modeling

class NeZhaModel(config: NeZhaConfig)[source]

Bases: NeZhaPretrainedModel

The bare NeZha 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) – Vocabulary size of inputs_ids in DistilBertModel. Defines the number of different tokens that can be represented by the inputs_ids passed when calling DistilBertModel.
hidden_size (int, optional) – Dimensionality of the embedding layer, encoder layers and the pooler layer. Defaults to 768.
num_hidden_layers (int, optional) – Number of hidden layers in the Transformer encoder. Defaults to 12.
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. Defaults to 3072.
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. Defaults to "gelu".
hidden_dropout_prob (float, optional) – The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to 0.1.
attention_probs_dropout_prob (float, optional) – The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. Defaults to 0.1.
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 16.
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 NeZhaPretrainedModel.init_weights() for how weights are initialized in NeZhaModel.
max_relative_embeddings (int, optional) – The maximum value of the dimensionality of relative encoding, which dictates the maximum supported relative distance of two sentences. Defaults to 64.
layer_norm_eps (float, optional) – The small value added to the variance in LayerNorm to prevent division by zero. Defaults to 1e-12.
use_relative_position (bool, optional) – Whether or not to use relative position embedding. Defaults to True.

The NeZhaModel 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].
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.
attention_mask (Tensor, optional) – Mask used in multi-head attention to avoid performing attention 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]. For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length], [batch_size, num_attention_heads, sequence_length, sequence_length]. We use whole-word-mask in NeZha, so the whole word will have the same value. For example, “使用” as a word, “使” and “用” will have the same value. Defaults to None, which means nothing needed to be prevented attention to.
inputs_embeds (Tensor, optional) – 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.
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:

An instance of BaseModelOutputWithPoolingAndCrossAttentions if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of BaseModelOutputWithPoolingAndCrossAttentions.

Example

import paddle
from paddlenlp.transformers import NeZhaModel, NeZhaTokenizer

tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')
model = NeZhaModel.from_pretrained('nezha-base-chinese')

inputs = tokenizer("欢迎使用百度飞浆!", return_tensors='pt')
output = model(**inputs)

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

class NeZhaPretrainedModel(*args, **kwargs)[source]

Bases: PretrainedModel

An abstract class for pretrained NeZha models. It provides NeZha 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: alias of NeZhaConfig

base_model_class: alias of NeZhaModel

class NeZhaForPretraining(config: NeZhaConfig)[source]

Bases: NeZhaPretrainedModel

NeZha Model with pretraining tasks on top.

Parameters:: nezha (NeZhaModel) – An instance of NeZhaModel.

Parameters:

input_ids (Tensor) – See NeZhaModel.
token_type_ids (Tensor, optional) – See NeZhaModel.
attention_mask (Tensor, optional) – See NeZhaModel.
inputs_embeds (Tensor, optional) – See NeZhaModel.
masked_lm_labels (Tensor, optional) – The labels of the masked language modeling, its dimensionality is equal to prediction_scores. Its data type should be int64 and its shape is [batch_size, sequence_length, 1].
next_sentence_label (Tensor, optional) – The labels of the next sentence prediction task, the dimensionality of next_sentence_labels is equal to seq_relation_labels. Its data type should be int64 and its shape is [batch_size, 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 NeZhaForPreTrainingOutput object. If False, the output will be a tuple of tensors. Defaults to False.

Returns:

An instance of NeZhaForPreTrainingOutput if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of NeZhaForPreTrainingOutput.

class NeZhaForSequenceClassification(config: NeZhaConfig)[source]

Bases: NeZhaPretrainedModel

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

Parameters:: config (NeZhaConfig) – An instance of NeZhaConfig used to construct NeZhaForSequenceClassification.

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

Parameters:

input_ids (Tensor) – See NeZhaModel.
token_type_ids (Tensor, optional) – See NeZhaModel.
attention_mask (Tensor, optional) – See NeZhaModel.
inputs_embeds (Tensor, optional) – See NeZhaModel.
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.

Returns:

Returns tensor logits, a tensor of the input text classification logits. Shape as [batch_size, num_classes] and dtype as float32.

Return type:

Tensor

Example

import paddle
from paddlenlp.transformers import NeZhaForSequenceClassification
from paddlenlp.transformers import NeZhaTokenizer

tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')
model = NeZhaForSequenceClassification.from_pretrained('nezha-base-chinese')

inputs = tokenizer("欢迎使用百度飞浆!", return_tensors='pt')
output = model(**inputs)

logits = outputs[0]

class NeZhaForTokenClassification(config: NeZhaConfig)[source]

Bases: NeZhaPretrainedModel

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

Parameters:: config (NeZhaConfig) – An instance of NeZhaConfig used to construct NeZhaForSequenceClassification.

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

Parameters:

input_ids (Tensor) – See NeZhaModel.
token_type_ids (Tensor, optional) – See NeZhaModel.
attention_mask (list, optional) – See NeZhaModel.
inputs_embeds (Tensor, optional) – See NeZhaModel.
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.

Returns:

An instance of TokenClassifierOutput if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of TokenClassifierOutput.

Example

import paddle
from paddlenlp.transformers import NeZhaForTokenClassification
from paddlenlp.transformers import NeZhaTokenizer

tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')
model = NeZhaForTokenClassification.from_pretrained('nezha-base-chinese')

inputs = tokenizer("欢迎使用百度飞浆!", return_tensors='pt')
output = model(**inputs)

logits = outputs[0]

class NeZhaForQuestionAnswering(config: NeZhaConfig)[source]

Bases: NeZhaPretrainedModel

NeZha 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:: config (NeZhaConfig) – An instance of NeZhaConfig used to construct NeZhaForQuestionAnswering.

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

Parameters:

input_ids (Tensor) – See NeZhaModel.
token_type_ids (Tensor, optional) – See NeZhaModel.
attention_mask (Tensor, optional) – See NeZhaModel.
inputs_embeds (Tensor, optional) – See NeZhaModel.
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).

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].

Return type:

tuple

Example

import paddle
from paddlenlp.transformers import NeZhaForQuestionAnswering
from paddlenlp.transformers import NeZhaTokenizer

tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')
model = NeZhaForQuestionAnswering.from_pretrained('nezha-base-chinese')

inputs = tokenizer("欢迎使用百度飞浆!", return_tensors='pt')
outputs = model(**inputs)

start_logits = outputs[0]
end_logits  =outputs[1]

class NeZhaForMultipleChoice(config: NeZhaConfig)[source]

Bases: NeZhaPretrainedModel

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

Parameters:: config (BertConfig) – An instance of BertConfig used to construct BertForMultipleChoice.

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

Parameters:

input_ids (Tensor) – See NeZhaModel.
token_type_ids (Tensor, optional) – See NeZhaModel.
attention_mask (list, optional) – See NeZhaModel.
inputs_embeds (Tensor, optional) – See NeZhaModel.
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 QuestionAnsweringModelOutput object. If False, the output will be a tuple of tensors. Defaults to False.

Returns:

Returns tensor reshaped_logits, a tensor of the input multiple choice classification logits. Shape as [batch_size, num_classes] and dtype as float32.

Return type:

Tensor