Black preview (#17217)
* Black preview * Fixup too! * Fix check copies * Use the same version as the CI * Bump black
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@@ -103,8 +103,10 @@ 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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@@ -148,29 +150,37 @@ class DataTrainingArguments:
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max_seq_length: int = field(
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default=None,
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metadata={
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"help": "The maximum total input sequence length after tokenization. If set, 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. If set, 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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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_eval_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 evaluation 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 evaluation 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_predict_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 prediction 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 prediction 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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@@ -585,7 +595,8 @@ def main():
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write_train_metric(summary_writer, train_metrics, train_time, cur_step)
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epochs.write(
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f"Step... ({cur_step}/{total_steps} | Training Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
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f"Step... ({cur_step}/{total_steps} | Training Loss: {train_metric['loss']}, Learning Rate:"
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f" {train_metric['learning_rate']})"
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)
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train_metrics = []
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