Black preview (#17217)
* Black preview * Fixup too! * Fix check copies * Use the same version as the CI * Bump black
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@@ -85,8 +85,10 @@ class DataTrainingArguments:
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max_seq_length: int = field(
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default=128,
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metadata={
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"help": "The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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"help": (
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"The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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)
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},
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)
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overwrite_cache: bool = field(
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@@ -95,30 +97,38 @@ class DataTrainingArguments:
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pad_to_max_length: bool = field(
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default=False,
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metadata={
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"help": "Whether to pad all samples to `max_seq_length`. "
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"If False, will pad the samples dynamically when batching to the maximum length in the batch."
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"Data will always be padded when using TPUs."
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"help": (
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"Whether to pad all samples to `max_seq_length`. "
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"If False, will pad the samples dynamically when batching to the maximum length in the batch."
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"Data will always be padded when using TPUs."
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)
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},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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"help": (
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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)
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},
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)
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max_val_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
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"value if set."
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"help": (
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"For debugging purposes or quicker training, truncate the number of validation examples to this "
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"value if set."
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)
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},
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)
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max_test_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of test examples to this "
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"value if set."
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"help": (
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"For debugging purposes or quicker training, truncate the number of test examples to this "
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"value if set."
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)
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},
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)
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@@ -162,8 +172,10 @@ class ModelArguments:
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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"help": (
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"Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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)
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},
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)
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@@ -330,8 +342,8 @@ def main():
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else:
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logger.warning(
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"Your model seems to have been trained with labels, but they don't match the dataset: ",
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f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
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"\nIgnoring the model labels as a result.",
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f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels:"
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f" {list(sorted(label_list))}.\nIgnoring the model labels as a result.",
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)
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label_to_id = {v: i for i, v in enumerate(label_list)}
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elif not is_regression:
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