Setfit does not work with transformers dev-version with error "'CallbackHandler' object has no attribute 'tokenizer'"

[WeichenXu123]

Repro:

transformers: pip install git+https://github.com/huggingface/transformers setfit: pip install setfit==1.1.0

code:

    dataset = load_dataset("sst2")

    train_dataset = sample_dataset(dataset["train"], label_column="label", num_samples=8)
    eval_dataset = dataset["validation"]

    model = SetFitModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

    training_args = SetFitTrainingArguments(
        loss=CosineSimilarityLoss,
        batch_size=16,
        num_iterations=5,
        num_epochs=1,
        report_to="none",
    )

    # TODO: Remove this once https://github.com/huggingface/setfit/issues/512
    #   is resolved. This is a workaround during the deprecation of the
    #   evaluation_strategy argument is being addressed in the SetFit library.
    training_args.eval_strategy = training_args.evaluation_strategy

    trainer = SetFitTrainer(
        model=model,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        metric="accuracy",
        column_mapping={"sentence": "text", "label": "label"},
        args=training_args,
    )

SetFitTrainer constructor Raises error:

AttributeError: 'CallbackHandler' object has no attribute 'tokenizer'

Stack trace:

/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/setfit/trainer.py:328: in __init__
    self.st_trainer = BCSentenceTransformersTrainer(
        args       = TrainingArguments(output_dir='checkpoints', max_steps=-1, sampling_strategy='oversampling', num_iterations=5, head_lea...al_delay=0, eval_max_steps=-1, save_strategy='steps', save_steps=500, save_total_limit=1, load_best_model_at_end=False)
        callbacks  = [<setfit.model_card.ModelCardCallback object at 0x7fdd193972b0>]
        column_mapping = {'label': 'label', 'sentence': 'text'}
        eval_dataset = Dataset({
    features: ['feat_idx', 'text', 'label'],
    num_rows: 872
})
        metric     = 'accuracy'
        metric_kwargs = None
        model      = <setfit.modeling.SetFitModel object at 0x7fdd5a0bf9d0>
        model_init = None
        self       = <setfit.trainer.Trainer object at 0x7fdd54daf940>
        train_dataset = Dataset({
    features: ['feat_idx', 'text', 'label'],
    num_rows: 16
})
/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/setfit/trainer.py:94: in __init__
    self.callback_handler.on_init_end(self.args, self.state, self.control)
        __class__  = <class 'setfit.trainer.BCSentenceTransformersTrainer'>
        callback   = <setfit.model_card.ModelCardCallback object at 0x7fdd193972b0>
        callbacks  = [<setfit.model_card.ModelCardCallback object at 0x7fdd193972b0>]
        kwargs     = {}
        overwritten_call_event = <function BCSentenceTransformersTrainer.__init__.<locals>.overwritten_call_event at 0x7fdd1938eca0>
        self       = <setfit.trainer.BCSentenceTransformersTrainer object at 0x7fdd19397a60>
        setfit_args = TrainingArguments(output_dir='checkpoints', max_steps=-1, sampling_strategy='oversampling', num_iterations=5, head_lea...al_delay=0, eval_max_steps=-1, save_strategy='steps', save_steps=500, save_total_limit=1, load_best_model_at_end=False)
        setfit_model = <setfit.modeling.SetFitModel object at 0x7fdd5a0bf9d0>
/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/transformers/trainer_callback.py:464: in on_init_end
    return self.call_event("on_init_end", args, state, control)
        args       = SentenceTransformerTrainingArguments(output_dir='checkpoints', overwrite_output_dir=False, do_train=False, do_eval=Fal....BATCH_SAMPLER: 'batch_sampler'>, multi_dataset_batch_sampler=<MultiDatasetBatchSamplers.PROPORTIONAL: 'proportional'>)
        control    = TrainerControl(should_training_stop=False, should_epoch_stop=False, should_save=False, should_evaluate=False, should_log=False)
        self       = <transformers.trainer_callback.CallbackHandler object at 0x7fdd04d53280>
        state      = TrainerState(epoch=None, global_step=0, max_steps=0, logging_steps=500, eval_steps=500, save_steps=500, train_batch_si..., 'should_epoch_stop': False, 'should_save': False, 'should_evaluate': False, 'should_log': False}, 'attributes': {}}})
/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/setfit/trainer.py:87: in <lambda>
    self.callback_handler.call_event = lambda *args, **kwargs: overwritten_call_event(
        args       = ('on_init_end', SentenceTransformerTrainingArguments(output_dir='checkpoints', overwrite_output_dir=False, do_train=Fa...ntrol(should_training_stop=False, should_epoch_stop=False, should_save=False, should_evaluate=False, should_log=False))
        kwargs     = {}
        overwritten_call_event = <function BCSentenceTransformersTrainer.__init__.<locals>.overwritten_call_event at 0x7fdd1938eca0>
        self       = <setfit.trainer.BCSentenceTransformersTrainer object at 0x7fdd19397a60>
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <transformers.trainer_callback.CallbackHandler object at 0x7fdd04d53280>
event = 'on_init_end'
args = SentenceTransformerTrainingArguments(output_dir='checkpoints', overwrite_output_dir=False, do_train=False, do_eval=Fal....BATCH_SAMPLER: 'batch_sampler'>, multi_dataset_batch_sampler=<MultiDatasetBatchSamplers.PROPORTIONAL: 'proportional'>)
state = TrainerState(epoch=None, global_step=0, max_steps=0, logging_steps=500, eval_steps=500, save_steps=500, train_batch_si..., 'should_epoch_stop': False, 'should_save': False, 'should_evaluate': False, 'should_log': False}, 'attributes': {}}})
control = TrainerControl(should_training_stop=False, should_epoch_stop=False, should_save=False, should_evaluate=False, should_log=False)
kwargs = {}
callback = <transformers.trainer_callback.DefaultFlowCallback object at 0x7fdd04e541c0>

    def overwritten_call_event(self, event, args, state, control, **kwargs):
        for callback in self.callbacks:
            result = getattr(callback, event)(
                self.setfit_args,
                state,
                control,
                model=self.setfit_model,
                st_model=self.model,
                st_args=args,
>               tokenizer=self.tokenizer,
                optimizer=self.optimizer,
                lr_scheduler=self.lr_scheduler,
                train_dataloader=self.train_dataloader,
                eval_dataloader=self.eval_dataloader,
                **kwargs,
            )
E           AttributeError: 'CallbackHandler' object has no attribute 'tokenizer'

[harupy]

@WeichenXu123 full traceback?

[WeichenXu123]

added full stack trace.