ctheodoris jackkuo commited on
Commit
42dbf0a
·
verified ·
1 Parent(s): d471d1b

fix TypeError: TrainingArguments.__init__() got an unexpected keyword argument 'evaluation_strategy' (#539)

Browse files

- fix TypeError: TrainingArguments.__init__() got an unexpected keyword argument 'evaluation_strategy' (af5699d3d03a21594973b7159b1b1c3d85fca067)


Co-authored-by: Jack Kuo <[email protected]>

Files changed (1) hide show
  1. geneformer/classifier.py +6 -8
geneformer/classifier.py CHANGED
@@ -1063,11 +1063,10 @@ class Classifier:
1063
  def_training_args["logging_steps"] = logging_steps
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  def_training_args["output_dir"] = output_directory
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  if eval_data is None:
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- if transformers_version >= parse("4.46"):
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- def_training_args["eval_strategy"] = "no"
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- else:
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- def_training_args["evaluation_strategy"] = "no"
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  def_training_args["load_best_model_at_end"] = False
 
 
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  def_training_args.update(
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  {"save_strategy": "epoch", "save_total_limit": 1}
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  ) # only save last model for each run
@@ -1237,11 +1236,10 @@ class Classifier:
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  def_training_args["logging_steps"] = logging_steps
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  def_training_args["output_dir"] = output_directory
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  if eval_data is None:
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- if transformers_version >= parse("4.46"):
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- def_training_args["eval_strategy"] = "no"
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- else:
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- def_training_args["evaluation_strategy"] = "no"
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  def_training_args["load_best_model_at_end"] = False
 
 
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  training_args_init = TrainingArguments(**def_training_args)
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  if self.freeze_layers is not None:
 
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  def_training_args["logging_steps"] = logging_steps
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  def_training_args["output_dir"] = output_directory
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  if eval_data is None:
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+ def_training_args["evaluation_strategy"] = "no"
 
 
 
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  def_training_args["load_best_model_at_end"] = False
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+ if transformers_version >= parse("4.46"):
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+ def_training_args["eval_strategy"] = def_training_args.pop("evaluation_strategy")
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  def_training_args.update(
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  {"save_strategy": "epoch", "save_total_limit": 1}
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  ) # only save last model for each run
 
1236
  def_training_args["logging_steps"] = logging_steps
1237
  def_training_args["output_dir"] = output_directory
1238
  if eval_data is None:
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+ def_training_args["evaluation_strategy"] = "no"
 
 
 
1240
  def_training_args["load_best_model_at_end"] = False
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+ if transformers_version >= parse("4.46"):
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+ def_training_args["eval_strategy"] = def_training_args.pop("evaluation_strategy")
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  training_args_init = TrainingArguments(**def_training_args)
1244
 
1245
  if self.freeze_layers is not None: