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Pass datasets trust_remote_code (#31406)

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* Pass datasets trust_remote_code

* Pass trust_remote_code in more tests

* Add trust_remote_dataset_code arg to some tests

* Revert "Temporarily pin datasets upper version to fix CI"

This reverts commit b7672826ca.

* Pass trust_remote_code in librispeech_asr_dummy docstrings

* Revert "Pin datasets<2.20.0 for examples"

This reverts commit 833fc17a3e.

* Pass trust_remote_code to all examples

* Revert "Add trust_remote_dataset_code arg to some tests" to research_projects

* Pass trust_remote_code to tests

* Pass trust_remote_code to docstrings

* Fix flax examples tests requirements

* Pass trust_remote_dataset_code arg to tests

* Replace trust_remote_dataset_code with trust_remote_code in one example

* Fix duplicate trust_remote_code

* Replace args.trust_remote_dataset_code with args.trust_remote_code

* Replace trust_remote_dataset_code with trust_remote_code in parser

* Replace trust_remote_dataset_code with trust_remote_code in dataclasses

* Replace trust_remote_dataset_code with trust_remote_code arg
This commit is contained in:
Albert Villanova del Moral
2024-06-17 18:29:13 +02:00
committed by GitHub
parent 485fd81471
commit a14b055b65
168 changed files with 804 additions and 410 deletions
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13
examples/pytorch/language-modeling/run_plm.py
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@@ -133,6 +133,16 @@ class DataTrainingArguments:
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether to trust the execution of code from datasets/models defined on the Hub."
" This option should only be set to `True` for repositories you trust and in which you have read the"
" code, as it will execute code present on the Hub on your local machine."
)
},
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
@@ -292,6 +302,7 @@ def main():
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
trust_remote_code=data_args.trust_remote_code,
)
if "validation" not in raw_datasets.keys():
raw_datasets["validation"] = load_dataset(
@@ -300,6 +311,7 @@ def main():
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
token=model_args.token,
trust_remote_code=data_args.trust_remote_code,
)
raw_datasets["train"] = load_dataset(
data_args.dataset_name,
@@ -307,6 +319,7 @@ def main():
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
token=model_args.token,
trust_remote_code=data_args.trust_remote_code,
)
else:
data_files = {}