Training#

Here we describe the available trainers in Argilla:

Base Trainer: Internal mechanism to handle Trainers
SetFit Trainer: Internal mechanism for handling training logic of SetFit models
OpenAI Trainer: Internal mechanism for handling training logic of OpenAI models
PEFT (LoRA) Trainer: Internal mechanism for handling training logic of PEFT (LoRA) models
spaCy Trainer: Internal mechanism for handling training logic of spaCy models
Transformers Trainer: Internal mechanism for handling training logic of Transformers models
SpanMarker Trainer: Internal mechanism for handling training logic of SpanMarker models

Base Trainer#

class argilla.training.base.ArgillaTrainerSkeleton(name, dataset, record_class, workspace=None, multi_label=False, settings=None, model=None, seed=None, *arg, **kwargs)#

Parameters:

name (str) –
record_class (Union[TokenClassificationRecord, Text2TextRecord, TextClassificationRecord]) –
workspace (Optional[str]) –
multi_label (bool) –
settings (Union[TextClassificationSettings, TokenClassificationSettings]) –
model (str) –
seed (int) –

abstract init_model()#: Initializes a model.

abstract init_training_args()#: Initializes the training arguments.

abstract predict(text, as_argilla_records=True, **kwargs)#

Predicts the label of the text.

Parameters:

text (Union[List[str], str]) –
as_argilla_records (bool) –

abstract save(output_dir)#

Saves the model to the specified path.

Parameters:: output_dir (str) –

abstract train(output_dir=None)#

Trains the model.

Parameters:: output_dir (Optional[str]) –

abstract update_config(*args, **kwargs)#: Updates the configuration of the trainer, but the parameters depend on the trainer.subclass.

SetFit Trainer#

class argilla.training.setfit.ArgillaSetFitTrainer(*args, **kwargs)#

init_model()#: Initializes a model.

init_training_args()#

Initializes the training arguments.

Return type:: None

predict(text, as_argilla_records=True, **kwargs)#

The function takes in a list of strings and returns a list of predictions

Parameters:

text (Union[List[str], str]) – The text to be classified.
as_argilla_records (bool) – If True, the prediction will be returned as an Argilla record. If

False, the prediction will be returned as a string. Defaults to True

Returns:

A list of predictions

Parameters:

text (Union[List[str], str]) –
as_argilla_records (bool) –

save(output_dir)#

The function saves the model to the path specified, and also saves the label2id and id2label dictionaries to the same path

Parameters:

path (str) – the path to save the model to
output_dir (str) –

train(output_dir=None)#

We create a SetFitModel object from a pretrained model, then create a SetFitTrainer object with the model, and then train the model

Parameters:: output_dir (Optional[str]) –

update_config(**kwargs)#

Updates the setfit_model_kwargs and setfit_trainer_kwargs dictionaries with the keyword arguments passed to the update_config function.

Return type:: None

OpenAI Trainer#

class argilla.training.openai.ArgillaOpenAITrainer(*args, **kwargs)#

init_model()#: Initializes a model.

init_training_args(training_file=None, validation_file=None, model='curie', n_epochs=None, batch_size=None, learning_rate_multiplier=0.1, prompt_loss_weight=0.1, compute_classification_metrics=False, classification_n_classes=None, classification_positive_class=None, classification_betas=None, suffix=None)#

Initializes the training arguments.

Parameters:

training_file (Optional[str]) –
validation_file (Optional[str]) –
model (str) –
n_epochs (Optional[int]) –
batch_size (Optional[int]) –
learning_rate_multiplier (float) –
prompt_loss_weight (float) –
compute_classification_metrics (bool) –
classification_n_classes (Optional[int]) –
classification_positive_class (Optional[str]) –
classification_betas (Optional[list]) –
suffix (Optional[str]) –

predict(text, as_argilla_records=True, **kwargs)#

The function takes in a list of strings and returns a list of predictions

Parameters:

text (Union[List[str], str]) – The text to be classified.
as_argilla_records (bool) – If True, the prediction will be returned as an Argilla record. If

False, the prediction will be returned as a string. Defaults to True

Returns:

A list of predictions

Parameters:

text (Union[List[str], str]) –
as_argilla_records (bool) –

save(*arg, **kwargs)#

The function saves the model to the path specified, and also saves the label2id and id2label dictionaries to the same path

Parameters:: output_dir (str) – the path to save the model to

train(output_dir=None)#

We create a openai.FineTune object from a pretrained model, and send data to finetune it.

Parameters:: output_dir (Optional[str]) –

update_config(**kwargs)#: Updates the model_kwargs dictionaries with the keyword arguments passed to the update_config function.

PEFT (LoRA) Trainer#

class argilla.training.peft.ArgillaPeftTrainer(*args, **kwargs)#

init_model(new=False)#

Initializes a model.

Parameters:: new (bool) –

init_training_args()#: Initializes the training arguments.

predict(text, as_argilla_records=True, **kwargs)#

The function takes in a list of strings and returns a list of predictions

Parameters:

text (Union[List[str], str]) – The text to be classified.
as_argilla_records (bool) – If True, the prediction will be returned as an Argilla record. If

False, the prediction will be returned as a string. Defaults to True

Returns:

A list of predictions

Parameters:

text (Union[List[str], str]) –
as_argilla_records (bool) –

save(output_dir)#

The function saves the model to the path specified, and also saves the label2id and id2label dictionaries to the same path

Parameters:: output_dir (str) – the path to save the model to

sys = <module 'sys' (built-in)>#

update_config(**kwargs)#: Updates the setfit_model_kwargs and setfit_trainer_kwargs dictionaries with the keyword arguments passed to the update_config function.

spaCy Trainer#

class argilla.training.spacy.ArgillaSpaCyTrainer(freeze_tok2vec=False, **kwargs)#

Parameters:: freeze_tok2vec (bool) –

init_training_args()#

Initializes the training arguments.

Return type:: None

class argilla.training.spacy.ArgillaSpaCyTransformersTrainer(update_transformer=True, **kwargs)#

Parameters:: update_transformer (bool) –

init_training_args()#

Initializes the training arguments.

Return type:: None

Transformers Trainer#

class argilla.training.transformers.ArgillaTransformersTrainer(*args, **kwargs)#

init_model(new=False)#

Initializes a model.

Parameters:: new (bool) –

init_training_args()#: Initializes the training arguments.

predict(text, as_argilla_records=True, **kwargs)#

The function takes in a list of strings and returns a list of predictions

Parameters:

text (Union[List[str], str]) – The text to be classified.
as_argilla_records (bool) – If True, the prediction will be returned as an Argilla record. If

False, the prediction will be returned as a string. Defaults to True

Returns:

A list of predictions

Parameters:

text (Union[List[str], str]) –
as_argilla_records (bool) –

save(output_dir)#

The function saves the model to the path specified, and also saves the label2id and id2label dictionaries to the same path

Parameters:: output_dir (str) – the path to save the model to

train(output_dir)#

We create a SetFitModel object from a pretrained model, then create a SetFitTrainer object with the model, and then train the model

Parameters:: output_dir (str) –

update_config(**kwargs)#: Updates the setfit_model_kwargs and setfit_trainer_kwargs dictionaries with the keyword arguments passed to the update_config function.

SpanMarker Trainer#

class argilla.training.span_marker.ArgillaSpanMarkerTrainer(*args, **kwargs)#

init_model()#

Initializes a model.

Return type:: None

init_training_args()#

Initializes the training arguments.

Return type:: None

predict(text, as_argilla_records=True, **kwargs)#

The function takes in a list of strings and returns a list of predictions

Parameters:

text (Union[List[str], str]) – The text to be classified.
as_argilla_records (bool) – If True, the prediction will be returned as an Argilla record. If

False, the prediction will be returned as a string. Defaults to True

Returns:

A list of predictions

Parameters:

text (Union[List[str], str]) –
as_argilla_records (bool) –

save(output_dir)#

The function saves the model to the path specified, and also saves the label2id and id2label dictionaries to the same path

Parameters:: output_dir (str) – the path to save the model to

train(output_dir)#

We create a SetFitModel object from a pretrained model, then create a SetFitTrainer object with the model, and then train the model

Parameters:: output_dir (str) –

update_config(**kwargs)#

Updates the model_kwargs and trainer_kwargs dictionaries with the keyword arguments passed to the update_config function.

Return type:: None