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Apply deprecated evaluation_strategy #588

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Apply deprecated evaluation_strategy #588

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10 changes: 5 additions & 5 deletions src/autotrain/app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -238,7 +238,7 @@ async def fetch_params(task: str, param_type: str):
"use_flash_attention_2",
"disable_gradient_checkpointing",
"logging_steps",
"evaluation_strategy",
"eval_strategy",
"save_total_limit",
"save_strategy",
"auto_find_batch_size",
Expand All @@ -264,7 +264,7 @@ async def fetch_params(task: str, param_type: str):
"auto_find_batch_size",
"save_total_limit",
"save_strategy",
"evaluation_strategy",
"eval_strategy",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "image-classification" and param_type == "basic":
Expand All @@ -277,7 +277,7 @@ async def fetch_params(task: str, param_type: str):
"auto_find_batch_size",
"save_total_limit",
"save_strategy",
"evaluation_strategy",
"eval_strategy",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "seq2seq" and param_type == "basic":
Expand All @@ -290,7 +290,7 @@ async def fetch_params(task: str, param_type: str):
"auto_find_batch_size",
"save_total_limit",
"save_strategy",
"evaluation_strategy",
"eval_strategy",
"quantization",
"lora_r",
"lora_alpha",
Expand All @@ -308,7 +308,7 @@ async def fetch_params(task: str, param_type: str):
"auto_find_batch_size",
"save_total_limit",
"save_strategy",
"evaluation_strategy",
"eval_strategy",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "dreambooth":
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2 changes: 1 addition & 1 deletion src/autotrain/cli/run_llm.py
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Expand Up @@ -139,7 +139,7 @@ def register_subcommand(parser: ArgumentParser):
"alias": ["--logging-steps"],
},
{
"arg": "--evaluation_strategy",
"arg": "--eval_strategy",
"help": "Evaluation strategy to use",
"required": False,
"type": str,
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2 changes: 1 addition & 1 deletion src/autotrain/trainers/clm/__main__.py
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Expand Up @@ -225,7 +225,7 @@ def train(config):
per_device_eval_batch_size=config.batch_size,
learning_rate=config.lr,
num_train_epochs=config.epochs,
evaluation_strategy=config.evaluation_strategy if config.valid_split is not None else "no",
eval_strategy=config.eval_strategy if config.valid_split is not None else "no",
logging_steps=logging_steps,
save_total_limit=config.save_total_limit,
save_strategy=config.save_strategy,
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2 changes: 1 addition & 1 deletion src/autotrain/trainers/clm/params.py
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Expand Up @@ -24,7 +24,7 @@ class LLMTrainingParams(AutoTrainParams):
log: str = Field("none", title="Logging using experiment tracking")
disable_gradient_checkpointing: bool = Field(False, title="Gradient checkpointing")
logging_steps: int = Field(-1, title="Logging steps")
evaluation_strategy: str = Field("epoch", title="Evaluation strategy")
eval_strategy: str = Field("epoch", title="Evaluation strategy")
save_total_limit: int = Field(1, title="Save total limit")
save_strategy: str = Field("no", title="Save strategy")
auto_find_batch_size: bool = Field(False, title="Auto find batch size")
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2 changes: 1 addition & 1 deletion src/autotrain/trainers/image_classification/__main__.py
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Expand Up @@ -125,7 +125,7 @@ def train(config):
per_device_eval_batch_size=2 * config.batch_size,
learning_rate=config.lr,
num_train_epochs=config.epochs,
evaluation_strategy=config.evaluation_strategy if config.valid_split is not None else "no",
eval_strategy=config.eval_strategy if config.valid_split is not None else "no",
logging_steps=logging_steps,
save_total_limit=config.save_total_limit,
save_strategy=config.save_strategy,
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2 changes: 1 addition & 1 deletion src/autotrain/trainers/image_classification/params.py
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Expand Up @@ -30,7 +30,7 @@ class ImageClassificationParams(AutoTrainParams):
token: Optional[str] = Field(None, title="Hub Token")
push_to_hub: bool = Field(False, title="Push to hub")
repo_id: Optional[str] = Field(None, title="Repo id")
evaluation_strategy: str = Field("epoch", title="Evaluation strategy")
eval_strategy: str = Field("epoch", title="Evaluation strategy")
image_column: str = Field("image", title="Image column")
target_column: str = Field("target", title="Target column")
log: str = Field("none", title="Logging using experiment tracking")
2 changes: 1 addition & 1 deletion src/autotrain/trainers/seq2seq/__main__.py
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Expand Up @@ -101,7 +101,7 @@ def train(config):
per_device_eval_batch_size=2 * config.batch_size,
learning_rate=config.lr,
num_train_epochs=config.epochs,
evaluation_strategy=config.evaluation_strategy if config.valid_split is not None else "no",
eval_strategy=config.eval_strategy if config.valid_split is not None else "no",
logging_steps=logging_steps,
save_total_limit=config.save_total_limit,
save_strategy=config.save_strategy,
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2 changes: 1 addition & 1 deletion src/autotrain/trainers/seq2seq/params.py
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Expand Up @@ -30,7 +30,7 @@ class Seq2SeqParams(AutoTrainParams):
weight_decay: float = Field(0.0, title="Weight decay")
max_grad_norm: float = Field(1.0, title="Max gradient norm")
logging_steps: int = Field(-1, title="Logging steps")
evaluation_strategy: str = Field("epoch", title="Evaluation strategy")
eval_strategy: str = Field("epoch", title="Evaluation strategy")
auto_find_batch_size: bool = Field(False, title="Auto find batch size")
mixed_precision: Optional[str] = Field(None, title="fp16, bf16, or None")
save_total_limit: int = Field(1, title="Save total limit")
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2 changes: 1 addition & 1 deletion src/autotrain/trainers/text_classification/__main__.py
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Expand Up @@ -132,7 +132,7 @@ def train(config):
per_device_eval_batch_size=2 * config.batch_size,
learning_rate=config.lr,
num_train_epochs=config.epochs,
evaluation_strategy=config.evaluation_strategy if config.valid_split is not None else "no",
eval_strategy=config.eval_strategy if config.valid_split is not None else "no",
logging_steps=logging_steps,
save_total_limit=config.save_total_limit,
save_strategy=config.save_strategy,
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2 changes: 1 addition & 1 deletion src/autotrain/trainers/text_classification/params.py
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Expand Up @@ -32,6 +32,6 @@ class TextClassificationParams(AutoTrainParams):
token: Optional[str] = Field(None, title="Hub Token")
push_to_hub: bool = Field(False, title="Push to hub")
repo_id: Optional[str] = Field(None, title="Repo id")
evaluation_strategy: str = Field("epoch", title="Evaluation strategy")
eval_strategy: str = Field("epoch", title="Evaluation strategy")
username: Optional[str] = Field(None, title="Hugging Face Username")
log: str = Field("none", title="Logging using experiment tracking")
2 changes: 1 addition & 1 deletion src/autotrain/trainers/token_classification/__main__.py
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Expand Up @@ -128,7 +128,7 @@ def train(config):
per_device_eval_batch_size=2 * config.batch_size,
learning_rate=config.lr,
num_train_epochs=config.epochs,
evaluation_strategy=config.evaluation_strategy if config.valid_split is not None else "no",
eval_strategy=config.eval_strategy if config.valid_split is not None else "no",
logging_steps=logging_steps,
save_total_limit=config.save_total_limit,
save_strategy=config.save_strategy,
Expand Down
2 changes: 1 addition & 1 deletion src/autotrain/trainers/token_classification/params.py
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Expand Up @@ -32,6 +32,6 @@ class TokenClassificationParams(AutoTrainParams):
token: Optional[str] = Field(None, title="Hub Token")
push_to_hub: bool = Field(False, title="Push to hub")
repo_id: Optional[str] = Field(None, title="Repo id")
evaluation_strategy: str = Field("epoch", title="Evaluation strategy")
eval_strategy: str = Field("epoch", title="Evaluation strategy")
username: Optional[str] = Field(None, title="Hugging Face Username")
log: str = Field("none", title="Logging using experiment tracking")