modeling

class ErnieMModel(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[source]

Bases: paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel

The bare ERNIE-M 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 (ErnieMConfig) – An instance of ErnieMConfig used to construct ErnieMModel.

forward(input_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: 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)[source]
Parameters

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

  • 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]. 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.

  • 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.

  • 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 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. tuple: Returns tuple (sequence_output, pooled_output).

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 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].

Example

import paddle
from paddlenlp.transformers import ErnieMModel, ErnieMTokenizer

tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base')
model = ErnieMModel.from_pretrained('ernie-m-base')

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

Bases: paddlenlp.transformers.model_utils.PretrainedModel

An abstract class for pretrained ERNIE-M models. It provides ERNIE-M related model_config_file, pretrained_init_configuration, resource_files_names, pretrained_resource_files_map, base_model_prefix for downloading and loading pretrained models. Refer to PretrainedModel for more details.

config_class

alias of paddlenlp.transformers.ernie_m.configuration.ErnieMConfig

init_weights(layer)[source]

Initialization hook

base_model_class

alias of paddlenlp.transformers.ernie_m.modeling.ErnieMModel

class ErnieMForSequenceClassification(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[source]

Bases: paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel

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

Parameters

config (ErnieMConfig) – An instance of ErnieMConfig used to construct ErnieMForSequenceClassification.

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

  • input_ids (Tensor) – See ErnieMModel.

  • position_ids (Tensor, optional) – See ErnieMModel.

  • attention_mask (Tensor, optional) – See ErnieMModel.

  • 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).

  • 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 SequenceClassifierOutput object. If False, the output will be a tuple of tensors. Defaults to False.

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 import ErnieMForSequenceClassification, ErnieMTokenizer

tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base')
model = ErnieMForSequenceClassification.from_pretrained('ernie-m-base')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
class ErnieMForTokenClassification(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[source]

Bases: paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel

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

Parameters

config (ErnieMConfig) – An instance of ErnieMConfig used to construct ErnieMForTokenClassification.

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

  • input_ids (Tensor) – See ErnieMModel.

  • position_ids (Tensor, optional) – See ErnieMModel.

  • attention_mask (Tensor, optional) – See ErnieMModel.

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

  • 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 TokenClassifierOutput object. If False, the output will be a tuple of tensors. Defaults to False.

Returns

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

Return type

Tensor

Example

import paddle
from paddlenlp.transformers import ErnieMForTokenClassification, ErnieMTokenizer

tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base')
model = ErnieMForTokenClassification.from_pretrained('ernie-m-base')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
class ErnieMForQuestionAnswering(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[source]

Bases: paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel

Ernie-M 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 (ErnieMConfig) – An instance of ErnieMConfig used to construct ErnieMForQuestionAnswering.

forward(input_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, inputs_embeds: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]
Parameters

  • input_ids (Tensor) – See ErnieMModel.

  • position_ids (Tensor, optional) – See ErnieMModel.

  • attention_mask (Tensor, optional) – See ErnieMModel.

  • 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.

  • 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 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 ErnieMForQuestionAnswering, ErnieMTokenizer

tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base')
model = ErnieMForQuestionAnswering.from_pretrained('ernie-m-base')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
class ErnieMForMultipleChoice(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[source]

Bases: paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel

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

Parameters

config (ErnieMConfig) – An instance of ErnieMConfig used to construct ErnieMForMultipleChoice.

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

The ErnieMForMultipleChoice forward method, overrides the __call__() special method. :param input_ids: See ErnieMModel and shape as [batch_size, num_choice, sequence_length]. :type input_ids: Tensor :param position_ids: See ErnieMModel and shape as [batch_size, num_choice, sequence_length]. :type position_ids: Tensor, optional :param attention_mask: See ErnieMModel and shape as [batch_size, num_choice, sequence_length]. :type attention_mask: list, optional :param labels: 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)

Parameters

  • 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 MultipleChoiceModelOutput object. If False, the output will be a tuple of tensors. Defaults to False.

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.

class UIEM(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[source]

Bases: paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel

Ernie-M Model with two linear layer on top of the hidden-states output to compute start_prob and end_prob, designed for Universal Information Extraction.

Parameters

config (ErnieMConfig) – An instance of ErnieMConfig used to construct UIEM.

forward(input_ids, position_ids=None, attention_mask=None)[source]
Parameters

Example

import paddle
from paddlenlp.transformers import UIEM, ErnieMTokenizer

tokenizer = ErnieMTokenizer.from_pretrained('uie-m-base')
model = UIEM.from_pretrained('uie-m-base')

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