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from datasets import concatenate_datasets, load_dataset, load_from_disk
import argparse
from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
from transformers import GPT2TokenizerFast, AutoTokenizer
from datasets import config
from datasets import DatasetDict, Dataset
import logging
def initialize_logger(log_file):
logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s')
def log_parameters(vocab_size, batch_size, fertility_score, proportion_continued_words, log_file='parameters.log'):
initialize_logger(log_file)
logging.info(f"Vocabulary Size: {vocab_size}, Batch Size: {batch_size}, Fertility Score: {fertility_score}, Proportion of Continued word: {proportion_continued_words}")
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument(
"--batch_size",
type=int,
required=True,
help="Batch size to use for training"
)
parser.add_argument(
"--vocab_size",
type=int,
required=True,
help="Vocabulary size to use for tokenizer"
)
parser.add_argument(
"--use_config",
choices=['xlm-roberta', 'vanilla','gemma'],
required=True,
help="Use XLM-RoBERTa config or Vanilla BPE"
)
parser.add_argument(
"--do_evaluate",
action='store_true',
help="Enable evaluation."
)
args = parser.parse_known_args()
return args
def train_tokenizer(args):
# indic_datasets_hi= load_dataset('satpalsr/indicCorpv2', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')['text'][:20502390]
indic_datasets_en= load_dataset('satpalsr/indicCorpv2', 'en', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')['text'][:205090]
# indic_datasets_bn= load_dataset('satpalsr/indicCorpv2', 'bn', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')['text'][:20502390]
# combined_train_set= indic_datasets_hi + indic_datasets_en + indic_datasets_bn
combined_train_set=indic_datasets_en
data = {
"train":{"text": combined_train_set},
"validation": {"text": []},
"test": {"text": []},
}
# print(data)
custom_dataset = DatasetDict()
for split in data:
custom_dataset[split] = Dataset.from_dict(data[split])
custom_dataset=custom_dataset["train"]
def batch_iterator():
for idx in range(0, len(custom_dataset), args.batch_size):
yield custom_dataset[idx: idx + args.batch_size]['text']
if args.use_config == 'vanilla':
tokenizer = Tokenizer(models.BPE())
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
print(f"[INFO] The brown fox jumped over the lazy dog\n{tokenizer.pre_tokenizer.pre_tokenize_str('The brown fox jumped over the lazy dog')}")
print(f"[INFO] Training...")
trainer = trainers.BpeTrainer(vocab_size=args.vocab_size, special_tokens=["<|endoftext|>"])
tokenizer.train_from_iterator(batch_iterator(), trainer=trainer)
tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)
tokenizer.decoder = decoders.ByteLevel()
tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)
elif args.use_config == 'xlm-roberta':
print("skipped")
tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')
trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size)
elif args.use_config == 'gemma':
print("skipped")
tokenizer = AutoTokenizer.from_pretrained('hf-internal-testing/dummy-gemma')
trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size)
trained_tokenizer.save_pretrained('hi-indiccorp-gemma-bgpt-bpe-tokenizer1')
print(f"[INFO] Tokenizer saved to disk")
# test_hi= load_dataset('ai4bharat/samanantar', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
# test_bn= load_dataset('ai4bharat/samanantar', 'bn', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
# print("-------------------------------")
# print(test_hi)
# print(test_bn)
# print("-------------------------------")
# print(len(test_hi["tgt"]))
# print(len(test_bn["tgt"]))
def main():
args, _ = parse_arguments()
train_tokenizer(args)
if __name__ == "__main__":
main()
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