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Add token arugment in example scripts (#25172)

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* fix

* fix

* fix

* fix

* fix

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-08-02 11:17:31 +02:00
committed by GitHub
parent c6a8768dab
commit 149cb0cce2
43 changed files with 987 additions and 420 deletions
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30
examples/pytorch/audio-classification/run_audio_classification.py
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@@ -152,15 +152,21 @@ class ModelArguments:
attention_mask: bool = field(
default=True, metadata={"help": "Whether to generate an attention mask in the feature extractor."}
)
use_auth_token: bool = field(
default=False,
token: str = field(
default=None,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
)
},
)
use_auth_token: bool = field(
default=None,
metadata={
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
},
)
freeze_feature_extractor: Optional[bool] = field(
default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
)
@@ -198,6 +204,12 @@ def main():
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if model_args.use_auth_token is not None:
warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning)
if model_args.token is not None:
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
model_args.token = model_args.use_auth_token
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_audio_classification", model_args, data_args)
@@ -250,13 +262,13 @@ def main():
data_args.dataset_name,
data_args.dataset_config_name,
split=data_args.train_split_name,
use_auth_token=True if model_args.use_auth_token else None,
token=model_args.token,
)
raw_datasets["eval"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=data_args.eval_split_name,
use_auth_token=True if model_args.use_auth_token else None,
token=model_args.token,
)
if data_args.audio_column_name not in raw_datasets["train"].column_names:
@@ -280,7 +292,7 @@ def main():
return_attention_mask=model_args.attention_mask,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=True if model_args.use_auth_token else None,
token=model_args.token,
)
# `datasets` takes care of automatically loading and resampling the audio,
@@ -340,7 +352,7 @@ def main():
finetuning_task="audio-classification",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=True if model_args.use_auth_token else None,
token=model_args.token,
)
model = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path,
@@ -348,7 +360,7 @@ def main():
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=True if model_args.use_auth_token else None,
token=model_args.token,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)