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RobertaTransformer

Model
DashAI.back.models.hugging_face.RobertaTransformer

Pretrained RoBERTa model for English text classification.

RoBERTa (Robustly Optimised BERT Pre-training Approach) improves upon BERT by training longer with larger mini-batches, removing the next sentence prediction objective, and using dynamic masking. It achieves consistently higher performance on NLP benchmarks than BERT.

References​

Parameters​

num_train_epochs : integer, default=1
Total number of training epochs to perform.
batch_size : integer, default=16
The batch size per GPU/TPU core/CPU for training
learning_rate : number, default=3e-05
The initial learning rate for AdamW optimizer
device : string, default=CPU
Hardware on which the training is run. If available, GPU is recommended for efficiency reasons. Otherwise, use CPU.
weight_decay : number, default=0.01
Weight decay is a regularization technique used in training neural networks to prevent overfitting. In the context of the AdamW optimizer, the 'weight_decay' parameter is the rate at which the weights of all layers are reduced during training, provided that this rate is not zero.
log_train_every_n_epochs, default=1
Log metrics for train split every n epochs during training. If None, it won't log per epoch.
log_train_every_n_steps, default=None
Log metrics for train split every n steps during training. If None, it won't log per step.
log_validation_every_n_epochs, default=1
Log metrics for validation split every n epochs during training. If None, it won't log per epoch.
log_validation_every_n_steps, default=None
Log metrics for validation split every n steps during training. If None, it won't log per step.

Methods​

calculate_metrics(self, split: DashAI.back.core.enums.metrics.SplitEnum = <SplitEnum.VALIDATION: 'validation'>, level: DashAI.back.core.enums.metrics.LevelEnum = <LevelEnum.LAST: 'last'>, log_index: int = None, x_data: 'DashAIDataset' = None, y_data: 'DashAIDataset' = None)

Defined on BaseModel

Calculate and save metrics for a given data split and level.

Parameters

split : SplitEnum
The data split to evaluate (TRAIN, VALIDATION, or TEST). Defaults to SplitEnum.VALIDATION.
level : LevelEnum
The metric granularity level (LAST, TRIAL, STEP, or BATCH). Defaults to LevelEnum.LAST.
log_index : int, optional
Explicit step index for the metric entry. If None, the next step index is computed automatically. Defaults to None.
x_data : DashAIDataset, optional
Input features. If None, the dataset stored in the model for the given split is used. Defaults to None.
y_data : DashAIDataset, optional
Target labels. If None, the labels stored in the model for the given split are used. Defaults to None.

get_metadata(cls) -> Dict[str, Any]

Defined on BaseModel

Get metadata values for the current model.

Returns

Dict[str, Any]
Dictionary containing UI metadata such as the model icon used in the DashAI frontend.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

load(cls, filename: Union[str, ForwardRef('Path')]) -> 'HuggingFaceTextClassificationTransformer'

Defined on HuggingFaceTextClassificationTransformer

Restore a HuggingFaceTextClassificationTransformer instance from disk.

Parameters

filename : str or Path
Directory path from which the model files will be read.

Returns

HuggingFaceTextClassificationTransformer
The restored model instance with fitted set to the persisted value.

predict(self, x_pred: 'DashAIDataset')

Defined on HuggingFaceTextClassificationTransformer

Predict with the fine-tuned model.

Parameters

x_pred : DashAIDataset
Dataset with text data.

Returns

List
List of predicted probabilities for each class.

prepare_dataset(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on HuggingFaceTextClassificationTransformer

Apply the model transformations to the dataset.

Parameters

dataset : DashAIDataset
The dataset to be transformed.
is_fit : bool
Whether this is for fitting (True) or prediction (False).

Returns

DashAIDataset
The prepared dataset ready to be converted to an accepted format in the model.

prepare_output(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on BaseModel

Hook for model-specific preprocessing of output targets.

Parameters

dataset : DashAIDataset
The output dataset (target labels) to preprocess.
is_fit : bool
Whether the call is part of a fitting phase. Defaults to False.

Returns

DashAIDataset
The preprocessed output dataset.

save(self, filename: Union[str, ForwardRef('Path')]) -> None

Defined on HuggingFaceTextClassificationTransformer

Store the fine-tuned model and its configuration to disk.

Parameters

filename : str or Path
Directory path where the model files will be written.

tokenize_data(self, dataset: 'DashAIDataset') -> 'DashAIDataset'

Defined on HuggingFaceTextClassificationTransformer

Tokenize the input data.

Parameters

dataset : DashAIDataset
Dataset with the input data to preprocess.

Returns

DashAIDataset
Dataset with the tokenized input data.

train(self, x_train, y_train, x_validation=None, y_validation=None)

Defined on HuggingFaceTextClassificationTransformer

Fine-tune the model on the provided classification data.

Parameters

x_train : DashAIDataset
Input text features for training.
y_train : DashAIDataset
Target labels for training.
x_validation : DashAIDataset
Input text features for validation.
y_validation : DashAIDataset
Target labels for validation.

Returns

HuggingFaceTextClassificationTransformer
The fine-tuned model instance.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict
A dictionary with the data provided by the user to initialize the model.

Returns

dict
A validated dictionary with the necessary objects.

Compatible with​