import datasets from typing import List _DESCRIPTION = """\ Dataset for the shared baby language modeling task. The goal is to train a language model from scratch on this data which represents roughly the amount of text and speech data a young child observes. """ _HOMEPAGE = "https://babylm.github.io" filenames = [ "aochildes.txt", "bnc_spoken.txt", "cbt.txt", "children_stories.txt", "gutenberg.txt", "open_subtitles.txt", "qed.txt", "simple_wikipedia.txt", "switchboard.txt", "wikipedia.txt" ] class BabyLM(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ datasets.BuilderConfig( name="strict_small", description="Small version of the dataset with 10M words", version="1.0.0", ), datasets.BuilderConfig( name="strict", description="Full version of the dataset with 100M words", version="1.0.0", ), datasets.BuilderConfig( name="strict_small_gold", description="Small version of the dataset with 10M words and gold POS tags", version="1.0.0", ), datasets.BuilderConfig( name="strict_gold", description="Full version of the dataset with 100M words and gold POS tags", version="1.0.0", ) ] DEFAULT_CONFIG_NAME = "strict_small" def _info(self): features = datasets.Features( { "text": datasets.Value("string"), "tagged_text": datasets.Value("string"), "filename": datasets.Value("string"), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, features=features, # Here we define them above because they are different between the two configurations homepage=_HOMEPAGE, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """ Returns data for different splits """ if self.config.name == "strict_small": train_data_dir = "10M" else: train_data_dir = "100M" if 'gold' in self.config.name: folder = 'tagged_gold' else: folder = 'tagged' urls_to_download = { "train": [f"{folder}/{train_data_dir}/{fn}" for fn in filenames], "dev": [f"{folder}/dev/{fn}" for fn in filenames], "test": [f"{folder}/test/{fn}" for fn in filenames] } downloaded_files = dl_manager.download_and_extract(urls_to_download) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "split": "train", "filepaths": downloaded_files["train"]} ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "split": "dev", "filepaths": downloaded_files["dev"]} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "split": "test", "filepaths": downloaded_files["test"] } ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, split, filepaths): # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. # the filepaths should be a list of filepaths if isinstance(filepaths, str): filepaths = [filepaths] global_idx = 0 for filepath in filepaths: with open(filepath, encoding="utf-8") as f: filename = str(filepath.split("/")[-1]) is_tags = False text = "" # Every other row contains POS tags for row in f: if is_tags: yield global_idx, {"text": text, "tagged_text": row, "filename": filename} global_idx += 1 is_tags = False else: text = row is_tags = True