adding dataset config

.gitattributes

@@ -14,3 +14,4 @@
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text

clean-si-mc4.py

@@ -0,0 +1,336 @@
+"""mC4 dataset based on Common Crawl."""
+
+
+import gzip
+import json
+import re
+
+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 mC4 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"
+
+_DATA_URL = "https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/multilingual/c4-{language}{split_suffix}.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"
+
+_LANGUAGES = [
+    "af",
+    "am",
+    "ar",
+    "az",
+    "be",
+    "bg",
+    "bg-Latn",
+    "bn",
+    "ca",
+    "ceb",
+    "co",
+    "cs",
+    "cy",
+    "da",
+    "de",
+    "el",
+    "el-Latn",
+    "en",
+    "eo",
+    "es",
+    "et",
+    "eu",
+    "fa",
+    "fi",
+    "fil",
+    "fr",
+    "fy",
+    "ga",
+    "gd",
+    "gl",
+    "gu",
+    "ha",
+    "haw",
+    "hi",
+    "hi-Latn",
+    "hmn",
+    "ht",
+    "hu",
+    "hy",
+    "id",
+    "ig",
+    "is",
+    "it",
+    "iw",
+    "ja",
+    "ja-Latn",
+    "jv",
+    "ka",
+    "kk",
+    "km",
+    "kn",
+    "ko",
+    "ku",
+    "ky",
+    "la",
+    "lb",
+    "lo",
+    "lt",
+    "lv",
+    "mg",
+    "mi",
+    "mk",
+    "ml",
+    "mn",
+    "mr",
+    "ms",
+    "mt",
+    "my",
+    "ne",
+    "nl",
+    "no",
+    "ny",
+    "pa",
+    "pl",
+    "ps",
+    "pt",
+    "ro",
+    "ru",
+    "ru-Latn",
+    "sd",
+    "si",
+    "sk",
+    "sl",
+    "sm",
+    "sn",
+    "so",
+    "sq",
+    "sr",
+    "st",
+    "su",
+    "sv",
+    "sw",
+    "ta",
+    "te",
+    "tg",
+    "th",
+    "tr",
+    "uk",
+    "und",
+    "ur",
+    "uz",
+    "vi",
+    "xh",
+    "yi",
+    "yo",
+    "zh",
+    "zh-Latn",
+    "zu",
+]
+
+_N_SHARDS_PER_SPLIT = {
+    "af": {"train": 64, "validation": 1},
+    "am": {"train": 16, "validation": 1},
+    "ar": {"train": 1024, "validation": 4},
+    "az": {"train": 256, "validation": 1},
+    "be": {"train": 128, "validation": 1},
+    "bg": {"train": 1024, "validation": 1},
+    "bg-Latn": {"train": 4, "validation": 1},
+    "bn": {"train": 512, "validation": 1},
+    "ca": {"train": 512, "validation": 1},
+    "ceb": {"train": 8, "validation": 1},
+    "co": {"train": 8, "validation": 1},
+    "cs": {"train": 1024, "validation": 2},
+    "cy": {"train": 256, "validation": 1},
+    "da": {"train": 1024, "validation": 1},
+    "de": {"train": 2048, "validation": 16},
+    "el": {"train": 1024, "validation": 2},
+    "el-Latn": {"train": 16, "validation": 1},
+    "en": {"train": 11264, "validation": 128},
+    "eo": {"train": 32, "validation": 1},
+    "es": {"train": 2048, "validation": 16},
+    "et": {"train": 256, "validation": 1},
+    "eu": {"train": 64, "validation": 1},
+    "fa": {"train": 1024, "validation": 2},
+    "fi": {"train": 1024, "validation": 1},
+    "fil": {"train": 64, "validation": 1},
+    "fr": {"train": 2048, "validation": 16},
+    "fy": {"train": 16, "validation": 1},
+    "ga": {"train": 16, "validation": 1},
+    "gd": {"train": 16, "validation": 1},
+    "gl": {"train": 128, "validation": 1},
+    "gu": {"train": 64, "validation": 1},
+    "ha": {"train": 8, "validation": 1},
+    "haw": {"train": 2, "validation": 1},
+    "hi": {"train": 1024, "validation": 2},
+    "hi-Latn": {"train": 16, "validation": 1},
+    "hmn": {"train": 8, "validation": 1},
+    "ht": {"train": 8, "validation": 1},
+    "hu": {"train": 1024, "validation": 2},
+    "hy": {"train": 128, "validation": 1},
+    "id": {"train": 1024, "validation": 4},
+    "ig": {"train": 4, "validation": 1},
+    "is": {"train": 128, "validation": 1},
+    "it": {"train": 1024, "validation": 8},
+    "iw": {"train": 1024, "validation": 1},
+    "ja": {"train": 1024, "validation": 8},
+    "ja-Latn": {"train": 8, "validation": 1},
+    "jv": {"train": 8, "validation": 1},
+    "ka": {"train": 256, "validation": 1},
+    "kk": {"train": 256, "validation": 1},
+    "km": {"train": 64, "validation": 1},
+    "kn": {"train": 64, "validation": 1},
+    "ko": {"train": 1024, "validation": 1},
+    "ku": {"train": 16, "validation": 1},
+    "ky": {"train": 64, "validation": 1},
+    "la": {"train": 64, "validation": 1},
+    "lb": {"train": 32, "validation": 1},
+    "lo": {"train": 8, "validation": 1},
+    "lt": {"train": 512, "validation": 1},
+    "lv": {"train": 256, "validation": 1},
+    "mg": {"train": 8, "validation": 1},
+    "mi": {"train": 4, "validation": 1},
+    "mk": {"train": 128, "validation": 1},
+    "ml": {"train": 128, "validation": 1},
+    "mn": {"train": 128, "validation": 1},
+    "mr": {"train": 1024, "validation": 1},
+    "ms": {"train": 512, "validation": 1},
+    "mt": {"train": 128, "validation": 1},
+    "my": {"train": 64, "validation": 1},
+    "ne": {"train": 256, "validation": 1},
+    "nl": {"train": 1024, "validation": 4},
+    "no": {"train": 1024, "validation": 1},
+    "ny": {"train": 4, "validation": 1},
+    "pa": {"train": 32, "validation": 1},
+    "pl": {"train": 1024, "validation": 4},
+    "ps": {"train": 16, "validation": 1},
+    "pt": {"train": 1024, "validation": 4},
+    "ro": {"train": 1024, "validation": 2},
+    "ru": {"train": 4096, "validation": 32},
+    "ru-Latn": {"train": 32, "validation": 1},
+    "sd": {"train": 64, "validation": 1},
+    "si": {"train": 64, "validation": 1},
+    "sk": {"train": 512, "validation": 1},
+    "sl": {"train": 256, "validation": 1},
+    "sm": {"train": 4, "validation": 1},
+    "sn": {"train": 8, "validation": 1},
+    "so": {"train": 64, "validation": 1},
+    "sq": {"train": 128, "validation": 1},
+    "sr": {"train": 256, "validation": 1},
+    "st": {"train": 2, "validation": 1},
+    "su": {"train": 4, "validation": 1},
+    "sv": {"train": 1024, "validation": 2},
+    "sw": {"train": 32, "validation": 1},
+    "ta": {"train": 256, "validation": 1},
+    "te": {"train": 128, "validation": 1},
+    "tg": {"train": 64, "validation": 1},
+    "th": {"train": 1024, "validation": 1},
+    "tr": {"train": 1024, "validation": 4},
+    "uk": {"train": 1024, "validation": 2},
+    "und": {"train": 3072, "validation": 32},
+    "ur": {"train": 128, "validation": 1},
+    "uz": {"train": 32, "validation": 1},
+    "vi": {"train": 1024, "validation": 4},
+    "xh": {"train": 2, "validation": 1},
+    "yi": {"train": 16, "validation": 1},
+    "yo": {"train": 2, "validation": 1},
+    "zh": {"train": 1024, "validation": 2},
+    "zh-Latn": {"train": 8, "validation": 1},
+    "zu": {"train": 8, "validation": 1},
+}
+
+
+class Mc4Config(datasets.BuilderConfig):
+    """BuilderConfig for mC4."""
+
+    def __init__(self, *args, languages, **kwargs):
+        """BuilderConfig for mC4.
+        Args:
+            languages (:obj:`List[str]`): list of languages to load
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(
+            *args,
+            name="+".join(languages),
+            **kwargs,
+        )
+        self.languages = languages
+
+
+class Mc4(datasets.GeneratorBasedBuilder):
+    """mC4, a colossal, cleaned version of Common Crawl's web crawl corpus."""
+
+    BUILDER_CONFIGS = [Mc4Config(languages=[lang]) for lang in _LANGUAGES]
+    BUILDER_CONFIG_CLASS = Mc4Config
+
+    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"]:
+            data_urls[split] = [
+                _DATA_URL.format(
+                    language=self.config.name,
+                    split_suffix="-validation" if split == "validation" else "",
+                    index=index,
+                    n_shards=_N_SHARDS_PER_SPLIT[lang][split],
+                )
+                for lang in self.config.languages
+                for index in range(_N_SHARDS_PER_SPLIT[lang][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 _clean_prompts(self, example):
+        example["text"] = re.sub('[\<\>\»\|\🕔\◂\▸\෴]', '', example["text"])
+        example["text"] = re.sub('[a-zA-Z]+', '', example["text"])
+        example["text"] = re.sub('[\t\n]', ' ', example["text"])
+        example["text"] = re.sub('[\,\/\?\.\!\-\;\:\"\“\%\‘\”\�\_]{2,}', ' ', example["text"])
+        example["text"] = re.sub('[\s]{2,}', ' ', example["text"])
+        return example
+    
+    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 = self._clean_prompts(json.loads(line))                       
+                        yield id_, example
+                        id_ += 1