+        CleanMc4IdConfig(
+            name="small",
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                f"""\
+                A small cleaned version of the Italian portion of the multilingual C4 corpus.
+                Estimated size of compressed files: 25GB
+                """
+            )
+        ),
+        CleanMc4IdConfig(
+            name="medium",
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                f"""\
+                A medium cleaned version of the Italian portion of the multilingual C4 corpus.
+                Estimated size of compressed files: 50GB
+                """
+            )
+        ),
+        CleanMc4IdConfig(
+            name="large",
+            version=datasets.Version("1.0.0"),
+            description=textwrap.dedent(
+                f"""\
+                A large cleaned version of the Italian portion of the multilingual C4 corpus.
+                Estimated size of compressed files: 75GB
+                """
+            )
+        ),
+        CleanMc4IdConfig(
+            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"),
+                    "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 = {}
+        for split in ["train", "validation"]:
+            data_urls[split] = [
+                _BASE_URL.format(
+                    split_suffix="-validation" if split == "validation" else "",
+                    index=index,
+                    n_shards=8 if split == "validation" else 1024,
+                )
+                for index in range(_CONFIGS[self.config.name][split])
+            ]
+        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}
+            ),
+        ]
+    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_, example
+                        id_ += 1