wehub-resource-sync
346 lines
11 KiB
Python
346 lines
11 KiB
Python
import paddle.nn as nn
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import paddle
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from ..model_outputs import ModelOutput
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from .configuration import BertConfig
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from _typeshed import Incomplete
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from paddle import Tensor
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from paddle.nn import Layer, Embedding, Linear
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from paddlenlp.transformers.model_utils import PretrainedModel
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from typing import Dict, Optional, Tuple, Union, overload
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class BertEmbeddings(Layer):
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word_embeddings: Embedding
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position_embeddings: Embedding
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token_type_embeddings: Embedding
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layer_norm: Layer
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dropout: float
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def __init__(self, config: BertConfig) -> None: ...
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def forward(
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self,
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input_ids: Tensor,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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past_key_values_length: int = 0,
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): ...
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class BertPooler(Layer):
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dense: Linear
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activation: Layer
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pool_act: Layer
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def __init__(self, config: BertConfig) -> None: ...
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def forward(self, hidden_states): ...
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class BertPretrainedModel(PretrainedModel):
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model_config_file: str
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config_class: Incomplete
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resource_files_names: Dict[str, str]
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base_model_prefix: str
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pretrained_init_configuration: Dict[str, dict]
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pretrained_resource_files_map: Dict[str, str]
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def init_weights(self, layer) -> None: ...
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class BertModel(BertPretrainedModel):
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pad_token_id: int
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initializer_range: float
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embeddings: Embedding
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fuse: bool
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encoder: nn.TransformerDecoder
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pooler: BertPooler
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def __init__(self, config: BertConfig) -> None: ...
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def get_input_embeddings(self): ...
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def set_input_embeddings(self, value) -> None: ...
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def forward(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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past_key_values: Tensor | None = ...,
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use_cache: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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@staticmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: str,
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cache_dir: str | None = None,
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config: Optional[BertConfig] = None,
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*args,
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**kwargs
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) -> BertModel: ...
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class BertForQuestionAnswering(BertPretrainedModel):
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bert: BertModel
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dropout: nn.Dropout
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classifier: Linear
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def __init__(self, config: BertConfig): ...
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def forward(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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start_positions: Tensor | None = ...,
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end_positions: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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def __call__(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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start_positions: Tensor | None = ...,
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end_positions: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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@staticmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: str,
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cache_dir: str | None = None,
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classifier_dropout: float | None = None,
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config: Optional[BertConfig] = None,
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*args,
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**kwargs
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) -> BertForQuestionAnswering: ...
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class BertForSequenceClassification(BertPretrainedModel):
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bert: BertModel
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num_labels: int
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dropout: nn.Dropout
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classifier: Linear
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def __init__(self, config: BertConfig): ...
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def forward(
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self,
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input_ids: Tensor,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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labels: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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def __call__(
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self,
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input_ids: Tensor,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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labels: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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@staticmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: str,
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cache_dir: str | None = None,
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num_labels: int | None = 2,
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classifier_dropout: float | None = None,
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config: Optional[BertConfig] = None,
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*args,
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**kwargs
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) -> BertForSequenceClassification: ...
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class BertForTokenClassification(BertPretrainedModel):
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bert: BertModel
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num_labels: int
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dropout: nn.Dropout
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classifier: Linear
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def __init__(self, config: BertConfig): ...
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def forward(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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labels: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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def __call__(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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labels: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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@staticmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: str,
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cache_dir: str | None = None,
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num_labels: int | None = 2,
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classifier_dropout: float | None = None,
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config: Optional[BertConfig] = None,
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*args,
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**kwargs
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) -> BertForTokenClassification: ...
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class BertLMPredictionHead(Layer):
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transform: Incomplete
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activation: Incomplete
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layer_norm: nn.LayerNorm
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decoder_weight: paddle.ParamAttr
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decoder_bias: paddle.ParamAttr
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def __init__(self, config: BertConfig, embedding_weights: Tensor | None = ...) -> None: ...
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def forward(self, hidden_states, masked_positions: Tensor | None = ...): ...
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class BertPretrainingHeads(Layer):
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predictions: Incomplete
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seq_relationship: Incomplete
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def __init__(self, config: BertConfig, embedding_weights: Tensor | None = ...) -> None: ...
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def forward(self, sequence_output, pooled_output, masked_positions: Tensor | None = ...): ...
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class BertForPreTrainingOutput(ModelOutput):
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loss: Optional[paddle.Tensor]
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prediction_logits: paddle.Tensor
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seq_relationship_logits: paddle.Tensor
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hidden_states: Optional[Tuple[paddle.Tensor]]
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attentions: Optional[Tuple[paddle.Tensor]]
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def __init__(
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self,
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loss: Tensor | None,
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prediction_logits: Tensor | None,
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seq_relationship_logits: Tensor | None,
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hidden_states: Tensor | None,
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attentions: Tensor | None,
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) -> None: ...
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class BertForPretraining(BertPretrainedModel):
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bert: BertModel
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cls: Incomplete
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def __init__(self, config: BertConfig) -> None: ...
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def forward(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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masked_positions: Tensor | None = ...,
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labels: Tensor | None = ...,
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next_sentence_label: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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def __call__(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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masked_positions: Tensor | None = ...,
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labels: Tensor | None = ...,
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next_sentence_label: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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@staticmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: str,
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cache_dir: str | None = None,
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config: Optional[BertConfig] = None,
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*args,
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**kwargs
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) -> BertForQuestionAnswering: ...
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class BertPretrainingCriterion(paddle.nn.Layer):
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loss_fn: nn.Layer
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vocab_size: int
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def __init__(self, vocab_size) -> None: ...
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def forward(
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self, prediction_scores, seq_relationship_score, masked_lm_labels, next_sentence_labels, masked_lm_scale
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): ...
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def __call__(
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self, prediction_scores, seq_relationship_score, masked_lm_labels, next_sentence_labels, masked_lm_scale
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): ...
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class BertForMultipleChoice(BertPretrainedModel):
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bert: BertModel
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num_choices: int
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dropout: nn.Dropout
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classifier: Linear
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@overload
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def __init__(self, config: BertConfig) -> None: ...
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def forward(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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labels: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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@staticmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: str,
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cache_dir: str | None = None,
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num_choices: int | None = 2,
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classifier_dropout: float | None = None,
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config: Optional[BertConfig] = None,
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*args,
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**kwargs
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) -> BertForMultipleChoice: ...
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class BertOnlyMLMHead(nn.Layer):
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predictions: BertLMPredictionHead
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def __init__(self, config: BertConfig, embedding_weights: Tensor | None = ...) -> None: ...
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def forward(self, sequence_output, masked_positions: Tensor | None = ...): ...
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class BertForMaskedLM(BertPretrainedModel):
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bert: BertModel
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cls: BertOnlyMLMHead
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def __init__(self, config: BertConfig) -> None: ...
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def forward(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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labels: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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def __call__(
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self,
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input_ids,
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token_type_ids: Tensor | None = ...,
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position_ids: Tensor | None = ...,
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attention_mask: Tensor | None = ...,
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labels: Tensor | None = ...,
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output_hidden_states: bool = ...,
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output_attentions: bool = ...,
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return_dict: bool = ...,
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): ...
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@staticmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: str,
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cache_dir: str | None = None,
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config: Optional[BertConfig] = None,
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*args,
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**kwargs
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) -> BertForMaskedLM: ...
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