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Update use_auth_token -> token in example scripts (#25167)

Browse Source 
* pytorch examples

* tensorflow examples

* flax examples

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-07-28 15:33:45 +02:00
committed by GitHub
parent 3cbc560d03
commit d53b8ad780
38 changed files with 116 additions and 116 deletions
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6
examples/pytorch/audio-classification/run_audio_classification.py
View File

@@ -280,7 +280,7 @@ def main():
return_attention_mask=model_args.attention_mask,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
# `datasets` takes care of automatically loading and resampling the audio,
@@ -340,7 +340,7 @@ def main():
finetuning_task="audio-classification",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path,
@@ -348,7 +348,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)

4
examples/pytorch/contrastive-image-text/run_clip.py
View File

@@ -336,14 +336,14 @@ def main():
model_args.image_processor_name or model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModel.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
config = model.config

6
examples/pytorch/image-classification/run_image_classification.py
View File

@@ -276,7 +276,7 @@ def main():
finetuning_task="image-classification",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path,
@@ -284,14 +284,14 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)
image_processor = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
# Define torchvision transforms to be applied to each image.

2
examples/pytorch/image-pretraining/run_mae.py
View File

@@ -280,7 +280,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
else:
logger.info("Training new model from scratch")

2
examples/pytorch/image-pretraining/run_mim.py
View File

@@ -357,7 +357,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
else:
logger.info("Training new model from scratch")

2
examples/pytorch/image-pretraining/run_mim_no_trainer.py
View File

@@ -497,7 +497,7 @@ def main():
config=config,
cache_dir=args.cache_dir,
revision=args.model_revision,
use_auth_token=True if args.use_auth_token else None,
token=True if args.use_auth_token else None,
)
else:
logger.info("Training new model from scratch")

2
examples/pytorch/language-modeling/run_clm.py
View File

@@ -415,7 +415,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
torch_dtype=torch_dtype,
low_cpu_mem_usage=model_args.low_cpu_mem_usage,
)

2
examples/pytorch/language-modeling/run_mlm.py
View File

@@ -403,7 +403,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
low_cpu_mem_usage=model_args.low_cpu_mem_usage,
)
else:

2
examples/pytorch/language-modeling/run_plm.py
View File

@@ -383,7 +383,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
low_cpu_mem_usage=model_args.low_cpu_mem_usage,
)
else:

6
examples/pytorch/multiple-choice/run_swag.py
View File

@@ -314,14 +314,14 @@ def main():
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path,
@@ -329,7 +329,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
# When using your own dataset or a different dataset from swag, you will probably need to change this.

6
examples/pytorch/question-answering/run_qa.py
View File

@@ -322,14 +322,14 @@ def main():
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=True,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForQuestionAnswering.from_pretrained(
model_args.model_name_or_path,
@@ -337,7 +337,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
# Tokenizer check: this script requires a fast tokenizer.

6
examples/pytorch/question-answering/run_qa_beam_search.py
View File

@@ -320,13 +320,13 @@ def main():
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer = XLNetTokenizerFast.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = XLNetForQuestionAnswering.from_pretrained(
model_args.model_name_or_path,
@@ -334,7 +334,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
# Preprocessing the datasets.

6
examples/pytorch/question-answering/run_seq2seq_qa.py
View File

@@ -367,14 +367,14 @@ def main():
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForSeq2SeqLM.from_pretrained(
model_args.model_name_or_path,
@@ -382,7 +382,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch

6
examples/pytorch/semantic-segmentation/run_semantic_segmentation.py
View File

@@ -379,7 +379,7 @@ def main():
id2label=id2label,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForSemanticSegmentation.from_pretrained(
model_args.model_name_or_path,
@@ -387,13 +387,13 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
image_processor = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
# Define torchvision transforms to be applied to each image + target.

8
examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
View File

@@ -370,7 +370,7 @@ def main():
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
@@ -383,21 +383,21 @@ def main():
model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_args.model_name_or_path,
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
if model.config.decoder_start_token_id is None:

6
examples/pytorch/summarization/run_summarization.py
View File

@@ -417,14 +417,14 @@ def main():
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForSeq2SeqLM.from_pretrained(
model_args.model_name_or_path,
@@ -432,7 +432,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch

6
examples/pytorch/text-classification/run_glue.py
View File

@@ -361,14 +361,14 @@ def main():
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
@@ -376,7 +376,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)

6
examples/pytorch/text-classification/run_xnli.py
View File

@@ -278,7 +278,7 @@ def main():
finetuning_task="xnli",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -286,7 +286,7 @@ def main():
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
@@ -294,7 +294,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)

8
examples/pytorch/token-classification/run_ner.py
View File

@@ -348,7 +348,7 @@ def main():
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
@@ -358,7 +358,7 @@ def main():
cache_dir=model_args.cache_dir,
use_fast=True,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
add_prefix_space=True,
)
else:
@@ -367,7 +367,7 @@ def main():
cache_dir=model_args.cache_dir,
use_fast=True,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForTokenClassification.from_pretrained(
@@ -376,7 +376,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)

6
examples/pytorch/translation/run_translation.py
View File

@@ -366,14 +366,14 @@ def main():
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
model = AutoModelForSeq2SeqLM.from_pretrained(
model_args.model_name_or_path,
@@ -381,7 +381,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
token=True if model_args.use_auth_token else None,
)
# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch