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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

• TRL Trainer: Internal mechanism for handling training logic of TRL models

• SentenceTransformer Trainer: Internal mechanism for handling training logic of SentenceTransformer 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) –

get_model()

Returns the model.

get_model_card_data(card_data_kwargs)

Generates a FrameworkCardData instance to generate a model card from.

Parameters:

card_data_kwargs (Dict[str, Any]) –

Return type:

FrameworkCardData

get_model_kwargs()

Returns the model kwargs.

get_tokenizer()

Returns the tokenizer.

get_trainer()

Returns the trainer.

get_trainer_kwargs()

Returns the training kwargs.

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) –

push_to_huggingface(repo_id, **kwargs)

Uploads the model to [Huggingface Hub](https://huggingface.co/docs/hub/models-the-hub).

Parameters:

repo_id (str) –

Return type:

Optional[str]

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.

class argilla.client.feedback.integrations.huggingface.model_card.FrameworkCardData(*args, **kwargs)

Parent class to generate the variables to add to the ModelCard.

Each framework will inherit from here and update accordingly.

Parameters:

• language (Optional[Union[str, List[str]]]) –

• license (Optional[str]) –

• model_name (Optional[str]) –

• model_id (Optional[str]) –

• dataset_name (Optional[str]) –

• dataset_id (Optional[str]) –

• tags (Optional[List[str]]) –

• model_summary (Optional[str]) –

• model_description (Optional[str]) –

• developers (Optional[str]) –

• shared_by (Optional[str]) –

• model_type (Optional[str]) –

• finetuned_from (Optional[str]) –

• repo (Optional[str]) –

• _is_on_huggingface (bool) –

• framework (Optional[Framework]) –

• train_size (Optional[float]) –

• seed (Optional[int]) –

• framework_kwargs (Dict[str, Any]) –

• task (Optional[Union[TrainingTaskForTextClassification, TrainingTaskForSFT, TrainingTaskForRM, TrainingTaskForPPO, TrainingTaskForDPO, TrainingTaskForChatCompletion, TrainingTaskForSentenceSimilarity]]) –

• output_dir (Optional[str]) –

• library_name (Optional[str]) –

• update_config_kwargs (Dict[str, Any]) –

to_dict()

Main method to generate the variables that will be written in the model card.

Return type:

Dict[str, Any]

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

Return type:

None

class argilla.client.feedback.integrations.huggingface.model_card.SetFitModelCardData(language: Union[str, List[str], NoneType] = None, license: Optional[str] = None, model_name: Optional[str] = None, model_id: Optional[str] = None, dataset_name: Optional[str] = None, dataset_id: Optional[str] = None, tags: Optional[List[str]] = <factory>, model_summary: Optional[str] = None, model_description: Optional[str] = None, developers: Optional[str] = None, shared_by: Optional[str] = None, model_type: Optional[str] = None, finetuned_from: Optional[str] = None, repo: Optional[str] = None, _is_on_huggingface: bool = False, framework: argilla.client.models.Framework = <Framework.SETFIT: 'setfit'>, train_size: Optional[float] = None, seed: Optional[int] = None, framework_kwargs: Dict[str, Any] = <factory>, task: Union[argilla.client.feedback.training.schemas.base.TrainingTaskForTextClassification, argilla.client.feedback.training.schemas.base.TrainingTaskForSFT, argilla.client.feedback.training.schemas.base.TrainingTaskForRM, argilla.client.feedback.training.schemas.base.TrainingTaskForPPO, argilla.client.feedback.training.schemas.base.TrainingTaskForDPO, argilla.client.feedback.training.schemas.base.TrainingTaskForChatCompletion, argilla.client.feedback.training.schemas.base.TrainingTaskForSentenceSimilarity, NoneType] = None, output_dir: Optional[str] = None, library_name: Optional[str] = None, update_config_kwargs: Dict[str, Any] = <factory>, tokenizer: 'PreTrainedTokenizer' = '')

Parameters:

• language (Optional[Union[str, List[str]]]) –

• license (Optional[str]) –

• model_name (Optional[str]) –

• model_id (Optional[str]) –

• dataset_name (Optional[str]) –

• dataset_id (Optional[str]) –

• tags (Optional[List[str]]) –

• model_summary (Optional[str]) –

• model_description (Optional[str]) –

• developers (Optional[str]) –

• shared_by (Optional[str]) –

• model_type (Optional[str]) –

• finetuned_from (Optional[str]) –

• repo (Optional[str]) –

• _is_on_huggingface (bool) –

• framework (Framework) –

• train_size (Optional[float]) –

• seed (Optional[int]) –

• framework_kwargs (Dict[str, Any]) –

• task (Optional[Union[TrainingTaskForTextClassification, TrainingTaskForSFT, TrainingTaskForRM, TrainingTaskForPPO, TrainingTaskForDPO, TrainingTaskForChatCompletion, TrainingTaskForSentenceSimilarity]]) –

• output_dir (Optional[str]) –

• library_name (Optional[str]) –

• update_config_kwargs (Dict[str, Any]) –

• tokenizer (PreTrainedTokenizer) –

OpenAI Trainer

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

init_model()

Initializes a model.

Return type:

None

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, hyperparameters=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]) –

• hyperparameters (Optional[dict]) –

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

Return type:

Union[List, str]

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) –

Return type:

Union[List, str]

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

Return type:

None

train(output_dir=None)

We create an openai.FineTune object from a pre-trained model, and send data to finetune it.

Parameters:

output_dir (Optional[str]) –

Return type:

None

update_config(**kwargs)

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

class argilla.client.feedback.integrations.huggingface.model_card.OpenAIModelCardData(language: Union[str, List[str], NoneType] = None, license: Optional[str] = None, model_name: Optional[str] = None, model_id: Optional[str] = None, dataset_name: Optional[str] = None, dataset_id: Optional[str] = None, tags: Optional[List[str]] = <factory>, model_summary: Optional[str] = None, model_description: Optional[str] = None, developers: Optional[str] = None, shared_by: Optional[str] = None, model_type: Optional[str] = None, finetuned_from: Optional[str] = None, repo: Optional[str] = None, _is_on_huggingface: bool = False, framework: argilla.client.models.Framework = <Framework.OPENAI: 'openai'>, train_size: Optional[float] = None, seed: Optional[int] = None, framework_kwargs: Dict[str, Any] = <factory>, task: Union[argilla.client.feedback.training.schemas.base.TrainingTaskForTextClassification, argilla.client.feedback.training.schemas.base.TrainingTaskForSFT, argilla.client.feedback.training.schemas.base.TrainingTaskForRM, argilla.client.feedback.training.schemas.base.TrainingTaskForPPO, argilla.client.feedback.training.schemas.base.TrainingTaskForDPO, argilla.client.feedback.training.schemas.base.TrainingTaskForChatCompletion, argilla.client.feedback.training.schemas.base.TrainingTaskForSentenceSimilarity, NoneType] = None, output_dir: Optional[str] = None, library_name: Optional[str] = None, update_config_kwargs: Dict[str, Any] = <factory>)

Parameters:

• language (Optional[Union[str, List[str]]]) –

• license (Optional[str]) –

• model_name (Optional[str]) –

• model_id (Optional[str]) –

• dataset_name (Optional[str]) –

• dataset_id (Optional[str]) –

• tags (Optional[List[str]]) –

• model_summary (Optional[str]) –

• model_description (Optional[str]) –

• developers (Optional[str]) –

• shared_by (Optional[str]) –

• model_type (Optional[str]) –

• finetuned_from (Optional[str]) –

• repo (Optional[str]) –

• _is_on_huggingface (bool) –

• framework (Framework) –

• train_size (Optional[float]) –

• seed (Optional[int]) –

• framework_kwargs (Dict[str, Any]) –

• task (Optional[Union[TrainingTaskForTextClassification, TrainingTaskForSFT, TrainingTaskForRM, TrainingTaskForPPO, TrainingTaskForDPO, TrainingTaskForChatCompletion, TrainingTaskForSentenceSimilarity]]) –

• output_dir (Optional[str]) –

• library_name (Optional[str]) –

• update_config_kwargs (Dict[str, Any]) –

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

class argilla.client.feedback.integrations.huggingface.model_card.PeftModelCardData(language: Union[str, List[str], NoneType] = None, license: Optional[str] = None, model_name: Optional[str] = None, model_id: Optional[str] = None, dataset_name: Optional[str] = None, dataset_id: Optional[str] = None, tags: Optional[List[str]] = <factory>, model_summary: Optional[str] = None, model_description: Optional[str] = None, developers: Optional[str] = None, shared_by: Optional[str] = None, model_type: Optional[str] = None, finetuned_from: Optional[str] = None, repo: Optional[str] = None, _is_on_huggingface: bool = False, framework: argilla.client.models.Framework = <Framework.PEFT: 'peft'>, train_size: Optional[float] = None, seed: Optional[int] = None, framework_kwargs: Dict[str, Any] = <factory>, task: Union[argilla.client.feedback.training.schemas.base.TrainingTaskForTextClassification, argilla.client.feedback.training.schemas.base.TrainingTaskForSFT, argilla.client.feedback.training.schemas.base.TrainingTaskForRM, argilla.client.feedback.training.schemas.base.TrainingTaskForPPO, argilla.client.feedback.training.schemas.base.TrainingTaskForDPO, argilla.client.feedback.training.schemas.base.TrainingTaskForChatCompletion, argilla.client.feedback.training.schemas.base.TrainingTaskForSentenceSimilarity, NoneType] = None, output_dir: Optional[str] = None, library_name: Optional[str] = None, update_config_kwargs: Dict[str, Any] = <factory>, tokenizer: 'PreTrainedTokenizer' = '')

Parameters:

• language (Optional[Union[str, List[str]]]) –

• license (Optional[str]) –

• model_name (Optional[str]) –

• model_id (Optional[str]) –

• dataset_name (Optional[str]) –

• dataset_id (Optional[str]) –

• tags (Optional[List[str]]) –

• model_summary (Optional[str]) –

• model_description (Optional[str]) –

• developers (Optional[str]) –

• shared_by (Optional[str]) –

• model_type (Optional[str]) –

• finetuned_from (Optional[str]) –

• repo (Optional[str]) –

• _is_on_huggingface (bool) –

• framework (Framework) –

• train_size (Optional[float]) –

• seed (Optional[int]) –

• framework_kwargs (Dict[str, Any]) –

• task (Optional[Union[TrainingTaskForTextClassification, TrainingTaskForSFT, TrainingTaskForRM, TrainingTaskForPPO, TrainingTaskForDPO, TrainingTaskForChatCompletion, TrainingTaskForSentenceSimilarity]]) –

• output_dir (Optional[str]) –

• library_name (Optional[str]) –

• update_config_kwargs (Dict[str, Any]) –

• tokenizer (PreTrainedTokenizer) –

spaCy Trainer

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

Parameters:

freeze_tok2vec (bool) –

init_training_args()

This method is used to generate the spacy configuration file, which is used to train

Return type:

None

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

Parameters:

update_transformer (bool) –

init_training_args()

This method is used to generate the spacy configuration file, which is used to train

Return type:

None

class argilla.training.spacy._ArgillaSpaCyTrainerBase(language=None, gpu_id=-1, model=None, optimize='efficiency', *args, **kwargs)

Parameters:

• language (Optional[str]) –

• gpu_id (Optional[int]) –

• model (Optional[str]) –

• optimize (Literal['efficiency', 'accuracy']) –

init_model()

Initializes a model.

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

Predict the labels for the given text using the trained pipeline.

Parameters:

• text (Union[List[str], str]) – A str or a List[str] with the text to predict the labels for.

• as_argilla_records (bool) – A bool indicating whether to return the predictions as argilla records or as dicts. Defaults to True.

Returns:

Either a dict, BaseModel (if as_argilla_records is True) or a List[dict], List[BaseModel] (if as_argilla_records is True) with the predictions.

Return type:

Union[Dict[str, Any], List[Dict[str, Any]], BaseModel, List[BaseModel]]

save(output_dir)

Save the trained pipeline to disk.

Parameters:

output_dir (str) – A str with the path to save the pipeline.

Return type:

None

train(output_dir=None)

Train the pipeline using spaCy.

Parameters:

output_dir (Optional[str]) – A str with the path to save the trained pipeline. Defaults to None.

Return type:

None

update_config(**spacy_training_config)

Update the spaCy training config.

Disclaimer: currently just the training config is supported, but in the future we will support all the spaCy config values for more precise control over the training process. Also, note that the arguments may differ between the CPU and GPU training.

Parameters:

**spacy_training_config – The spaCy training config.

Return type:

None

class argilla.client.feedback.integrations.huggingface.model_card.SpacyTransformersModelCardData(language: Union[str, List[str], NoneType] = None, license: Optional[str] = None, model_name: Optional[str] = None, model_id: Optional[str] = None, dataset_name: Optional[str] = None, dataset_id: Optional[str] = None, tags: Optional[List[str]] = <factory>, model_summary: Optional[str] = None, model_description: Optional[str] = None, developers: Optional[str] = None, shared_by: Optional[str] = None, model_type: Optional[str] = None, finetuned_from: Optional[str] = None, repo: Optional[str] = None, _is_on_huggingface: bool = False, framework: argilla.client.models.Framework = <Framework.SPACY_TRANSFORMERS: 'spacy-transformers'>, train_size: Optional[float] = None, seed: Optional[int] = None, framework_kwargs: Dict[str, Any] = <factory>, task: Union[argilla.client.feedback.training.schemas.base.TrainingTaskForTextClassification, argilla.client.feedback.training.schemas.base.TrainingTaskForSFT, argilla.client.feedback.training.schemas.base.TrainingTaskForRM, argilla.client.feedback.training.schemas.base.TrainingTaskForPPO, argilla.client.feedback.training.schemas.base.TrainingTaskForDPO, argilla.client.feedback.training.schemas.base.TrainingTaskForChatCompletion, argilla.client.feedback.training.schemas.base.TrainingTaskForSentenceSimilarity, NoneType] = None, output_dir: Optional[str] = None, library_name: Optional[str] = None, update_config_kwargs: Dict[str, Any] = <factory>, lang: Optional[ForwardRef('spacy.Language')] = None, gpu_id: Optional[int] = -1, optimize: Literal['efficiency', 'accuracy'] = 'efficiency', pipeline: List[str] = <factory>, update_transformer: bool = True)

Parameters:

• language (Optional[Union[str, List[str]]]) –

• license (Optional[str]) –

• model_name (Optional[str]) –

• model_id (Optional[str]) –

• dataset_name (Optional[str]) –

• dataset_id (Optional[str]) –

• tags (Optional[List[str]]) –

• model_summary (Optional[str]) –

• model_description (Optional[str]) –

• developers (Optional[str]) –

• shared_by (Optional[str]) –

• model_type (Optional[str]) –

• finetuned_from (Optional[str]) –

• repo (Optional[str]) –

• _is_on_huggingface (bool) –

• framework (Framework) –

• train_size (Optional[float]) –

• seed (Optional[int]) –

• framework_kwargs (Dict[str, Any]) –

• task (Optional[Union[TrainingTaskForTextClassification, TrainingTaskForSFT, TrainingTaskForRM, TrainingTaskForPPO, TrainingTaskForDPO, TrainingTaskForChatCompletion, TrainingTaskForSentenceSimilarity]]) –

• output_dir (Optional[str]) –

• library_name (Optional[str]) –

• update_config_kwargs (Dict[str, Any]) –

• lang (Optional[spacy.Language]) –

• gpu_id (Optional[int]) –

• optimize (Literal['efficiency', 'accuracy']) –

• pipeline (List[str]) –

• update_transformer (bool) –

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)

Trains 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.

class argilla.client.feedback.integrations.huggingface.model_card.TransformersModelCardData(language: Union[str, List[str], NoneType] = None, license: Optional[str] = None, model_name: Optional[str] = None, model_id: Optional[str] = None, dataset_name: Optional[str] = None, dataset_id: Optional[str] = None, tags: Optional[List[str]] = <factory>, model_summary: Optional[str] = None, model_description: Optional[str] = None, developers: Optional[str] = None, shared_by: Optional[str] = None, model_type: Optional[str] = None, finetuned_from: Optional[str] = None, repo: Optional[str] = None, _is_on_huggingface: bool = False, framework: argilla.client.models.Framework = <Framework.TRANSFORMERS: 'transformers'>, train_size: Optional[float] = None, seed: Optional[int] = None, framework_kwargs: Dict[str, Any] = <factory>, task: Union[argilla.client.feedback.training.schemas.base.TrainingTaskForTextClassification, argilla.client.feedback.training.schemas.base.TrainingTaskForSFT, argilla.client.feedback.training.schemas.base.TrainingTaskForRM, argilla.client.feedback.training.schemas.base.TrainingTaskForPPO, argilla.client.feedback.training.schemas.base.TrainingTaskForDPO, argilla.client.feedback.training.schemas.base.TrainingTaskForChatCompletion, argilla.client.feedback.training.schemas.base.TrainingTaskForSentenceSimilarity, NoneType] = None, output_dir: Optional[str] = None, library_name: Optional[str] = None, update_config_kwargs: Dict[str, Any] = <factory>, tokenizer: 'PreTrainedTokenizer' = '')

Parameters:

• language (Optional[Union[str, List[str]]]) –

• license (Optional[str]) –

• model_name (Optional[str]) –

• model_id (Optional[str]) –

• dataset_name (Optional[str]) –

• dataset_id (Optional[str]) –

• tags (Optional[List[str]]) –

• model_summary (Optional[str]) –

• model_description (Optional[str]) –

• developers (Optional[str]) –

• shared_by (Optional[str]) –

• model_type (Optional[str]) –

• finetuned_from (Optional[str]) –

• repo (Optional[str]) –

• _is_on_huggingface (bool) –

• framework (Framework) –

• train_size (Optional[float]) –

• seed (Optional[int]) –

• framework_kwargs (Dict[str, Any]) –

• task (Optional[Union[TrainingTaskForTextClassification, TrainingTaskForSFT, TrainingTaskForRM, TrainingTaskForPPO, TrainingTaskForDPO, TrainingTaskForChatCompletion, TrainingTaskForSentenceSimilarity]]) –

• output_dir (Optional[str]) –

• library_name (Optional[str]) –

• update_config_kwargs (Dict[str, Any]) –

• tokenizer (PreTrainedTokenizer) –

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

TRL Trainer

class argilla.client.feedback.integrations.huggingface.model_card.TRLModelCardData(language: Union[str, List[str], NoneType] = None, license: Optional[str] = None, model_name: Optional[str] = None, model_id: Optional[str] = None, dataset_name: Optional[str] = None, dataset_id: Optional[str] = None, tags: Optional[List[str]] = <factory>, model_summary: Optional[str] = None, model_description: Optional[str] = None, developers: Optional[str] = None, shared_by: Optional[str] = None, model_type: Optional[str] = None, finetuned_from: Optional[str] = None, repo: Optional[str] = None, _is_on_huggingface: bool = False, framework: argilla.client.models.Framework = <Framework.TRL: 'trl'>, train_size: Optional[float] = None, seed: Optional[int] = None, framework_kwargs: Dict[str, Any] = <factory>, task: Union[argilla.client.feedback.training.schemas.base.TrainingTaskForTextClassification, argilla.client.feedback.training.schemas.base.TrainingTaskForSFT, argilla.client.feedback.training.schemas.base.TrainingTaskForRM, argilla.client.feedback.training.schemas.base.TrainingTaskForPPO, argilla.client.feedback.training.schemas.base.TrainingTaskForDPO, argilla.client.feedback.training.schemas.base.TrainingTaskForChatCompletion, argilla.client.feedback.training.schemas.base.TrainingTaskForSentenceSimilarity, NoneType] = None, output_dir: Optional[str] = None, library_name: Optional[str] = None, update_config_kwargs: Dict[str, Any] = <factory>)

Parameters:

• language (Optional[Union[str, List[str]]]) –

• license (Optional[str]) –

• model_name (Optional[str]) –

• model_id (Optional[str]) –

• dataset_name (Optional[str]) –

• dataset_id (Optional[str]) –

• tags (Optional[List[str]]) –

• model_summary (Optional[str]) –

• model_description (Optional[str]) –

• developers (Optional[str]) –

• shared_by (Optional[str]) –

• model_type (Optional[str]) –

• finetuned_from (Optional[str]) –

• repo (Optional[str]) –

• _is_on_huggingface (bool) –

• framework (Framework) –

• train_size (Optional[float]) –

• seed (Optional[int]) –

• framework_kwargs (Dict[str, Any]) –

• task (Optional[Union[TrainingTaskForTextClassification, TrainingTaskForSFT, TrainingTaskForRM, TrainingTaskForPPO, TrainingTaskForDPO, TrainingTaskForChatCompletion, TrainingTaskForSentenceSimilarity]]) –

• output_dir (Optional[str]) –

• library_name (Optional[str]) –

• update_config_kwargs (Dict[str, Any]) –

SentenceTransformer Trainer

class argilla.client.feedback.training.frameworks.sentence_transformers.ArgillaSentenceTransformersTrainer(dataset, task, prepared_data=None, model=None, seed=None, train_size=1, cross_encoder=False)

Parameters:

• dataset (FeedbackDataset) –

• task (TrainingTaskForSentenceSimilarity) –

• model (str) –

• seed (int) –

• train_size (Optional[float]) –

• cross_encoder (bool) –

get_model_card_data(**card_data_kwargs)

Generate the card data to be used for the ArgillaModelCard.

Parameters:

card_data_kwargs – Extra arguments provided by the user when creating the ArgillaTrainer.

Returns:

Container for the data to be written on the ArgillaModelCard.

Return type:

SentenceTransformerCardData

init_model()

Initializes a model.

Return type:

None

init_training_args()

Initializes the training arguments.

Return type:

None

predict(text, as_argilla_records=False, **kwargs)

Predicts the similarity of the sentences.

Parameters:

• text (Union[List[List[str]], Tuple[str, List[str]]]) – The sentences to obtain the similarity from. Allowed inputs are: - A list with a single sentence (as a string) and a list of sentences to compare against. - A list with pair of sentences.

• 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 predicted similarities.

Return type:

List[float]

push_to_huggingface(repo_id, **kwargs)

Uploads the model to [huggingface’s model hub](https://huggingface.co/models).

The full list of parameters can be seen at: [sentence-transformer api docs](https://www.sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.save_to_hub).

Parameters:

repo_id (str) – The name of the repository you want to push your model and tokenizer to. It should contain your organization name when pushing to a given organization.

Raises:

NotImplementedError – For CrossEncoder models, that currently aren’t implemented underneath.

Return type:

None

save(output_dir)

Saves the model to the specified path.

Parameters:

output_dir (str) –

Return type:

None

train(output_dir=None)

Trains the model.

Parameters:

output_dir (Optional[str]) –

Return type:

None

update_config(**kwargs)

Updates the configuration of the trainer, but the parameters depend on the trainer.subclass.

Return type:

None

class argilla.client.feedback.integrations.huggingface.model_card.SentenceTransformerCardData(language: Union[str, List[str], NoneType] = None, license: Optional[str] = None, model_name: Optional[str] = None, model_id: Optional[str] = None, dataset_name: Optional[str] = None, dataset_id: Optional[str] = None, tags: Optional[List[str]] = <factory>, model_summary: Optional[str] = None, model_description: Optional[str] = None, developers: Optional[str] = None, shared_by: Optional[str] = None, model_type: Optional[str] = None, finetuned_from: Optional[str] = None, repo: Optional[str] = None, _is_on_huggingface: bool = False, framework: argilla.client.models.Framework = <Framework.SENTENCE_TRANSFORMERS: 'sentence-transformers'>, train_size: Optional[float] = None, seed: Optional[int] = None, framework_kwargs: Dict[str, Any] = <factory>, task: Union[argilla.client.feedback.training.schemas.base.TrainingTaskForTextClassification, argilla.client.feedback.training.schemas.base.TrainingTaskForSFT, argilla.client.feedback.training.schemas.base.TrainingTaskForRM, argilla.client.feedback.training.schemas.base.TrainingTaskForPPO, argilla.client.feedback.training.schemas.base.TrainingTaskForDPO, argilla.client.feedback.training.schemas.base.TrainingTaskForChatCompletion, argilla.client.feedback.training.schemas.base.TrainingTaskForSentenceSimilarity, NoneType] = None, output_dir: Optional[str] = None, library_name: Optional[str] = None, update_config_kwargs: Dict[str, Any] = <factory>, cross_encoder: bool = False, trainer_cls: Optional[Callable] = None)

Parameters:

• language (Optional[Union[str, List[str]]]) –

• license (Optional[str]) –

• model_name (Optional[str]) –

• model_id (Optional[str]) –

• dataset_name (Optional[str]) –

• dataset_id (Optional[str]) –

• tags (Optional[List[str]]) –

• model_summary (Optional[str]) –

• model_description (Optional[str]) –

• developers (Optional[str]) –

• shared_by (Optional[str]) –

• model_type (Optional[str]) –

• finetuned_from (Optional[str]) –

• repo (Optional[str]) –

• _is_on_huggingface (bool) –

• framework (Framework) –

• train_size (Optional[float]) –

• seed (Optional[int]) –

• framework_kwargs (Dict[str, Any]) –

• task (Optional[Union[TrainingTaskForTextClassification, TrainingTaskForSFT, TrainingTaskForRM, TrainingTaskForPPO, TrainingTaskForDPO, TrainingTaskForChatCompletion, TrainingTaskForSentenceSimilarity]]) –

• output_dir (Optional[str]) –

• library_name (Optional[str]) –

• update_config_kwargs (Dict[str, Any]) –

• cross_encoder (bool) –

• trainer_cls (Optional[Callable]) –