modeling#

class DistilBertModel(config: DistilBertConfig)[source]#

Bases: DistilBertPretrainedModel

The bare DistilBert 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.

  • 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 DistilBertPretrainedModel.init_weights() for how weights are initialized in DistilBertModel.

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

forward(input_ids, attention_mask=None)[source]#

The DistilBertModel 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 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]. Defaults to None, which means nothing needed to be prevented attention to.

Returns:

Returns tensor encoder_output, which means the sequence of hidden-states at the last layer of the model. Its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

Return type:

Tensor

Example

import paddle
from paddlenlp.transformers import DistilBertModel, DistilBertTokenizer

tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertModel.from_pretrained('distilbert-base-uncased')

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

Bases: PretrainedModel

An abstract class for pretrained DistilBert models. It provides DistilBert 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 DistilBertConfig

base_model_class#

alias of DistilBertModel

class DistilBertForSequenceClassification(config: DistilBertConfig)[source]#

Bases: DistilBertPretrainedModel

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

Parameters:

config (DistilBertConfig) – An instance of DistilBertConfig used to construct DistilBertForSequenceClassification.

forward(input_ids, attention_mask=None)[source]#

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

Parameters:
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.distilbert.modeling import DistilBertForSequenceClassification
from paddlenlp.transformers.distilbert.tokenizer import DistilBertTokenizer

tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')

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 DistilBertForTokenClassification(config: DistilBertConfig)[source]#

Bases: DistilBertPretrainedModel

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

Parameters:

config (DistilBertConfig) – An instance of DistilBertConfig used to construct DistilBertForTokenClassification.

forward(input_ids, attention_mask=None)[source]#

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

Parameters:
Returns:

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

Return type:

Tensor

Example

import paddle
from paddlenlp.transformers.distilbert.modeling import DistilBertForTokenClassification
from paddlenlp.transformers.distilbert.tokenizer import DistilBertTokenizer

tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForTokenClassification.from_pretrained('distilbert-base-uncased')

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 DistilBertForQuestionAnswering(config: DistilBertConfig)[source]#

Bases: DistilBertPretrainedModel

DistilBert 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:

config (DistilBertConfig) – An instance of DistilBertConfig used to construct DistilBertForQuestionAnswering.

forward(input_ids, attention_mask=None)[source]#

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

Parameters:
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.distilbert.modeling import DistilBertForQuestionAnswering
from paddlenlp.transformers.distilbert.tokenizer import DistilBertTokenizer

tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased')

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

start_logits = outputs[0]
end_logits  =outputs[1]
class DistilBertForMaskedLM(config: DistilBertConfig)[source]#

Bases: DistilBertPretrainedModel

DistilBert Model with a language modeling head on top.

Parameters:

config (DistilBertConfig) – An instance of DistilBertConfig used to construct DistilBertForMaskedLM

forward(input_ids=None, attention_mask=None)[source]#

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

Parameters:
Returns:

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

Return type:

Tensor

Example

import paddle
from paddlenlp.transformers import DistilBertForMaskedLM, DistilBertTokenizer

tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForMaskedLM.from_pretrained('distilbert-base-uncased')

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