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Add ByT5 option to example run_t5_mlm_flax.py (#12634)

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* Allow ByT5 type in Flax T5 script

* use T5TokenizerFast

* change up tokenizer config

* model_args

* reorder imports

* Update run_t5_mlm_flax.py
This commit is contained in:
Nick Doiron
2021-07-13 08:39:57 -04:00
committed by GitHub
parent 9da1acaea2
commit 5803a2a7ac
1 changed files with 3 additions and 3 deletions
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6
examples/flax/language-modeling/run_t5_mlm_flax.py
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@@ -42,12 +42,12 @@ from flax.training.common_utils import get_metrics, onehot, shard
from transformers import ( from transformers import (
CONFIG_MAPPING, CONFIG_MAPPING,
FLAX_MODEL_FOR_MASKED_LM_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING,
AutoTokenizer,
BatchEncoding, BatchEncoding,
FlaxT5ForConditionalGeneration, FlaxT5ForConditionalGeneration,
HfArgumentParser, HfArgumentParser,
PreTrainedTokenizerBase, PreTrainedTokenizerBase,
T5Config, T5Config,
T5TokenizerFast,
TrainingArguments, TrainingArguments,
is_tensorboard_available, is_tensorboard_available,
set_seed, set_seed,
@@ -477,11 +477,11 @@ if __name__ == "__main__":
# Load pretrained model and tokenizer # Load pretrained model and tokenizer
if model_args.tokenizer_name: if model_args.tokenizer_name:
tokenizer = T5TokenizerFast.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
) )
elif model_args.model_name_or_path: elif model_args.model_name_or_path:
tokenizer = T5TokenizerFast.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
) )
else: else: