modeling#

class RemBertModel(config: RemBertConfig)[source]#

Bases: RemBertPretrainedModel

The bare RemBERT 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.

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

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

The RemBertModel 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.

Returns:

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].

Return type:

tuple

Example

import paddle
from paddlenlp.transformers import RemBertModel, RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')
model = RemBertModel.from_pretrained('rembert')

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 RemBertForMaskedLM(config: RemBertConfig)[source]#

Bases: RemBertPretrainedModel

RemBert Model with a masked language modeling head on top.

Parameters:

config (RemBertConfig) – An instance of RemBertConfig used to construct RemBertForMaskedLM.

forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[source]#
Parameters:
Returns:

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

Return type:

Tensor

Example

import paddle
from paddlenlp.transformers import RemBertForMaskedLM, RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')
model = RemBertForMaskedLM.from_pretrained('rembert')

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

logits = model(**inputs)
class RemBertForQuestionAnswering(config: RemBertConfig)[source]#

Bases: RemBertPretrainedModel

RemBert 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 (RemBertConfig) – An instance of RemBertConfig used to construct RemBertForQuestionAnswering.

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

The RemBertForQuestionAnswering 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 import RemBertForQuestionAnswering
from paddlenlp.transformers import RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')
model = RemBertForQuestionAnswering.from_pretrained('rembert')

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 RemBertForSequenceClassification(config: RemBertConfig)[source]#

Bases: RemBertPretrainedModel

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

Parameters:

config (RemBertConfig) – An instance of RemBertConfig used to construct RemBertForSequenceClassification.

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

The RemBertForSequenceClassification 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 import RemBertForSequenceClassification
from paddlenlp.transformers import RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')
model = RemBertForQuestionAnswering.from_pretrained('rembert', num_classes=2)

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

Bases: RemBertPretrainedModel

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

Parameters:

config (RemBertConfig) – An instance of RemBertConfig used to construct RemBertForMultipleChoice.

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

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

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

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

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

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

Returns:

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

Return type:

Tensor

Example

import paddle
from paddlenlp.transformers import RemBertForMultipleChoice, RemBertTokenizer
from paddlenlp.data import Pad, Dict

tokenizer = RemBertTokenizer.from_pretrained('rembert')
model = RemBertForMultipleChoice.from_pretrained('rembert', 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"),
)
class RemBertPretrainedModel(*args, **kwargs)[source]#

Bases: PretrainedModel

config_class#

alias of RemBertConfig

base_model_class#

alias of RemBertModel

class RemBertForTokenClassification(config: RemBertConfig)[source]#

Bases: RemBertPretrainedModel

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

Parameters:

config (RemBertConfig) – An instance of RemBertConfig used to construct RemBertForTokenClassification.

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

The RemBertForTokenClassification 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 import RemBertForTokenClassification
from paddlenlp.transformers import RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')
model = RemBertForTokenClassification.from_pretrained('rembert')

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)