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from datasets import load_dataset, concatenate_datasets
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
model_dir = "./scandinavian" # ${MODEL_DIR}
# load dataset
# dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"])
# yoruba_dataset = load_dataset("mc4", "yo", split="train[0:10]")
# yoruba_dataset2 = load_dataset("mc4", "yo", split="train[10:20]")
danish_dataset = load_dataset("mc4", "da") # , download_mode="force_redownload")
norwegian_dataset = load_dataset("mc4", "no") # , download_mode="force_redownload")
swedish_dataset = load_dataset("mc4", "sv") # , download_mode="force_redownload")
# all_datasets = concatenate_datasets([yoruba_dataset, yoruba_dataset2])
all_datasets = concatenate_datasets([danish_dataset, norwegian_dataset, swedish_dataset])
all_datasets = all_datasets.shuffle()
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(all_datasets), batch_size):
yield all_datasets[i : i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(
batch_iterator(),
vocab_size=50265,
min_frequency=2,
special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
],
)
# Save files to disk
tokenizer.save(f"{model_dir}/tokenizer.json")
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