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T5SmallTransformer

Model
DashAI.back.models.hugging_face.T5SmallTransformer

T5-small seq2seq model for English-to-{German, French, Romanian} translation.

Fine-tunes the t5-small checkpoint from Google. Translation direction is controlled by a task prefix prepended to each source sentence, e.g. "translate English to German: <text>".

Supported target languages: German, French, Romanian (T5 pre-training scope). The source language is always English.

References​

  • [1] https://huggingface.co/t5-small
  • [2] Raffel et al. (2020). "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer." JMLR 2020.

Parameters​

num_train_epochs : integer, default=1
Total number of training epochs to perform.
batch_size : integer, default=4
The batch size per GPU/TPU core/CPU for training.
learning_rate : number, default=2e-05
The initial learning rate for AdamW optimizer.
device : string, default=CPU
Hardware on which training is run. GPU is recommended when available. If GPU is selected, all available GPUs are used.
weight_decay : number, default=0.01
L2 regularization coefficient applied via the AdamW optimizer to prevent overfitting.
log_train_every_n_epochs, default=1
Log train metrics every N epochs. None disables per-epoch logging.
log_train_every_n_steps, default=None
Log train metrics every N steps. None disables per-step logging.
log_validation_every_n_epochs, default=1
Log validation metrics every N epochs. None disables per-epoch logging.
log_validation_every_n_steps, default=None
Log validation metrics every N steps. None disables per-step logging.
target_language : string, default=German
Target language for translation. Supported: 'German', 'French', 'Romanian'. T5-small translates from English only.

Methods​

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

Defined on T5SmallTransformer

Restore a T5SmallTransformer instance from disk.

predict(self, x_pred: 'DashAIDataset') -> List

Defined on T5SmallTransformer

Translate from English to the configured target language.

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

Defined on T5SmallTransformer

Return the dataset unchanged.

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

Defined on T5SmallTransformer

Persist model weights and hyperparameters to disk.

tokenize_data(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> 'DashAIDataset'

Defined on T5SmallTransformer

Prepend the T5 task prefix and tokenize source/target texts.

train(self, x_train: 'DashAIDataset', y_train: 'DashAIDataset', x_validation: 'DashAIDataset' = None, y_validation: 'DashAIDataset' = None)

Defined on T5SmallTransformer

Fine-tune T5-small on the configured translation direction.

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.

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.

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​