Black preview (#17217)
* Black preview * Fixup too! * Fix check copies * Use the same version as the CI * Bump black
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@@ -73,8 +73,9 @@ class ModelArguments:
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": "The model checkpoint for weights initialization."
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"Don't set if you want to train a model from scratch."
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"help": (
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"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
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)
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},
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)
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model_type: Optional[str] = field(
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@@ -84,8 +85,10 @@ class ModelArguments:
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config_overrides: Optional[str] = field(
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default=None,
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metadata={
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"help": "Override some existing default config settings when a model is trained from scratch. Example: "
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"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
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"help": (
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"Override some existing default config settings when a model is trained from scratch. Example: "
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"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
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)
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},
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)
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config_name: Optional[str] = field(
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@@ -109,8 +112,10 @@ 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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@@ -141,24 +146,30 @@ class DataTrainingArguments:
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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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block_size: Optional[int] = field(
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default=None,
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metadata={
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"help": "Optional input sequence length after tokenization. "
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"The training dataset will be truncated in block of this size for training. "
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"Default to the model max input length for single sentence inputs (take into account special tokens)."
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"help": (
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"Optional input sequence length after tokenization. "
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"The training dataset will be truncated in block of this size for training. "
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"Default to the model max input length for single sentence inputs (take into account special tokens)."
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)
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},
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)
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overwrite_cache: bool = field(
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@@ -390,7 +401,8 @@ def main():
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# clm input could be much much longer than block_size
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if "Token indices sequence length is longer than the" in cl.out:
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tok_logger.warning(
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"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
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"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
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" before being passed to the model."
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)
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return output
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