modeling¶
-
class
MPNetModel
(vocab_size, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=514, initializer_range=0.02, relative_attention_num_buckets=32, layer_norm_eps=1e-05, pad_token_id=1)[source]¶ Bases:
paddlenlp.transformers.mpnet.modeling.MPNetPretrainedModel
The bare MPNet 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
vocab_size (int) – Vocabulary size of
inputs_ids
inMPNetModel
. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingMPNetModel
.hidden_size (int, optional) – Dimensionality of the embedding layer, encoder layer 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
514
.initializer_range (float, optional) –
The standard deviation of the normal initializer. Defaults to 0.02.
Note
A normal_initializer initializes weight matrices as normal distributions. See
MPNetPretrainedModel.init_weights()
for how weights are initialized inMPNetModel
.relative_attention_num_buckets (int, optional) – The number of buckets to use for each attention layer. Defaults to
32
.layer_norm_eps (float, optional) – The epsilon used by the layer normalization layers. Defaults to
1e-5
.pad_token_id (int, optional) – The index of padding token in the token vocabulary. Defaults to
1
.
-
forward
(input_ids, position_ids=None, attention_mask=None)[source]¶ The MPNetModel 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].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. If its data type is int, the values should be either 0 or 1.
1 for tokens that not masked,
0 for tokens that masked.
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 (
<s>
) 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 MPNetModel, MPNetTokenizer tokenizer = MPNetTokenizer.from_pretrained('mpnet-base') model = MPNetModel.from_pretrained('mpnet-base') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} outputs = model(**inputs)
-
class
MPNetPretrainedModel
(*args, **kwargs)[source]¶ Bases:
paddlenlp.transformers.model_utils.PretrainedModel
An abstract class for pretrained MPNet models. It provides MPNet related
model_config_file
,resource_files_names
,pretrained_resource_files_map
,pretrained_init_configuration
,base_model_prefix
for downloading and loading pretrained models. SeePretrainedModel
for more details.-
base_model_class
¶
-
-
class
MPNetForMaskedLM
(mpnet)[source]¶ Bases:
paddlenlp.transformers.mpnet.modeling.MPNetPretrainedModel
MPNet Model with a
language modeling
head on top.- Parameters
MPNet (
MPNetModel
) – An instance ofMPNetModel
.
-
forward
(input_ids, position_ids=None, attention_mask=None, labels=None)[source]¶ - Parameters
input_ids (Tensor) – See
MPNetModel
.position_ids (Tensor, optional) – See
MPNetModel
.attention_mask (Tensor, optional) – See
MPNetModel
.labels (Tensor, optional) – The Labels for computing the masked language modeling loss. Indices should be in
[-100, 0, ..., vocab_size]
Tokens with indices set to-100
are ignored (masked), the loss is only computed for the tokens with labels in[0, ..., vocab_size]
Its shape is [batch_size, sequence_length].
- Returns
Returns tuple (
masked_lm_loss
,prediction_scores
, ``sequence_output`).With the fields:
masked_lm_loss
(Tensor):The masked lm loss. Its data type should be float32 and its shape is [1].
prediction_scores
(Tensor):The scores of masked token prediction. Its data type should be float32. Its shape is [batch_size, sequence_length, vocab_size].
sequence_output
(Tensor):Sequence of hidden-states at the last layer of the model. Its data type should be float32. Its shape is
[batch_size, sequence_length, hidden_size]
.
- Return type
tuple
-
class
MPNetForSequenceClassification
(mpnet, num_classes=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.mpnet.modeling.MPNetPretrainedModel
MPNet Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- Parameters
mpnet (
MPNetModel
) – An instance of MPNetModel.num_classes (int, optional) – The number of classes. Defaults to
2
.dropout (float, optional) – The dropout probability for output of MPNet. If None, use the same value as
hidden_dropout_prob
ofMPNetModel
instancempnet
. Defaults to None.
-
forward
(input_ids, position_ids=None, attention_mask=None)[source]¶ The MPNetForSequenceClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MPNetModel
.position_ids (Tensor, optional) – See
MPNetModel
.attention_mask (list, optional) – See
MPNetModel
.
- 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 MPNetForSequenceClassification, MPNetTokenizer tokenizer = MPNetTokenizer.from_pretrained('mpnet-base') model = MPNetForSequenceClassification.from_pretrained('mpnet-base') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} outputs = model(**inputs) logits = outputs[0]
-
class
MPNetForMultipleChoice
(mpnet, num_choices=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.mpnet.modeling.MPNetPretrainedModel
MPNet Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- Parameters
mpnet (
MPNetModel
) – An instance of MPNetModel.num_choices (int, optional) – The number of choices. Defaults to
2
.dropout (float, optional) – The dropout probability for output of MPNet. If None, use the same value as
hidden_dropout_prob
ofMPNetModel
instancempnet
. Defaults to None.
-
forward
(input_ids, position_ids=None, attention_mask=None)[source]¶ The MPNetForMultipleChoice forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MPNetModel
and shape as [batch_size, num_choice, sequence_length].position_ids (Tensor, optional) – See
MPNetModel
and shape as [batch_size, num_choice, sequence_length].attention_mask (list, optional) – See
MPNetModel
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
-
class
MPNetForTokenClassification
(mpnet, num_classes=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.mpnet.modeling.MPNetPretrainedModel
MPNet Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- Parameters
mpnet (
MPNetModel
) – An instance of MPNetModel.num_classes (int, optional) – The number of classes. Defaults to
2
.dropout (float, optional) – The dropout probability for output of MPNet. If None, use the same value as
hidden_dropout_prob
ofMPNetModel
instancempnet
. Defaults to None.
-
forward
(input_ids, position_ids=None, attention_mask=None)[source]¶ The MPNetForTokenClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MPNetModel
.position_ids (Tensor, optional) – See
MPNetModel
.attention_mask (list, optional) – See
MPNetModel
.
- 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 MPNetForTokenClassification, MPNetTokenizer tokenizer = MPNetTokenizer.from_pretrained('mpnet-base') model = MPNetForTokenClassification.from_pretrained('mpnet-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
MPNetForQuestionAnswering
(mpnet, num_classes=2)[source]¶ Bases:
paddlenlp.transformers.mpnet.modeling.MPNetPretrainedModel
MPNet 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
mpnet (
MPNetModel
) – An instance of MPNetModel.num_classes (int, optional) – The number of classes. Defaults to
2
.
-
forward
(input_ids, position_ids=None, attention_mask=None)[source]¶ The MPNetForQuestionAnswering forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MPNetModel
.position_ids (Tensor, optional) – See
MPNetModel
.attention_mask (Tensor, optional) – See
MPNetModel
.
- 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 MPNetForQuestionAnswering, MPNetTokenizer tokenizer = MPNetTokenizer.from_pretrained('mpnet-base') model = MPNetForQuestionAnswering.from_pretrained('mpnet-base') 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]