Create chensu_test_dataset2.py

chensu_test_dataset2.py

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+"""C4 dataset based on Common Crawl."""
+
+
+import gzip
+import json
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_DESCRIPTION = """\
+A colossal, cleaned version of Common Crawl's web crawl corpus.
+Based on Common Crawl dataset: "https://commoncrawl.org".
+This is the processed version of Google's C4 dataset by AllenAI.
+"""
+
+_CITATION = """
+@article{2019t5,
+    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 = {arXiv e-prints},
+    year = {2019},
+    archivePrefix = {arXiv},
+    eprint = {1910.10683},
+}
+"""
+
+_URL = "https://github.com/allenai/allennlp/discussions/5056"
+
+_VARIANTS = ["en", "realnewslike", "en.noblocklist", "en.noclean"]
+
+_N_SHARDS_PER_SPLIT = {
+    "en": {"train": 1024, "validation": 8},
+    "realnewslike": {"train": 512, "validation": 1},
+    "en.noblocklist": {"train": 1024, "validation": 8},
+    "en.noclean": {"train": 7168, "validation": 64},
+}
+
+_DATA_URL = "https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/{name}/c4-{split}.{index:05d}-of-{n_shards:05d}.json.gz"
+
+
+class C4(datasets.GeneratorBasedBuilder):
+    """C4, a colossal, cleaned version of Common Crawl's web crawl corpus."""
+
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS]
+
+    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=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_urls = {}
+        for split in ["train", "validation"]:
+            n_shards = _N_SHARDS_PER_SPLIT[self.config.name][split]
+            data_urls[split] = [
+                _DATA_URL.format(name=self.config.name, split=split, index=index, n_shards=n_shards)
+                for index in range(n_shards)
+            ]
+        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("generating examples from = %s", 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