modeling¶
-
class
RobertaModel
(config: paddlenlp.transformers.roberta.configuration.RobertaConfig, add_pooling_layer=True)[源代码]¶ 基类:
paddlenlp.transformers.roberta.modeling.RobertaPretrainedModel
The bare Roberta Model 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
inRobertaModel
. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingRobertaModel
.hidden_size (int, optional) -- Dimensionality of the embedding layer, encoder layers and pooler layer. Defaults to
768
.num_hidden_layers (int, optional) -- Number of hidden layers in the Transformer encoder. Defaults to
12
.num_attention_heads (int, optional) -- Number of attention heads for each attention layer in the Transformer encoder. Defaults to
12
.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
tointermediate_size
, and then projected back tohidden_size
. Typicallyintermediate_size
is larger thanhidden_size
. Defaults to3072
.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. Defaults to"gelu"
.hidden_dropout_prob (float, optional) -- The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to
0.1
.attention_probs_dropout_prob (float, optional) -- The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. Defaults to
0.1
.max_position_embeddings (int, optional) -- The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input sequence. Defaults to
512
.type_vocab_size (int, optional) -- The vocabulary size of the
token_type_ids
passed when callingRobertaModel
. Defaults to2
.initializer_range (float, optional) --
The standard deviation of the normal initializer. Defaults to 0.02.
注解
A normal_initializer initializes weight matrices as normal distributions. See
RobertaPretrainedModel._init_weights()
for how weights are initialized inRobertaModel
.pad_token_id (int, optional) -- The index of padding token in the token vocabulary. Defaults to
0
.cls_token_id (int, optional) -- The index of cls token in the token vocabulary. Defaults to
101
.
-
set_input_embeddings
(value)[源代码]¶ set new input embedding for model
- 参数
value (Embedding) -- the new embedding of model
- 引发
NotImplementedError -- Model has not implement
set_input_embeddings
method
-
forward
(input_ids: Optional[paddle.Tensor] = None, 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, inputs_embeds: Optional[paddle.Tensor] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[源代码]¶ - 参数
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].token_type_ids (Tensor, optional) --
Segment token indices to indicate first and second portions of the inputs. Indices can be either 0 or 1:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
It's data type should be
int64
and has a shape of [batch_size, sequence_length]. Defaults to None, which means no segment embeddings is added to token 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]
. It's data type should beint64
and has a shape of [batch_size, sequence_length]. Defaults toNone
.attention_mask (Tensor, optional) -- Mask used in multi-head attention to avoid performing attention 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]
. 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 toNone
, 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. Ifpast_key_values
are used, the user can optionally input only the lastinput_ids
(those that don't have their past key value states given to this model) of shape(batch_size, 1)
instead of allinput_ids
of shape(batch_size, sequence_length)
.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 passinginputs_ids
.use_cache (
bool
, optional) -- If set toTrue
,past_key_values
key value states are returned. Defaults toNone
.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. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
BaseModelOutputWithPoolingAndCrossAttentions
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofBaseModelOutputWithPoolingAndCrossAttentions
.
示例
import paddle from paddlenlp.transformers import RobertaModel, RobertaTokenizer tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext') model = RobertaModel.from_pretrained('roberta-wwm-ext') 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
RobertaPretrainedModel
(*args, **kwargs)[源代码]¶ 基类:
paddlenlp.transformers.model_utils.PretrainedModel
An abstract class for pretrained RoBerta models. It provides RoBerta related
model_config_file
,pretrained_init_configuration
,resource_files_names
,pretrained_resource_files_map
,base_model_prefix
for downloading and loading pretrained models. SeePretrainedModel
for more details.-
config_class
¶ alias of
paddlenlp.transformers.roberta.configuration.RobertaConfig
-
base_model_class
¶ alias of
paddlenlp.transformers.roberta.modeling.RobertaModel
-
-
class
RobertaForSequenceClassification
(config: paddlenlp.transformers.roberta.configuration.RobertaConfig)[源代码]¶ 基类:
paddlenlp.transformers.roberta.modeling.RobertaPretrainedModel
Roberta Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- 参数
roberta (
RobertaModel
) -- An instance ofRobertaModel
.num_classes (int, optional) -- The number of classes. Defaults to
2
.dropout (float, optional) -- The dropout probability for output of Roberta. If None, use the same value as
hidden_dropout_prob
ofRobertaModel
instanceroberta
. Defaults toNone
.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: 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)[源代码]¶ - 参数
input_ids (Tensor) -- See
RobertaModel
.token_type_ids (Tensor, optional) -- See
RobertaModel
.position_ids (Tensor, optional) -- See
RobertaModel
.attention_mask (Tensor, optional) -- See
RobertaModel
.inputs_embeds (Tensor, optional) -- See
RobertaModel
.labels (Tensor of shape
(batch_size,)
, optional) -- Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., num_classes - 1]
. Ifnum_classes == 1
a regression loss is computed (Mean-Square loss), Ifnum_classes > 1
a classification loss is computed (Cross-Entropy).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. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
SequenceClassifierOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofSequenceClassifierOutput
.
示例
import paddle from paddlenlp.transformers import RobertaForSequenceClassification, RobertaTokenizer tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext') model = RobertaForSequenceClassification.from_pretrained('roberta-wwm-ext') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
-
class
RobertaForTokenClassification
(config: paddlenlp.transformers.roberta.configuration.RobertaConfig)[源代码]¶ 基类:
paddlenlp.transformers.roberta.modeling.RobertaPretrainedModel
Roberta Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- 参数
roberta (
RobertaModel
) -- An instance ofRobertaModel
.num_classes (int, optional) -- The number of classes. Defaults to
2
.dropout (float, optional) -- The dropout probability for output of Roberta. If None, use the same value as
hidden_dropout_prob
ofRobertaModel
instanceroberta
. Defaults toNone
.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: 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)[源代码]¶ - 参数
input_ids (Tensor) -- See
RobertaModel
.token_type_ids (Tensor, optional) -- See
RobertaModel
.position_ids (Tensor, optional) -- See
RobertaModel
.attention_mask (Tensor, optional) -- See
RobertaModel
.inputs_embeds (Tensor, optional) -- See
RobertaModel
.labels (Tensor of shape
(batch_size, sequence_length)
, optional) -- Labels for computing the token classification loss. Indices should be in[0, ..., num_classes - 1]
.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. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
TokenClassifierOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofTokenClassifierOutput
.
示例
import paddle from paddlenlp.transformers import RobertaForTokenClassification, RobertaTokenizer tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext') model = RobertaForTokenClassification.from_pretrained('roberta-wwm-ext') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
-
class
RobertaForQuestionAnswering
(config: paddlenlp.transformers.roberta.configuration.RobertaConfig)[源代码]¶ 基类:
paddlenlp.transformers.roberta.modeling.RobertaPretrainedModel
- Roberta 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
roberta (
RobertaModel
) -- An instance of RobertaModel.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: 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)[源代码]¶ - 参数
input_ids (Tensor) -- See
RobertaModel
.token_type_ids (Tensor, optional) -- See
RobertaModel
.position_ids (Tensor, optional) -- See
RobertaModel
.attention_mask (Tensor, optional) -- See
RobertaModel
.inputs_embeds (Tensor, optional) -- See
RobertaModel
.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
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. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
QuestionAnsweringModelOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofQuestionAnsweringModelOutput
.
示例
import paddle from paddlenlp.transformers import RobertaForSequenceClassification, RobertaTokenizer tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext') model = RobertaForSequenceClassification.from_pretrained('roberta-wwm-ext') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
- Roberta Model with a linear layer on top of the hidden-states output to compute
-
class
RobertaForMaskedLM
(config: paddlenlp.transformers.roberta.configuration.RobertaConfig)[源代码]¶ 基类:
paddlenlp.transformers.roberta.modeling.RobertaPretrainedModel
Roberta Model with a
masked language modeling
head on top.- 参数
( (bert) -- class:RobertaModel`): An instance of
RobertaModel
.
-
get_output_embeddings
()[源代码]¶ To be overwrited for models with output embeddings
- 返回
the otuput embedding of model
- 返回类型
Optional[Embedding]
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: 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)[源代码]¶ - 参数
input_ids (Tensor) -- See
RobertaModel
.token_type_ids (Tensor, optional) -- See
RobertaModel
.position_ids (Tensor, optional) -- See
RobertaModel
.attention_mask (Tensor, optional) -- See
RobertaModel
.inputs_embeds (Tensor, optional) -- See
RobertaModel
.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]
(seeinput_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
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
MaskedLMOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
MaskedLMOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofMaskedLMOutput
.
示例
import paddle from paddlenlp.transformers import RobertaForMaskedLM, RobertaTokenizer tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext') model = RobertaForMaskedLM.from_pretrained('roberta-wwm-ext') 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]
-
class
RobertaForMultipleChoice
(config: paddlenlp.transformers.roberta.configuration.RobertaConfig)[源代码]¶ 基类:
paddlenlp.transformers.roberta.modeling.RobertaPretrainedModel
RoBerta Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- 参数
bert (
RobertaModel
) -- An instance of RobertaModel.num_choices (int, optional) -- The number of choices. Defaults to
2
.dropout (float, optional) -- The dropout probability for output of Bert. If None, use the same value as
hidden_dropout_prob
ofRobertaModel
instancebert
. Defaults to None.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: 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)[源代码]¶ The RobertaForMultipleChoice forward method, overrides the __call__() special method.
- 参数
input_ids (Tensor) -- See
RobertaModel
and shape as [batch_size, num_choice, sequence_length].token_type_ids (Tensor, optional) -- See
RobertaModel
and shape as [batch_size, num_choice, sequence_length].position_ids (Tensor, optional) -- See
RobertaModel
and shape as [batch_size, num_choice, sequence_length].attention_mask (list, optional) -- See
RobertaModel
and shape as [batch_size, num_choice, sequence_length].inputs_embeds (list, optional) -- See
RobertaModel
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]
wherenum_choices
is the size of the second dimension of the input tensors. (Seeinput_ids
above)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. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
MultipleChoiceModelOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofMultipleChoiceModelOutput
.
示例
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
RobertaForCausalLM
(config: paddlenlp.transformers.roberta.configuration.RobertaConfig)[源代码]¶ 基类:
paddlenlp.transformers.roberta.modeling.RobertaPretrainedModel
Roberta Model with a
Causal language modeling
head on top.- 参数
( (bert) -- class:RobertaModel`): An instance of
RobertaModel
.
-
get_output_embeddings
()[源代码]¶ To be overwrited for models with output embeddings
- 返回
the otuput embedding of model
- 返回类型
Optional[Embedding]
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_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, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[源代码]¶ - 参数
input_ids (Tensor) -- See
RobertaModel
.token_type_ids (Tensor, optional) -- See
RobertaModel
.position_ids (Tensor, optional) -- See
RobertaModel
.attention_mask (Tensor, optional) -- See
RobertaModel
.inputs_embeds (Tensor, optional) -- See
RobertaModel
.past_key_values (tuple(tuple(Tensor)), optional) -- See
RobertaModel
.use_cache (Tensor, optional) -- See
RobertaModel
.attention_mask -- See
RobertaModel
.labels (Tensor of shape
(batch_size, sequence_length)
, optional) -- Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in[-100, 0, ..., vocab_size]
(seeinput_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
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
CausalLMOutputWithCrossAttentions
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
CausalLMOutputWithCrossAttentions
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofCausalLMOutputWithCrossAttentions
.
示例
import paddle from paddlenlp.transformers import RobertaForCausalLM, RobertaTokenizer tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext') model = RobertaForCausalLM.from_pretrained('roberta-wwm-ext') 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]