modeling

class BertModel(config: paddlenlp.transformers.bert.configuration.BertConfig)

Bases: paddlenlp.transformers.bert.modeling.BertPretrainedModel

The bare BERT 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

config (BertConfig) – An instance of BertConfig used to construct BertModel.

get_input_embeddings()

get input embedding of model

Returns

embedding of model

Return type

nn.Embedding

set_input_embeddings(value)

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: paddle.Tensor, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, past_key_values: Optional[Tuple[Tuple[paddle.Tensor]]] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)

The BertModel 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.

• 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.

• 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.

• past_key_values (tuple(tuple(Tensor)), optional) – The length of tuple equals to the number of layers, and each inner tuple haves 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) which contains precomputed key and value hidden states of the attention blocks. If past_key_values are used, the user can optionally input only the last input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all input_ids of shape (batch_size, sequence_length).

• use_cache (bool, optional) – If set to True, past_key_values key value states are returned. Defaults to None.

• output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to None.

• output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to None.

• return_dict (bool, optional) – Whether to return a ModelOutput object. If False, the output will be a tuple of tensors. Defaults to None.

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 BertModel, BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-wwm-chinese')
model = BertModel.from_pretrained('bert-wwm-chinese')

inputs = tokenizer("欢迎使用百度飞桨!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
output = model(**inputs)

class BertPretrainedModel(*args, **kwargs)

Bases: paddlenlp.transformers.model_utils.PretrainedModel

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

config_class

alias of paddlenlp.transformers.bert.configuration.BertConfig

init_weights(layer)

Initialization hook

base_model_class

alias of paddlenlp.transformers.bert.modeling.BertModel

class BertForPretraining(config: paddlenlp.transformers.bert.configuration.BertConfig)

Bases: paddlenlp.transformers.bert.modeling.BertPretrainedModel

Bert Model with pretraining tasks on top.

Parameters

config (BertConfig) – An instance of BertConfig used to construct BertForPretraining.

forward(input_ids: paddle.Tensor, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, masked_positions: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, next_sentence_label: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)

Parameters

• input_ids (Tensor) – See BertModel.

• token_type_ids (Tensor, optional) – See BertModel.

• position_ids (Tensor, optional) – See BertModel.

• attention_mask (Tensor, optional) – See BertModel.

• masked_positions (Tensor, optional) – See BertPretrainingHeads.

• labels (Tensor of shape (batch_size, sequence_length), optional) – Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., vocab_size].

• next_sentence_label (Tensor of shape (batch_size,), optional) –

  Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see input_ids docstring) Indices should be in [0, 1]:

  • 0 indicates sequence B is a continuation of sequence A,

  • 1 indicates sequence B is a random sequence.

• output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to None.

• output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to None.

• return_dict (bool, optional) – Whether to return a BertForPreTrainingOutput object. If False, the output will be a tuple of tensors. Defaults to None.

Returns

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

class BertPretrainingCriterion(vocab_size)

Bases: paddle.fluid.dygraph.layers.Layer

Parameters

vocab_size (int) – Vocabulary size of inputs_ids in BertModel. Defines the number of different tokens that can be represented by the inputs_ids passed when calling BertModel.

forward(prediction_scores, seq_relationship_score, masked_lm_labels, next_sentence_labels, masked_lm_scale)

Parameters

• prediction_scores (Tensor) – The scores of masked token prediction. Its data type should be float32. If masked_positions is None, its shape is [batch_size, sequence_length, vocab_size]. Otherwise, its shape is [batch_size, mask_token_num, vocab_size]

• seq_relationship_score (Tensor) – The scores of next sentence prediction. Its data type should be float32 and its shape is [batch_size, 2]

• masked_lm_labels (Tensor) – The labels of the masked language modeling, its dimensionality is equal to prediction_scores. Its data type should be int64. If masked_positions is None, its shape is [batch_size, sequence_length, 1]. Otherwise, its shape is [batch_size, mask_token_num, 1]

• next_sentence_labels (Tensor) – 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]

• masked_lm_scale (Tensor or int) – The scale of masked tokens. Used for the normalization of masked language modeling loss. If it is a Tensor, its data type should be int64 and its shape is equal to prediction_scores.

Returns

The pretraining loss, equals to the sum of masked_lm_loss plus the mean of next_sentence_loss. Its data type should be float32 and its shape is [1].

Return type

Tensor

class BertPretrainingHeads(config: paddlenlp.transformers.bert.configuration.BertConfig, embedding_weights=None)

Bases: paddle.fluid.dygraph.layers.Layer

Perform language modeling task and next sentence classification task.

Parameters

• config (BertConfig) – An instance of BertConfig used to construct BertForPretraining.

• embedding_weights (Tensor, optional) – Decoding weights used to map hidden_states to logits of the masked token prediction. Its data type should be float32 and its shape is [vocab_size, hidden_size]. Defaults to None, which means use the same weights of the embedding layer.

forward(sequence_output, pooled_output, masked_positions=None)

Parameters

• sequence_output (Tensor) – Sequence of hidden-states at the last layer of the model. It’s data type should be float32 and its shape is [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 its shape is [batch_size, hidden_size].

• masked_positions (Tensor, optional) – A tensor indicates positions to be masked in the position embedding. Its data type should be int64 and its shape is [batch_size, mask_token_num]. mask_token_num is the number of masked tokens. It should be no bigger than sequence_length. Defaults to None, which means we output hidden-states of all tokens in masked token prediction.

Returns

Returns tuple (prediction_scores, seq_relationship_score).

With the fields:

• prediction_scores (Tensor):

  The scores of masked token prediction. Its data type should be float32. If masked_positions is None, its shape is [batch_size, sequence_length, vocab_size]. Otherwise, its shape is [batch_size, mask_token_num, vocab_size].

• seq_relationship_score (Tensor):

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

Return type

tuple

class BertForSequenceClassification(config: paddlenlp.transformers.bert.configuration.BertConfig)

Bases: paddlenlp.transformers.bert.modeling.BertPretrainedModel

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

Parameters

config (BertConfig) – An instance of BertConfig used to construct BertForSequenceClassification.

forward(input_ids: paddle.Tensor, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)

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

Parameters

• input_ids (Tensor) – See BertModel.

• token_type_ids (Tensor, optional) – See BertModel.

• position_ids (Tensor, optional) – See BertModel.

• attention_mask (Tensor, optional) – See BertModel.

• 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 None.

• output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to None.

• return_dict (bool, optional) – Whether to return a SequenceClassifierOutput object. If False, the output will be a tuple of tensors. Defaults to None.

Returns

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

Example

import paddle
from paddlenlp.transformers.bert.modeling import BertForSequenceClassification
from paddlenlp.transformers.bert.tokenizer import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = BertForSequenceClassification.from_pretrained('bert-base-cased', num_labels=2)

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

logits = model(**inputs)
print(logits.shape)
# [1, 2]

class BertForTokenClassification(config: paddlenlp.transformers.bert.configuration.BertConfig)

Bases: paddlenlp.transformers.bert.modeling.BertPretrainedModel

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

Parameters

config (BertConfig) – An instance of BertConfig used to construct BertForTokenClassification.

forward(input_ids: paddle.Tensor, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)

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

Parameters

• input_ids (Tensor) – See BertModel.

• token_type_ids (Tensor, optional) – See BertModel.

• position_ids (Tensor, optional) – See BertModel.

• attention_mask (list, optional) – See BertModel.

• 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 None.

• output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to None.

• return_dict (bool, optional) – Whether to return a TokenClassifierOutput object. If False, the output will be a tuple of tensors. Defaults to None.

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.bert.modeling import BertForTokenClassification
from paddlenlp.transformers.bert.tokenizer import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = BertForTokenClassification.from_pretrained('bert-base-cased', num_labels=2)

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

logits = model(**inputs)
print(logits.shape)
# [1, 13, 2]

class BertForQuestionAnswering(config: paddlenlp.transformers.bert.configuration.BertConfig)

Bases: paddlenlp.transformers.bert.modeling.BertPretrainedModel

Bert 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 (BertConfig) – An instance of BertConfig used to construct BertForQuestionAnswering.

forward(input_ids: paddle.Tensor, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, start_positions: Optional[paddle.Tensor] = None, end_positions: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)

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

Parameters

• input_ids (Tensor) – See BertModel.

• token_type_ids (Tensor, optional) – See BertModel.

• position_ids (Tensor, optional) – See BertModel.

• attention_mask (Tensor, optional) – See BertModel.

• 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 None.

• output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to None.

• return_dict (bool, optional) – Whether to return a QuestionAnsweringModelOutput object. If False, the output will be a tuple of tensors. Defaults to None.

Returns

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

Example

import paddle
from paddlenlp.transformers.bert.modeling import BertForQuestionAnswering
from paddlenlp.transformers.bert.tokenizer import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = BertForQuestionAnswering.from_pretrained('bert-base-cased')

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 BertForMultipleChoice(config: paddlenlp.transformers.bert.configuration.BertConfig)

Bases: paddlenlp.transformers.bert.modeling.BertPretrainedModel

Bert 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.

Examples

>>> model = BertForMultipleChoice(config, dropout=0.1)
>>> # or
>>> config.hidden_dropout_prob = 0.1
>>> model = BertForMultipleChoice(config)

forward(input_ids: paddle.Tensor, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)

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

Parameters

• input_ids (Tensor) – See BertModel and shape as [batch_size, num_choice, sequence_length].

• token_type_ids (Tensor, optional) – See BertModel and shape as [batch_size, num_choice, sequence_length].

• position_ids (Tensor, optional) – See BertModel and shape as [batch_size, num_choice, sequence_length].

• attention_mask (list, optional) – See BertModel and shape as [batch_size, num_choice, sequence_length].

• 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 None.

• output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to None.

• return_dict (bool, optional) – Whether to return a MultipleChoiceModelOutput object. If False, the output will be a tuple of tensors. Defaults to None.

Returns

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

Example

import paddle
from paddlenlp.transformers import BertForMultipleChoice, BertTokenizer
from paddlenlp.data import Pad, Dict

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForMultipleChoice.from_pretrained('bert-base-uncased', num_choices=2)

data = [
    {
        "question": "how do you turn on an ipad screen?",
        "answer1": "press the volume button.",
        "answer2": "press the lock button.",
        "label": 1,
    },
    {
        "question": "how do you indent something?",
        "answer1": "leave a space before starting the writing",
        "answer2": "press the spacebar",
        "label": 0,
    },
]

text = []
text_pair = []
for d in data:
    text.append(d["question"])
    text_pair.append(d["answer1"])
    text.append(d["question"])
    text_pair.append(d["answer2"])

inputs = tokenizer(text, text_pair)
batchify_fn = lambda samples, fn=Dict(
    {
        "input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input_ids
        "token_type_ids": Pad(
            axis=0, pad_val=tokenizer.pad_token_type_id
        ),  # token_type_ids
    }
): fn(samples)
inputs = batchify_fn(inputs)

reshaped_logits = model(
    input_ids=paddle.to_tensor(inputs[0], dtype="int64"),
    token_type_ids=paddle.to_tensor(inputs[1], dtype="int64"),
)
print(reshaped_logits.shape)
# [2, 2]

class BertForMaskedLM(config: paddlenlp.transformers.bert.configuration.BertConfig)

Bases: paddlenlp.transformers.bert.modeling.BertPretrainedModel

Bert Model with a masked language modeling head on top.

Parameters

config (BertConfig) – An instance of BertConfig used to construct BertForMaskedLM.

forward(input_ids: paddle.Tensor, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, masked_positions: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)

Parameters

• input_ids (Tensor) – See BertModel.

• token_type_ids (Tensor, optional) – See BertModel.

• position_ids (Tensor, optional) – See BertModel.

• attention_mask (Tensor, optional) – See BertModel.

• labels (Tensor of shape (batch_size, sequence_length), optional) – Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., vocab_size]

• output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to None.

• output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to None.

• return_dict (bool, optional) – Whether to return a MaskedLMOutput object. If False, the output will be a tuple of tensors. Defaults to None.

Returns

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

Example

import paddle
from paddlenlp.transformers import BertForMaskedLM, BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForMaskedLM.from_pretrained('bert-base-uncased')

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

logits = model(**inputs)
print(logits.shape)
# [1, 13, 30522]