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"""CONFIG"""
#!/usr/bin/env python3
from transformers import RobertaConfig
config = RobertaConfig.from_pretrained("roberta-large")
config.save_pretrained("./")
"""TOKENIZER"""
#!/usr/bin/env python3
from datasets import load_dataset
from tokenizers import ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("large_spanish_corpus")
# 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=50265, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./tokenizer.json")
"""TOKENIZER"""
#!/usr/bin/env bash
./run_mlm_flax.py \
--output_dir="./" \
--model_type="roberta" \
--config_name="./" \
--tokenizer_name="./" \
--dataset_name="large_spanish_corpus" \
--dataset_config_name \ # I think this would be empty
--max_seq_length="128" \
--per_device_train_batch_size="4" \
--per_device_eval_batch_size="4" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="8" \
--push_to_hub
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