Black preview (#17217)
* Black preview * Fixup too! * Fix check copies * Use the same version as the CI * Bump black
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@@ -100,15 +100,19 @@ class FTDataArguments:
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max_length: Optional[int] = dataclasses.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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pad_to_max_length: Optional[bool] = dataclasses.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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"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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)
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},
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)
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@@ -147,7 +151,10 @@ class FTTrainingArguments:
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weight_decay: Optional[float] = dataclasses.field(
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default=0.0,
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metadata={
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"help": "The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in [`AdamW`] optimizer."
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"help": (
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"The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in"
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" [`AdamW`] optimizer."
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)
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},
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)
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learning_rate: Optional[float] = dataclasses.field(
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@@ -157,13 +164,18 @@ class FTTrainingArguments:
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gradient_accumulation_steps: Optional[int] = dataclasses.field(
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default=1,
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metadata={
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"help": "Number of updates steps to accumulate the gradients for, before performing a backward/update pass."
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"help": (
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"Number of updates steps to accumulate the gradients for, before performing a backward/update pass."
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)
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},
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)
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max_steps: Optional[int] = dataclasses.field(
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default=-1,
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metadata={
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"help": "If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`."
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"help": (
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"If set to a positive number, the total number of training steps to perform. Overrides"
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" `num_train_epochs`."
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)
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},
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)
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lr_scheduler_type: Optional[str] = dataclasses.field(
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@@ -172,7 +184,10 @@ class FTTrainingArguments:
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warmup_steps: Optional[int] = dataclasses.field(
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default=1,
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metadata={
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"help": "Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`."
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"help": (
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"Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of"
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" `warmup_ratio`."
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)
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},
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)
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evaluation_strategy: Optional[str] = dataclasses.field(
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