| from datasets import load_from_disk | |
| from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer | |
| from transformers import AutoTokenizer | |
| model_dir = "./" | |
| # load dataset | |
| dataset = load_from_disk("/researchdisk/training_dataset_full_deduplicated") | |
| 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=50257, min_frequency=2, special_tokens=[ | |
| "<s>", | |
| "<pad>", | |
| "</s>", | |
| "<unk>", | |
| "<mask>", | |
| ]) | |
| # Save files to disk | |
| tokenizer.save(f"{model_dir}/tokenizer.json") | |
| tokenizer = AutoTokenizer.from_pretrained(model_dir) | |
| tokenizer.save_pretrained(model_dir) |