import datasets from typing import List _DESCRIPTION = """\ Dataset for the BabyLM Round2: French, German, Chinese & Japanese Small-Scale LMs 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. Author– Suchir Salhan """ _HOMEPAGE = "https://babylm.github.io" filenames = [ "aochildes.txt", "aochinese.txt", "aochinese_dev.txt", "aochinese_test.txt", "aofrench.txt", "aofrench_dev.txt", "aofrench_test.txt", "aogerman.txt", "aogerman_dev.txt", "aogerman_test.txt", "aojapanese.txt", "aojapanese_dev.txt", "aojapanese_test.txt", "bnc_spoken.txt", "cbt.txt", "children_stories.txt", "gutenberg.txt", "open_subtitles.txt", "qed.txt", "simple_wikipedia.txt", "switchboard.txt", "wikipedia.txt" ] en_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" ] #Suchir Salhan– addition of French, German, Japanese and Chinese dataset BUILDER_CONFIGS class BabyLM(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ datasets.BuilderConfig( name="original_strict_small", description="Original dataset, 10M words, no POS tags", version="1.0.0", ), datasets.BuilderConfig( name="strict_small", description="Cleaned version of the dataset, 10M words, no POS tags", version="1.0.0", ), datasets.BuilderConfig( name="original_strict", description="Original dataset, 100M words, no POS tags", version="1.0.0", ), datasets.BuilderConfig( name="strict", description="Cleaned version of the dataset, 100M words, unsupervised POS tags", version="1.0.0", ), datasets.BuilderConfig( name="original_strict_small_gold", description="Original dataset, 10M words, gold POS tags", version="1.0.0", ), datasets.BuilderConfig( name="strict_small_gold", description="Cleaned version of the dataset, 10M words, gold POS tags", version="1.0.0", ), datasets.BuilderConfig( name="original_strict_gold", description="Original dataset, 100M words, gold POS tags", version="1.0.0", ), datasets.BuilderConfig( name="strict_gold", description="Cleaned version of the dataset, 100M words, gold POS tags", version="1.0.0", ), datasets.BuilderConfig( name="fr_lang_small", #FRENCH description="FRENCH Cleaned version of the dataset, 10M words, unsupervised POS tags", version="1.0.0", ), datasets.BuilderConfig( name="ja_lang_small", description="GERMAN Cleaned version of the dataset, 10M words, unsupervised POS tags", version="1.0.0", ), datasets.BuilderConfig( name="zh_lang_small", description="JAPANESE Cleaned version of the dataset, 10M words, unsupervised POS tags", version="1.0.0", ), datasets.BuilderConfig( name="de_lang_small", description="GERMAN Cleaned version of the dataset, 10M words, unsupervised POS tags", version="1.0.0", ), datasets.BuilderConfig( name="fr_lang_gold", description="FRENCH Cleaned version of the dataset, 100M words, gold POS tags", version="1.0.0", ), datasets.BuilderConfig( name="ja_lang_gold", description="JAPANESE Cleaned version of the dataset, 100M words, gold POS tags", version="1.0.0", ), datasets.BuilderConfig( name="de_lang_gold", description="GERMAN Cleaned version of the dataset, 100M words, gold POS tags", version="1.0.0", ), datasets.BuilderConfig( name="zh_lang_gold", description="CHINESE Cleaned version of the dataset, 100M words, 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, ) #Suchir Salhan– addition of French, German, Japanese and Chinese datasets def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """ Returns data for different splits """ if "fr_lang_small" in self.config.name: train_data_dir = "FR" elif "de_lang_small" in self.config.name: train_data_dir = "DE" elif "zh_lang_small" in self.config.name: train_data_dir = "ZH" elif "ja_lang_small" in self.config.name: train_data_dir = "JA" elif "strict_small" in self.config.name: #default settings – English train_data_dir = "10M" else: train_data_dir = "100M" folder = 'original_tagged' if 'original' in self.config.name else 'clean_tagged' # folder = folder + '_gold' if 'gold' in self.config.name else folder #gold tags for french, german, japanese and english #modified urls to download urls_to_download = { "train": [], "dev": [], "test": [] } if 'fr_lang_small' in self.config.name: urls_to_download["train"].append(f"{folder}/FR/aofrench.txt") urls_to_download["dev"].append(f"{folder}/dev/aofrench_dev.txt") urls_to_download["test"].append(f"{folder}/test/aofrench_test.txt") elif 'de_lang_small' in self.config.name: urls_to_download["train"].append(f"{folder}/DE/aogerman.txt") urls_to_download["dev"].append(f"{folder}/dev/aogerman_dev.txt") urls_to_download["test"].append(f"{folder}/test/aogerman_test.txt") elif 'zh_lang_small' in self.config.name: urls_to_download["train"].append(f"{folder}/ZH/aochinese.txt") urls_to_download["dev"].append(f"{folder}/dev/aochinese_dev.txt") urls_to_download["test"].append(f"{folder}/test/aochinese_test.txt") elif 'ja_lang_small' in self.config.name: urls_to_download["train"].append(f"{folder}/JA/aojapanese.txt") urls_to_download["dev"].append(f"{folder}/dev/aojapanese_dev.txt") urls_to_download["test"].append(f"{folder}/test/aojapanese_test.txt") else: urls_to_download["train"] = [f"{folder}/10M/{fn}" for fn in en_filenames] urls_to_download["dev"] = [f"{folder}/dev/{fn}" for fn in en_filenames] urls_to_download["test"] = [f"{folder}/test/{fn}" for fn in en_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: is_tags = False text = "" filename = "" # Every other row contains POS tags. First row is the filename (we can't use filepath since the file path changes upon caching) for row in f: if filename == "": filename = row.strip() continue if is_tags: yield global_idx, {"text": text.strip(), "tagged_text": row.strip(), "filename": filename} global_idx += 1 is_tags = False else: text = row is_tags = True