modeling

class ElectraModel(vocab_size, embedding_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, pad_token_id)[源代码]

基类:paddlenlp.transformers.electra.modeling.ElectraPretrainedModel

The bare Electra 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.

参数
  • vocab_size (int) -- Vocabulary size of inputs_ids in ElectraModel. 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 ElectraModel.

  • embedding_size (int, optional) -- Dimensionality of the embedding layer.

  • hidden_size (int, optional) -- Dimensionality of the encoder layer and pooler layer.

  • num_hidden_layers (int, optional) -- Number of hidden layers in the Transformer encoder.

  • num_attention_heads (int, optional) -- Number of attention heads for each attention layer in the Transformer encoder.

  • 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.

  • 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.

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

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

  • type_vocab_size (int, optional) -- The vocabulary size of token_type_ids.

  • initializer_range (float, optional) --

    The standard deviation of the normal initializer.

    注解

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

  • pad_token_id (int, optional) -- The index of padding token in the token vocabulary.

forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]

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

参数
  • 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.

返回

Returns tensor encoder_outputs, which is the output at the last layer of the model. Its data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size].

返回类型

Tensor

示例

import paddle
from paddlenlp.transformers import ElectraModel, ElectraTokenizer

tokenizer = ElectraTokenizer.from_pretrained('electra-small')
model = ElectraModel.from_pretrained('electra-small')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
output = model(**inputs)
class ElectraPretrainedModel(name_scope=None, dtype='float32')[源代码]

基类:paddlenlp.transformers.model_utils.PretrainedModel

An abstract class for pretrained Electra models. It provides Electra 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.

init_weights()[源代码]

Initializes and tie weights if needed.

tie_weights()[源代码]

Tie the weights between the input embeddings and the output embeddings.

base_model_class

alias of paddlenlp.transformers.electra.modeling.ElectraModel

class ElectraForTotalPretraining(generator, discriminator)[源代码]

基类:paddlenlp.transformers.electra.modeling.ElectraPretrainedModel

Electra Model for pretraining tasks.

参数
forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, raw_input_ids=None, gen_labels=None)[源代码]

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

参数
  • input_ids (Tensor) -- See ElectraModel.

  • token_type_ids (Tensor, optional) -- See ElectraModel.

  • position_ids (Tensor, optional) -- See ElectraModel.

  • attention_mask (list, optional) -- See ElectraModel.

  • raw_input_ids (Tensor, optional) -- Raw inputs used to get discriminator labels. Its data type should be int64 and it has a shape of [batch_size, sequence_length].

  • gen_labels (Tensor, optional) -- Labels to compute the discriminator inputs. Its data type should be int64 and its shape is [batch_size, sequence_length]. The value for unmasked tokens should be -100 and value for masked tokens should be 0.

返回

Returns tuple (gen_logits, disc_logits, disc_labels, attention_mask).

With the fields:

  • gen_logits (Tensor):

    The scores of Electra Generator. Its data type should be int64 and its shape is [batch_size, sequence_length, vocab_size].

  • disc_logits (Tensor):

    The the prediction result of replaced tokens. Its data type should be float32 and if batch_size>1, its shape is [batch_size, sequence_length], if batch_size=1, its shape is [sequence_length].

  • disc_labels (Tensor):

    The labels of electra discriminator. Its data type should be int32, and its shape is [batch_size, sequence_length].

  • attention_mask (Tensor):

    See ElectraModel. Its data type should be bool.

返回类型

tuple

class ElectraDiscriminator(electra)[源代码]

基类:paddlenlp.transformers.electra.modeling.ElectraPretrainedModel

The Electra Discriminator can detect the tokens that are replaced by the Electra Generator.

参数

electra (ElectraModel) -- An instance of ElectraModel.

forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]
参数
返回

Returns tensor logits, the prediction result of replaced tokens. Its data type should be float32 and if batch_size>1, its shape is [batch_size, sequence_length], if batch_size=1, its shape is [sequence_length].

返回类型

Tensor

示例

import paddle
from paddlenlp.transformers import ElectraDiscriminator, ElectraTokenizer

tokenizer = ElectraTokenizer.from_pretrained('electra-small')
model = ElectraDiscriminator.from_pretrained('electra-small')

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

基类:paddlenlp.transformers.electra.modeling.ElectraPretrainedModel

The Electra Generator will replace some tokens of the given sequence, it is trained as a masked language model.

参数

electra (ElectraModel) -- An instance of ElectraModel.

forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]
参数
返回

Returns tensor prediction_scores, the scores of Electra Generator. Its data type should be int64 and its shape is [batch_size, sequence_length, vocab_size].

返回类型

Tensor

示例

import paddle
from paddlenlp.transformers import ElectraGenerator, ElectraTokenizer

tokenizer = ElectraTokenizer.from_pretrained('electra-small')
model = ElectraGenerator.from_pretrained('electra-small')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
prediction_scores = model(**inputs)
class ElectraClassificationHead(hidden_size, hidden_dropout_prob, num_classes)[源代码]

基类:paddle.fluid.dygraph.layers.Layer

Perform sentence-level classification tasks.

参数
  • hidden_size (int) -- Dimensionality of the embedding layer.

  • hidden_dropout_prob (float) -- The dropout probability for all fully connected layers.

  • num_classes (int) -- The number of classes.

forward(features, **kwargs)[源代码]

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

参数

features (Tensor) -- Input sequence, usually the sequence_output of electra model. Its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

返回

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

返回类型

Tensor

class ElectraForSequenceClassification(electra, num_classes=2, dropout=None)[源代码]

基类:paddlenlp.transformers.electra.modeling.ElectraPretrainedModel

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

参数
  • electra (ElectraModel) -- An instance of ElectraModel.

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

  • dropout (float, optional) -- The dropout probability for output of Electra. If None, use the same value as hidden_dropout_prob of ElectraModel instance electra. Defaults to None.

forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]

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

参数
返回

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

返回类型

Tensor

示例

import paddle
from paddlenlp.transformers import ElectraForSequenceClassification
from paddlenlp.transformers import ElectraTokenizer

tokenizer = ElectraTokenizer.from_pretrained('electra-small')
model = ElectraForSequenceClassification.from_pretrained('electra-small')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
class ElectraForTokenClassification(electra, num_classes=2, dropout=None)[源代码]

基类:paddlenlp.transformers.electra.modeling.ElectraPretrainedModel

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

参数
  • electra (ElectraModel) -- An instance of ElectraModel.

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

  • dropout (float, optional) -- The dropout probability for output of Electra. If None, use the same value as hidden_dropout_prob of ElectraModel instance electra. Defaults to None.

forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]

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

参数
返回

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

返回类型

Tensor

示例

import paddle
from paddlenlp.transformers import ElectraForTokenClassification
from paddlenlp.transformers import ElectraTokenizer

tokenizer = ElectraTokenizer.from_pretrained('electra-small')
model = ElectraForTokenClassification.from_pretrained('electra-small')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
class ElectraPretrainingCriterion(vocab_size, gen_weight, disc_weight)[源代码]

基类:paddle.fluid.dygraph.layers.Layer

参数
  • vocab_size (int) -- Vocabulary size of inputs_ids in ElectraModel. Defines the number of different tokens that can be represented by the inputs_ids passed when calling ElectraModel.

  • gen_weight (float) -- The weight of the Electra Generator.

  • disc_weight (float) -- The weight of the Electra Discriminator.

forward(generator_prediction_scores, discriminator_prediction_scores, generator_labels, discriminator_labels, attention_mask)[源代码]
参数
  • generator_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].

  • discriminator_prediction_scores (Tensor) -- The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length] or [sequence length] if batch_size=1.

  • generator_labels (Tensor) -- The labels of the generator, its dimensionality is equal to generator_prediction_scores. Its data type should be int64 and its shape is [batch_size, sequence_size, 1].

  • discriminator_labels (Tensor) -- The labels of the discriminator, its dimensionality is equal to discriminator_prediction_scores. The labels should be numbers between 0 and 1. Its data type should be float32 and its shape is [batch_size, sequence_size] or [sequence length] if batch_size=1.

  • attention_mask (Tensor) -- See ElectraModel.

返回

The pretraining loss, equals to weighted generator loss plus the weighted discriminator loss. Its data type should be float32 and its shape is [1].

返回类型

Tensor

class ElectraForMultipleChoice(electra, num_choices=2, dropout=None)[源代码]

基类:paddlenlp.transformers.electra.modeling.ElectraPretrainedModel

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

参数
  • electra (ElectraModel) -- An instance of ElectraModel.

  • num_choices (int, optional) -- The number of choices. Defaults to 2.

  • dropout (float, optional) -- The dropout probability for output of Electra. If None, use the same value as hidden_dropout_prob of ElectraModel instance electra. Defaults to None.

forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]

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

参数
  • input_ids (Tensor) -- See ElectraModel and shape as [batch_size, num_choice, sequence_length].

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

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

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

返回

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

返回类型

Tensor

示例

import paddle
from paddlenlp.transformers import ElectraForMultipleChoice, ElectraTokenizer
from paddlenlp.data import Pad, Dict

tokenizer = ElectraTokenizer.from_pretrained('electra-small')
model = ElectraForMultipleChoice.from_pretrained('electra-small', 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 ElectraForQuestionAnswering(electra)[源代码]

基类:paddlenlp.transformers.electra.modeling.ElectraPretrainedModel

Electra 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.

参数

electra (ElectraModel) -- An instance of ElectraModel.

forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]

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

参数
返回

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].

返回类型

tuple

示例

import paddle
from paddlenlp.transformers import ElectraForQuestionAnswering, ElectraTokenizer

tokenizer = ElectraTokenizer.from_pretrained('electra-small')
model = ElectraForQuestionAnswering.from_pretrained('electra-small')

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]
ElectraForMaskedLM

alias of paddlenlp.transformers.electra.modeling.ElectraGenerator