"""Perplexity Sampled mC4 dataset based on Common Crawl.""" import gzip import json import datasets try: import kenlm # pip install https://github.com/kpu/kenlm/archive/master.zip except ImportError: import warnings KENLM_IMPORT = ( "To be able to use bertin-project/mc4-sampling, you need to install the following dependency: kenlm.\n" "Please install it using 'pip install https://github.com/kpu/kenlm/archive/master.zip' for instance." ) kenlm = None warnings.warn(KENLM_IMPORT) import numpy as np from numpy.random import default_rng logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """\ A sampling-enabled version of mC4, the colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is a version of the processed version of Google's mC4 dataset by AllenAI, in which sampling methods are implemented to perform on the fly. """ _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 Mc4SamplingConfig(datasets.BuilderConfig): """BuilderConfig for mC4 Sampling.""" def __init__(self, languages, *args, **kwargs): """BuilderConfig for mC4 Sampling. 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 Mc4Sampling(datasets.GeneratorBasedBuilder): """mC4 Sampling, a colossal, cleaned version of Common Crawl's web crawl corpus.""" BUILDER_CONFIGS = [Mc4SamplingConfig(languages=[lang]) for lang in _LANGUAGES] BUILDER_CONFIG_CLASS = Mc4SamplingConfig def __init__(self, *args, **kwargs): self.data_files = kwargs.get("data_files", {}) self.sampling_method = kwargs.pop("sampling_method", None) self.perplexity_model = kwargs.pop("perplexity_model", None) self.sampling_factor = kwargs.pop("sampling_factor", None) self.boundaries = kwargs.pop("boundaries", None) self.seed = kwargs.pop("seed", None) self.kwargs = kwargs if self.sampling_method: if self.seed is not None: self.rng = default_rng(self.seed) else: self.rng = default_rng() if self.sampling_method == "random": self.should_keep_doc = self._should_keep_doc_random else: # Loading 5-gram model # http://dl.fbaipublicfiles.com/cc_net/lm/es.arpa.bin logger.info("loading model = %s", str(self.perplexity_model)) if isinstance(self.perplexity_model, str): if not kenlm: raise ImportError(KENLM_IMPORT) self.pp_model = kenlm.Model(self.perplexity_model) else: self.pp_model = self.perplexity_model if self.sampling_method == "gaussian": self.should_keep_doc = self._should_keep_doc_gaussian else: self.should_keep_doc = self._should_keep_doc_step # init_kwargs = { # prop: kwargs.get(prop) # for prop in ("name", "version", "data_dir", "data_files", "description") # } super().__init__(*args, **kwargs) def get_perplexity(self, doc): doc_log_score, doc_length = 0, 0 for line in doc.split("\n"): log_score = self.pp_model.score(line) length = len(line.split()) + 1 doc_log_score += log_score doc_length += length return 10.0 ** (-doc_log_score / doc_length) def _should_keep_doc_step(self, doc, factor=None, boundaries=None, **kwargs): perplexity = self.get_perplexity(doc) factor = 1.5e5 if factor is None else factor if boundaries is None: boundaries = [536394.99320948, 662247.50212365, 919250.87225178] if perplexity <= boundaries[0]: quartile_range = boundaries[0] elif boundaries[0] < perplexity < boundaries[1]: quartile_range = boundaries[1] - boundaries[0] elif boundaries[1] < perplexity < boundaries[2]: quartile_range = boundaries[2] - boundaries[1] elif perplexity >= boundaries[2]: quartile_range = 10 * boundaries[2] probability = factor / quartile_range return self.rng.uniform() < probability def _should_keep_doc_gaussian(self, doc, factor=None, width=None, boundaries=None, **kwargs): perplexity = self.get_perplexity(doc) width = (9 / 2) if width is None else width # width (spread) of the exponential curve factor = 0.78 if factor is None else factor if boundaries is not None: m = boundaries[1] else: m = 662247.50212365 exponential = np.exp((-1 / width) * ((perplexity - m) / m) ** 2) weighted_perplexity = factor * exponential return self.rng.uniform() < weighted_perplexity def _should_keep_doc_random(self, doc, factor=None, **kwargs): factor = 0.5 if factor is None else factor return self.rng.uniform() <= factor 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=lang, 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]) ] if self.data_files and "train" in self.data_files: train_downloaded_files = self.data_files["train"] if not isinstance(train_downloaded_files, (tuple, list)): train_downloaded_files = [train_downloaded_files] else: train_downloaded_files = dl_manager.download(data_urls["train"]) if self.data_files and "validation" in self.data_files: validation_downloaded_files = self.data_files["validation"] if not isinstance(validation_downloaded_files, (tuple, list)): validation_downloaded_files = [validation_downloaded_files] else: 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) if filepath.endswith("jsonl") or filepath.endswith("json"): with open(filepath, "r", encoding="utf-8") as f: for line in f: if line: example = json.loads(line) yield id_, example id_ += 1 else: with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f: if self.sampling_method: logger.info("sampling method = %s", self.sampling_method) for line in f: if line: example = json.loads(line) if self.should_keep_doc( example["text"], **self.kwargs): yield id_, example id_ += 1 else: for line in f: if line: example = json.loads(line) yield id_, example id_ += 1