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]
. Iftype_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 toNone
, 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 toNone
.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 haveFalse
values and the others haveTrue
values. When the data type is int, themasked
tokens have0
values and the others have1
values. When the data type is float, themasked
tokens have-INF
values and the others have0
values. It is a tensor with shape broadcasted to[batch_size, num_attention_heads, sequence_length, sequence_length]
. Defaults toNone
, 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:
input_ids (Tensor) – See
RemBertModel
.token_type_ids (Tensor, optional) – See
RemBertModel
.position_ids (Tensor, optional) – See
RemBertModel
.attention_mask (Tensor, optional) – See
RemBertModel
.
- 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
andspan_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:
input_ids (Tensor) – See
RemBertModel
.token_type_ids (Tensor, optional) – See
RemBertModel
.position_ids (Tensor, optional) – See
RemBertModel
.attention_mask (Tensor, optional) – See
RemBertModel
.
- 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:
input_ids (Tensor) – See
RemBertModel
.token_type_ids (Tensor, optional) – See
RemBertModel
.position_ids (Tensor, optional) – See
RemBertModel
.attention_mask (Tensor, optional) – See
RemBertModel
.
- 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 asfloat32
.- 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:
input_ids (Tensor) – See
RemBertModel
.token_type_ids (Tensor, optional) – See
RemBertModel
.position_ids (Tensor, optional) – See
RemBertModel
.attention_mask (list, optional) – See
RemBertModel
.
- Returns:
Returns tensor
logits
, a tensor of the input token classification logits. Shape as[batch_size, sequence_length, num_classes]
and dtype asfloat32
.- 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)