from datasets import load_dataset, load_from_disk from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer from transformers import AutoConfig model_dir = "./" # ${MODEL_DIR} # load roberta-large config config = AutoConfig.from_pretrained("roberta-large") config.save_pretrained(model_dir) # load dataset #dataset = load_dataset("oscar", "unshuffled_deduplicated_fi", split="train") dataset = load_from_disk("DATASET_PATH_HERE") dataset = dataset["train"] # Instantiate tokenizer tokenizer = ByteLevelBPETokenizer() def batch_iterator(batch_size=1000): for i in range(0, len(dataset), batch_size): yield dataset[i: i + batch_size]["text"] # Customized training tokenizer.train_from_iterator(batch_iterator(), vocab_size=config.vocab_size, min_frequency=2, special_tokens=[ "", "", "", "", "", ]) # Save files to disk tokenizer.save(f"{model_dir}/tokenizer.json")