modeling

Modeling classes for ALBERT model.

class AlbertPretrainedModel(*args, **kwargs)

基类：PretrainedModel

An abstract class for pretrained ALBERT models. It provides ALBERT related model_config_file, pretrained_init_configuration, resource_files_names, pretrained_resource_files_map, base_model_prefix for downloading and loading pretrained models. See PretrainedModel for more details.

config_class

AlbertConfig 的别名

base_model_class

AlbertModel 的别名

class AlbertModel(config: AlbertConfig)

基类：AlbertPretrainedModel

The bare Albert Model transformer outputting raw hidden-states.

This model inherits from PretrainedModel. Refer to the superclass documentation for the generic methods.

This model is also a Paddle paddle.nn.Layer subclass. Use it as a regular Paddle Layer and refer to the Paddle documentation for all matter related to general usage and behavior.

参数:

config (AlbertConfig) -- An instance of AlbertConfig used to construct AlbertModel.

get_input_embeddings()

get input embedding of model

返回:

embedding of model

返回类型:

nn.Embedding

set_input_embeddings(value)

set new input embedding for model

参数:

value (Embedding) -- the new embedding of model

抛出:

NotImplementedError -- Model has not implement set_input_embeddings method

forward(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_hidden_states=False, output_attentions=False, return_dict=False)

The AlbertModel forward method, overrides the __call__() special method.

参数:

• input_ids (Tensor) -- Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. Its data type should be int64 and it has a shape of [batch_size, sequence_length].

• attention_mask (Tensor, optional) -- Mask used in multi-head attention to avoid performing attention on to some unwanted positions, usually the paddings or the subsequent positions. Its data type can be int, float and bool. When the data type is bool, the masked tokens have False values and the others have True values. When the data type is int, the masked tokens have 0 values and the others have 1 values. When the data type is float, the masked tokens have -INF values and the others have 0 values. It is a tensor with shape broadcasted to [batch_size, num_attention_heads, sequence_length, sequence_length]. Defaults to None, which means nothing needed to be prevented attention to.

• token_type_ids (Tensor, optional) --

  Segment token indices to indicate different portions of the inputs. Selected in the range [0, type_vocab_size - 1]. If type_vocab_size is 2, which means the inputs have two portions. Indices can either be 0 or 1:

  • 0 corresponds to a sentence A token,

  • 1 corresponds to a sentence B token.

  Its data type should be int64 and it has a shape of [batch_size, sequence_length]. Defaults to None, which means we don't add segment embeddings.

• position_ids (Tensor, optional) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, max_position_embeddings - 1]. Shape as (batch_size, num_tokens) and dtype as int64. Defaults to None.

• head_mask (Tensor, optional) --

  Mask to nullify selected heads of the self-attention modules. Masks values can either be 0 or 1:

  • 1 indicates the head is not masked,

  • 0 indicated the head is masked.

• inputs_embeds -- If you want to control how to convert inputs_ids indices into associated vectors, you can pass an embedded representation directly instead of passing inputs_ids.

返回:

Returns tuple (sequence_output, pooled_output) or a dict with last_hidden_state, pooled_output, all_hidden_states, all_attentions fields.

With the fields:

• sequence_output (Tensor):

  Sequence of hidden-states at the last layer of the model. It's data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size].

• pooled_output (Tensor):

  The output of first token ([CLS]) in sequence. We "pool" the model by simply taking the hidden state corresponding to the first token. Its data type should be float32 and has a shape of [batch_size, hidden_size].

• last_hidden_state (Tensor):

  The output of the last encoder layer, it is also the sequence_output. It's data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size].

• all_hidden_states (Tensor):

  Hidden_states of all layers in the Transformer encoder. The length of all_hidden_states is num_hidden_layers + 1. For all element in the tuple, its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

• all_attentions (Tensor):

  Attentions of all layers of in the Transformer encoder. The length of all_attentions is num_hidden_layers. For all element in the tuple, its data type should be float32 and its shape is [batch_size, num_attention_heads, sequence_length, sequence_length].

返回类型:

tuple or Dict

示例

import paddle
from paddlenlp.transformers import AlbertModel, AlbertTokenizer

tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
model = AlbertModel.from_pretrained('albert-base-v1')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
output = model(**inputs)

class AlbertForPretraining(config: AlbertConfig)

基类：AlbertPretrainedModel

Albert Model with a masked language modeling head and a sentence order prediction head on top.

参数:

config (AlbertConfig) -- An instance of AlbertConfig used to construct AlbertModel.

get_output_embeddings()

To be overwrited for models with output embeddings

返回:

the otuput embedding of model

返回类型:

Optional[Embedding]

get_input_embeddings()

get input embedding of model

返回:

embedding of model

返回类型:

nn.Embedding

forward(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, sentence_order_label=None, labels=None, output_attentions=False, output_hidden_states=False, return_dict=False)

The AlbertForPretraining forward method, overrides the __call__() special method.

参数:

• input_ids (Tensor) -- See AlbertModel.

• attention_mask (list, optional) -- See AlbertModel.

• token_type_ids (Tensor, optional) -- See AlbertModel.

• position_ids (Tensor, optional) -- See AlbertModel.

• head_mask (Tensor, optional) -- See AlbertModel.

• inputs_embeds (Tensor, optional) -- See AlbertModel.

• sentence_order_label (Tensor, optional) -- Labels of the next sequence prediction. Input should be a sequence pair Indices should be 0 or 1. 0 indicates original order (sequence A, then sequence B), and 1 indicates switched order (sequence B, then sequence A). Defaults to None.

• output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to False.

• output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to False.

• return_dict (bool, optional) -- Whether to return a ModelOutput object. If False, the output will be a tuple of tensors. Defaults to False.

返回:

Returns tuple (prediction_scores, sop_scores) or a dict with prediction_logits, sop_logits, pooled_output, hidden_states, attentions fields.

With the fields:

• prediction_scores (Tensor):

  The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length, vocab_size].

• sop_scores (Tensor):

  The scores of sentence order prediction. Its data type should be float32 and its shape is [batch_size, 2].

• prediction_logits (Tensor):

  The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length, vocab_size].

• sop_logits (Tensor):

  The scores of sentence order prediction. Its data type should be float32 and its shape is [batch_size, 2].

• hidden_states (Tensor):

  Hidden_states of all layers in the Transformer encoder. The length of hidden_states is num_hidden_layers + 1. For all element in the tuple, its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

• attentions (Tensor):

  Attentions of all layers of in the Transformer encoder. The length of attentions is num_hidden_layers. For all element in the tuple, its data type should be float32 and its shape is [batch_size, num_attention_heads, sequence_length, sequence_length].

返回类型:

tuple or Dict

class AlbertForMaskedLM(config: AlbertConfig)

基类：AlbertPretrainedModel

Albert Model with a masked language modeling head on top.

参数:

config (AlbertConfig) -- An instance of AlbertConfig used to construct AlbertModel.

get_output_embeddings()

To be overwrited for models with output embeddings

返回:

the otuput embedding of model

返回类型:

Optional[Embedding]

get_input_embeddings()

get input embedding of model

返回:

embedding of model

返回类型:

nn.Embedding

forward(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_hidden_states=False, output_attentions=False, return_dict=False)

The AlbertForPretraining forward method, overrides the __call__() special method.

参数:

• input_ids (Tensor) -- See AlbertModel.

• attention_mask (list, optional) -- See AlbertModel.

• token_type_ids (Tensor, optional) -- See AlbertModel.

• position_ids (Tensor, optional) -- See AlbertModel.

• head_mask (Tensor, optional) -- See AlbertModel.

• inputs_embeds (Tensor, optional) -- See AlbertModel.

• output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to False.

• output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to False.

• return_dict (bool, optional) -- Whether to return a ModelOutput object. If False, the output will be a tuple of tensors. Defaults to False.

返回:

Returns tensor prediction_scores or a dict with logits, hidden_states, attentions fields.

With the fields:

• prediction_scores (Tensor):

  The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length, vocab_size].

• logits (Tensor):

  The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length, vocab_size].

• hidden_states (Tensor):

  Hidden_states of all layers in the Transformer encoder. The length of hidden_states is num_hidden_layers + 1. For all element in the tuple, its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

• attentions (Tensor):

  Attentions of all layers of in the Transformer encoder. The length of attentions is num_hidden_layers. For all element in the tuple, its data type should be float32 and its shape is [batch_size, num_attention_heads, sequence_length, sequence_length].

返回类型:

Tensor or Dict

class AlbertForSequenceClassification(config: AlbertConfig)

基类：AlbertPretrainedModel

Albert Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.

参数:

config (AlbertConfig) -- An instance of AlbertConfig used to construct AlbertModel.

forward(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_hidden_states=False, output_attentions=False, return_dict=False)

The AlbertForSequenceClassification forward method, overrides the __call__() special method.

参数:

• input_ids (Tensor) -- See AlbertModel.

• attention_mask (list, optional) -- See AlbertModel.

• token_type_ids (Tensor, optional) -- See AlbertModel.

• position_ids (Tensor, optional) -- See AlbertModel.

• head_mask (Tensor, optional) -- See AlbertModel.

• inputs_embeds (Tensor, optional) -- See AlbertModel.

• labels (Tensor of shape (batch_size,), optional) -- Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., num_labels - 1]. If num_labels == 1 a regression loss is computed (Mean-Square loss), If num_labels > 1 a classification loss is computed (Cross-Entropy).

• output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to False.

• output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to False.

• return_dict (bool, optional) -- Whether to return a SequenceClassifierOutput object. If False, the output will be a tuple of tensors. Defaults to False.

返回:

Returns tensor logits, or a dict with logits, hidden_states, attentions fields.

With the fields:

• logits (Tensor):

  A tensor of the input text classification logits. Shape as [batch_size, num_labels] and dtype as float32.

• hidden_states (Tensor):

  Hidden_states of all layers in the Transformer encoder. The length of hidden_states is num_hidden_layers + 1. For all element in the tuple, its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

• attentions (Tensor):

  Attentions of all layers of in the Transformer encoder. The length of attentions is num_hidden_layers. For all element in the tuple, its data type should be float32 and its shape is [batch_size, num_attention_heads, sequence_length, sequence_length].

返回类型:

Tensor or Dict

示例

import paddle
from paddlenlp.transformers import AlbertForSequenceClassification, AlbertTokenizer

tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
model = AlbertForSequenceClassification.from_pretrained('albert-base-v1')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)

logits = outputs[0]

class AlbertForTokenClassification(config: AlbertConfig)

基类：AlbertPretrainedModel

Albert Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.

参数:

config (AlbertConfig) -- An instance of AlbertConfig used to construct AlbertModel.

forward(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_hidden_states=False, output_attentions=False, return_dict=False)

The AlbertForTokenClassification forward method, overrides the __call__() special method.

参数:

• input_ids (Tensor) -- See AlbertModel.

• attention_mask (list, optional) -- See AlbertModel.

• token_type_ids (Tensor, optional) -- See AlbertModel.

• position_ids (Tensor, optional) -- See AlbertModel.

• head_mask (Tensor, optional) -- See AlbertModel.

• inputs_embeds (Tensor, optional) -- See AlbertModel.

• labels (Tensor of shape (batch_size, sequence_length), optional) -- Labels for computing the token classification loss. Indices should be in [0, ..., num_labels - 1].

• output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to False.

• output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to False.

• return_dict (bool, optional) -- Whether to return a TokenClassifierOutput object. If False, the output will be a tuple of tensors. Defaults to False.

返回:

Returns tensor logits, or a dict with logits, hidden_states, attentions fields.

With the fields:

• logits (Tensor):

  A tensor of the input token classification logits. Shape as [batch_size, sequence_length, num_labels] and dtype as float32.

• hidden_states (Tensor):

  Hidden_states of all layers in the Transformer encoder. The length of hidden_states is num_hidden_layers + 1. For all element in the tuple, its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

• attentions (Tensor):

  Attentions of all layers of in the Transformer encoder. The length of attentions is num_hidden_layers. For all element in the tuple, its data type should be float32 and its shape is [batch_size, num_attention_heads, sequence_length, sequence_length].

返回类型:

Tensor or Dict

示例

import paddle
from paddlenlp.transformers import AlbertForTokenClassification, AlbertTokenizer

tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
model = AlbertForTokenClassification.from_pretrained('albert-base-v1')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)

logits = outputs[0]

class AlbertForQuestionAnswering(config: AlbertConfig)

基类：AlbertPretrainedModel

Albert Model with a linear layer on top of the hidden-states output to compute span_start_logits and span_end_logits, designed for question-answering tasks like SQuAD.

参数:

config (AlbertConfig) -- An instance of AlbertConfig used to construct AlbertModel.

forward(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None, output_hidden_states=False, output_attentions=False, return_dict=False)

The AlbertForQuestionAnswering forward method, overrides the __call__() special method.

参数:

• input_ids (Tensor) -- See AlbertModel.

• attention_mask (list, optional) -- See AlbertModel.

• token_type_ids (Tensor, optional) -- See AlbertModel.

• position_ids (Tensor, optional) -- See AlbertModel.

• head_mask (Tensor, optional) -- See AlbertModel.

• inputs_embeds (Tensor, optional) -- See AlbertModel.

• start_positions (Tensor of shape (batch_size,), optional) -- Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

• end_positions (Tensor of shape (batch_size,), optional) -- Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

• output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to False.

• output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to False.

• return_dict (bool, optional) -- Whether to return a QuestionAnsweringModelOutput object. If False, the output will be a tuple of tensors. Defaults to False.

返回:

Returns tuple (start_logits, end_logits)or a dict with start_logits, end_logits, hidden_states, attentions fields.

With the fields:

• start_logits (Tensor):

  A tensor of the input token classification logits, indicates the start position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length].

• end_logits (Tensor):

  A tensor of the input token classification logits, indicates the end position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length].

• hidden_states (Tensor):

  Hidden_states of all layers in the Transformer encoder. The length of hidden_states is num_hidden_layers + 1. For all element in the tuple, its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

• attentions (Tensor):

  Attentions of all layers of in the Transformer encoder. The length of attentions is num_hidden_layers. For all element in the tuple, its data type should be float32 and its shape is [batch_size, num_attention_heads, sequence_length, sequence_length].

返回类型:

tuple or Dict

示例

import paddle
from paddlenlp.transformers import AlbertForQuestionAnswering, AlbertTokenizer

tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
model = AlbertForQuestionAnswering.from_pretrained('albert-base-v1')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)

logits = outputs[0]

class AlbertForMultipleChoice(config: AlbertConfig)

基类：AlbertPretrainedModel

Albert Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like SWAG tasks .

参数:

config (AlbertConfig) -- An instance of AlbertConfig used to construct AlbertModel.

forward(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_hidden_states=False, output_attentions=False, return_dict=False)

The AlbertForQuestionAnswering forward method, overrides the __call__() special method.

参数:

• input_ids (Tensor) -- See AlbertModel.

• attention_mask (list, optional) -- See AlbertModel.

• token_type_ids (Tensor, optional) -- See AlbertModel.

• position_ids (Tensor, optional) -- See AlbertModel.

• head_mask (Tensor, optional) -- See AlbertModel.

• inputs_embeds (Tensor, optional) -- See AlbertModel.

• labels (Tensor of shape (batch_size, ), optional) -- Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices-1] where num_choices is the size of the second dimension of the input tensors. (See input_ids above)

• output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to False.

• output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to False.

• return_dict (bool, optional) -- Whether to return a MultipleChoiceModelOutput object. If False, the output will be a tuple of tensors. Defaults to False.

返回:

Returns tensor reshaped_logits or a dict with reshaped_logits, hidden_states, attentions fields.

With the fields:

• reshaped_logits (Tensor):

  A tensor of the input multiple choice classification logits. Shape as [batch_size, num_labels] and dtype as float32.

• hidden_states (Tensor):

  Hidden_states of all layers in the Transformer encoder. The length of hidden_states is num_hidden_layers + 1. For all element in the tuple, its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

• attentions (Tensor):

  Attentions of all layers of in the Transformer encoder. The length of attentions is num_hidden_layers. For all element in the tuple, its data type should be float32 and its shape is [batch_size, num_attention_heads, sequence_length, sequence_length].

返回类型:

Tensor or Dict