modeling

Modeling classes for ALBERT model.

class AlbertPretrainedModel(*args, **kwargs)[source]

Bases: paddlenlp.transformers.model_utils.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.

base_model_class

alias of paddlenlp.transformers.albert.modeling.AlbertModel

class AlbertModel(vocab_size=30000, embedding_size=128, hidden_size=768, num_hidden_layers=12, num_hidden_groups=1, num_attention_heads=12, intermediate_size=3072, inner_group_num=1, hidden_act='gelu', hidden_dropout_prob=0, attention_probs_dropout_prob=0, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, bos_token_id=2, eos_token_id=3, add_pooling_layer=True)[source]

Bases: paddlenlp.transformers.albert.modeling.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.

Parameters

  • vocab_size (int, optional) – Vocabulary size of inputs_ids in AlbertModel. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by the inputs_ids passed when calling AlbertModel. Defaults to 30000.

  • embedding_size (int, optional) – Dimensionality of the embedding layer. Defaults to 128.

  • hidden_size (int, optional) – Dimensionality of the encoder layer and pooler layer. Defaults to 768.

  • num_hidden_layers (int, optional) – Number of hidden layers in the Transformer encoder. Defaults to 12.

  • inner_group_num (int, optional) – Number of hidden groups in the Transformer encoder. Defaults to 1.

  • num_attention_heads (int, optional) – Number of attention heads for each attention layer in the Transformer encoder. Defaults to 12.

  • intermediate_size (int, optional) – Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors to ff layers are firstly projected from hidden_size to intermediate_size, and then projected back to hidden_size. Typically intermediate_size is larger than hidden_size.

  • inner_group_num – Number of inner groups in a hidden group. Default to 1.

  • hidden_act (str, optional) – The non-linear activation function in the feed-forward layer. "gelu", "relu" and any other paddle supported activation functions are supported.

  • hidden_dropout_prob (float, optional) – The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to 0.

  • attention_probs_dropout_prob (float, optional) – The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. Defaults to 0.

  • max_position_embeddings (int, optional) – The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input sequence. Defaults to 512.

  • type_vocab_size (int, optional) – The vocabulary size of token_type_ids. Defaults to 12.

  • initializer_range (float, optional) –

    The standard deviation of the normal initializer. Defaults to 0.02.

    Note

    A normal_initializer initializes weight matrices as normal distributions. See BertPretrainedModel.init_weights() for how weights are initialized in ElectraModel.

  • layer_norm_eps (float, optional) – The epsilon parameter used in paddle.nn.LayerNorm for initializing layer normalization layers. A small value to the variance added to the normalization layer to prevent division by zero. Default to 1e-12.

  • pad_token_id (int, optional) – The index of padding token in the token vocabulary. Defaults to 0.

  • add_pooling_layer (bool, optional) – Whether or not to add the pooling layer. Default to False.

get_input_embeddings()[source]

get input embedding of model

Returns

embedding of model

Return type

nn.Embedding

set_input_embeddings(value)[source]

set new input embedding for model

Parameters

value (Embedding) – the new embedding of model

Raises

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)[source]

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

Parameters

  • 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.

Returns

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].

Return type

tuple or Dict

Example

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(albert, lm_head, sop_head, vocab_size)[source]

Bases: paddlenlp.transformers.albert.modeling.AlbertPretrainedModel

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

Parameters

  • albert (AlbertModel) – An instance of AlbertModel.

  • lm_head (AlbertMLMHead) – An instance of AlbertSOPHead.

  • sop_head (AlbertSOPHead) – An instance of AlbertSOPHead.

  • vocab_size (int) – See AlbertModel.

get_output_embeddings()[source]

To be overwrited for models with output embeddings

Returns

the otuput embedding of model

Return type

Optional[Embedding]

get_input_embeddings()[source]

get input embedding of model

Returns

embedding of model

Return type

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)[source]

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

Parameters

  • 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.

Returns

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].

Return type

tuple or Dict

class AlbertForMaskedLM(albert)[source]

Bases: paddlenlp.transformers.albert.modeling.AlbertPretrainedModel

Albert Model with a masked language modeling head on top.

Parameters

albert (AlbertModel) – An instance of AlbertModel.

get_output_embeddings()[source]

To be overwrited for models with output embeddings

Returns

the otuput embedding of model

Return type

Optional[Embedding]

get_input_embeddings()[source]

get input embedding of model

Returns

embedding of model

Return type

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)[source]

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

Parameters

  • 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.

Returns

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].

Return type

Tensor or Dict

class AlbertForSequenceClassification(albert, classifier_dropout_prob=0, num_classes=2)[source]

Bases: paddlenlp.transformers.albert.modeling.AlbertPretrainedModel

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

Parameters

  • albert (AlbertModel) – An instance of AlbertModel.

  • classifier_dropput_prob (float, optional) – The dropout probability for the classifier. Defaults to 0.

  • num_classes (int, optional) – The number of classes. Defaults to 2.

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)[source]

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

Parameters

  • 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_classes - 1]. If num_classes == 1 a regression loss is computed (Mean-Square loss), If num_classes > 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.

Returns

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_classes] 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].

Return type

Tensor or Dict

Example

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(albert, num_classes=2)[source]

Bases: paddlenlp.transformers.albert.modeling.AlbertPretrainedModel

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

Parameters

  • albert (AlbertModel) – An instance of AlbertModel.

  • num_classes (int, optional) – The number of classes. Defaults to 2.

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)[source]

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

Parameters

  • 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_classes - 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.

Returns

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_classes] 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].

Return type

Tensor or Dict

Example

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(albert, num_labels=2)[source]

Bases: paddlenlp.transformers.albert.modeling.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.

Parameters

  • albert (AlbertModel) – An instance of AlbertModel.

  • num_classes (int) – The number of classes.

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)[source]

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

Parameters

  • 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.

Returns

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].

Return type

tuple or Dict

Example

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(albert)[source]

Bases: paddlenlp.transformers.albert.modeling.AlbertPretrainedModel

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

Parameters

albert (AlbertModel) – An instance of 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)[source]

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

Parameters

  • 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.

Returns

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_classes] 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].

Return type

Tensor or Dict