langml.prompt.models.ptuning

Implementation P-Tuning

Paper: GPT Understands, Too URL: https://arxiv.org/pdf/2103.10385.pdf

Module Contents

Classes

PartialEmbedding
PTuniningPrompt

class langml.prompt.models.ptuning.PartialEmbedding(input_dim: int, output_dim: int, active_start: int, active_end: int, embeddings_initializer: Optional[langml.tensor_typing.Initializer] = 'uniform', embeddings_regularizer: Optional[langml.tensor_typing.Regularizer] = None, activity_regularizer: Optional[langml.tensor_typing.Regularizer] = None, embeddings_constraint: Optional[langml.tensor_typing.Constraint] = None, mask_zero: bool = False, input_length: Optional[int] = None, **kwargs)

Bases: langml.L.Embedding

static get_custom_objects() → dict

compute_mask(self, inputs: langml.tensor_typing.Tensors, mask: Optional[langml.tensor_typing.Tensors] = None) → List[Union[langml.tensor_typing.Tensors, None]]

call(self, inputs: langml.tensor_typing.Tensors) → List[langml.tensor_typing.Tensors]

compute_output_shape(self, input_shape: langml.tensor_typing.Tensors) → List[langml.tensor_typing.Tensors]

class langml.prompt.models.ptuning.PTuniningPrompt(plm_backbone: str, plm_config_path: str, plm_ckpt_path: str, template: langml.prompt.base.Template, learning_rate: float = 1e-05, freeze_plm: bool = True, encoder: str = 'mlp')

Bases: langml.prompt.base.BasePromptModel

build_model(self) → langml.tensor_typing.Models