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added cache_dir=model_args.cache_dir to all example with cache_dir arg (#11220)

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Philipp Schmid 2021-04-13 18:35:18 +02:00 committed by GitHub
parent 3312e96bfb
commit 9fa2995993
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12 changed files with 37 additions and 27 deletions
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6
examples/language-modeling/run_clm.py
View File

@ -230,17 +230,19 @@ def main():
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
else:
data_files = {}
@ -255,7 +257,7 @@ def main():
)
if extension == "txt":
extension = "text"
datasets = load_dataset(extension, data_files=data_files)
datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

6
examples/language-modeling/run_mlm.py
View File

@ -239,17 +239,19 @@ def main():
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
else:
data_files = {}
@ -260,7 +262,7 @@ def main():
extension = data_args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(extension, data_files=data_files)
datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

6
examples/language-modeling/run_mlm_flax.py
View File

@ -475,17 +475,19 @@ if __name__ == "__main__":
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
else:
data_files = {}
@ -496,7 +498,7 @@ if __name__ == "__main__":
extension = data_args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(extension, data_files=data_files)
datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

6
examples/language-modeling/run_plm.py
View File

@ -236,17 +236,19 @@ def main():
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
else:
data_files = {}
@ -257,7 +259,7 @@ def main():
extension = data_args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(extension, data_files=data_files)
datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

4
examples/multiple-choice/run_swag.py
View File

@ -268,10 +268,10 @@ def main():
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.train_file.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files)
datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
else:
# Downloading and loading the swag dataset from the hub.
datasets = load_dataset("swag", "regular")
datasets = load_dataset("swag", "regular", cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

4
examples/question-answering/run_qa.py
View File

@ -256,7 +256,7 @@ def main():
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
else:
data_files = {}
if data_args.train_file is not None:
@ -269,7 +269,7 @@ def main():
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files, field="data")
datasets = load_dataset(extension, data_files=data_files, field="data", cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

4
examples/question-answering/run_qa_beam_search.py
View File

@ -255,7 +255,7 @@ def main():
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
else:
data_files = {}
if data_args.train_file is not None:
@ -267,7 +267,7 @@ def main():
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files, field="data")
datasets = load_dataset(extension, data_files=data_files, field="data", cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

4
examples/seq2seq/run_summarization.py
View File

@ -310,7 +310,7 @@ def main():
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
else:
data_files = {}
if data_args.train_file is not None:
@ -322,7 +322,7 @@ def main():
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files)
datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

4
examples/seq2seq/run_translation.py
View File

@ -294,7 +294,7 @@ def main():
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
else:
data_files = {}
if data_args.train_file is not None:
@ -306,7 +306,7 @@ def main():
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files)
datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

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

@ -239,7 +239,7 @@ def main():
# download the dataset.
if data_args.task_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset("glue", data_args.task_name)
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
@ -263,10 +263,10 @@ def main():
if data_args.train_file.endswith(".csv"):
# Loading a dataset from local csv files
datasets = load_dataset("csv", data_files=data_files)
datasets = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir)
else:
# Loading a dataset from local json files
datasets = load_dataset("json", data_files=data_files)
datasets = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.

10
examples/text-classification/run_xnli.py
View File

@ -209,17 +209,19 @@ def main():
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
train_dataset = load_dataset("xnli", model_args.language, split="train")
train_dataset = load_dataset("xnli", model_args.language, split="train", cache_dir=model_args.cache_dir)
else:
train_dataset = load_dataset("xnli", model_args.train_language, split="train")
train_dataset = load_dataset(
"xnli", model_args.train_language, split="train", cache_dir=model_args.cache_dir
)
label_list = train_dataset.features["label"].names
if training_args.do_eval:
eval_dataset = load_dataset("xnli", model_args.language, split="validation")
eval_dataset = load_dataset("xnli", model_args.language, split="validation", cache_dir=model_args.cache_dir)
label_list = eval_dataset.features["label"].names
if training_args.do_predict:
test_dataset = load_dataset("xnli", model_args.language, split="test")
test_dataset = load_dataset("xnli", model_args.language, split="test", cache_dir=model_args.cache_dir)
label_list = test_dataset.features["label"].names
# Labels

4
examples/token-classification/run_ner.py
View File

@ -229,7 +229,7 @@ def main():
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
else:
data_files = {}
if data_args.train_file is not None:
@ -239,7 +239,7 @@ def main():
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.train_file.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files)
datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.