API reference: 🤗 Transformers integration

You can log model training metadata by passing a Neptune callback and to the Trainer callbacks.

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NeptuneCallback()

Creates a Neptune callback that you pass to the callbacks argument of the Trainer constructor.

Parameters

Name	Type	Default	Description
api_token	str, optional	None	Neptune API token obtained upon registration. You can leave this argument out if you have saved your token to the NEPTUNE_API_TOKEN environment variable (strongly recommended).
project	str, optional	None	Name of an existing Neptune project, in the form: "workspace-name/project-name". In Neptune, you can copy the name from the project settings () → Edit project details. If None, the value of the NEPTUNE_PROJECT environment variable will be used. If you just want to try logging anonymously, you can use the public project "common/huggingface-integration".
name	str, optional	None	Custom name for the run.
base_namespace	str, optional	"finetuning"	In the Neptune run, the root namespace (folder) that will contain all of the logged metadata.
run	Run, optional	None	Pass a Neptune run object if you want to continue logging to an existing run. See Resume a run and Pass object between files.
log_parameters	bool, optional	True	Log all Trainer arguments and model parameters provided by the Trainer.
log_checkpoints	str, optional	None	If "same", uploads checkpoints whenever they are saved by the Trainer. If "last", uploads only the most recently saved checkpoint. If "best", uploads the best checkpoint (among the ones saved by the Trainer). If None, does not upload checkpoints.
**neptune_run_kwargs	(optional)	-	Additional keyword arguments to be passed directly to the init_run() function when a new run is created.

Example

from transformers.integrations import NeptuneCallback

# Create Neptune callback
neptune_callback = NeptuneCallback(
    name="DistilBERT",
    description="DistilBERT fine-tuned on GLUE/MRPC",
    tags=["args-callback", "fine-tune", "MRPC"],  # tags help you manage runs
    base_namespace="custom_name",  # the default is "finetuning"
    log_checkpoints="best",  # other options are "last", "same", and None
    capture_hardware_metrics=False,  # additional kwargs for a Neptune run
)

# Create training arguments
training_args = TrainingArguments(
    "quick-training-distilbert-mrpc",
    evaluation_strategy="steps",
    eval_steps=20,
    report_to="none", # (1)!
)

# Pass Neptune callback to Trainer
trainer = Trainer(
    model,
    training_args,
    callbacks=[neptune_callback],
)

trainer.train()

1. To avoid creating several callbacks, set the report_to argument to "none". This will be the default behavior in version 5 of 🤗 Transformers.

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See also

NeptuneCallback in the 🤗 Transformers API reference