modeling¶
-
class
ConvBertModel
(vocab_size, embedding_size=768, 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=512, type_vocab_size=2, initializer_range=0.02, pad_token_id=0, conv_kernel_size=9, head_ratio=2, num_groups=1)[source]¶ Bases:
paddlenlp.transformers.convbert.modeling.ConvBertPretrainedModel
The bare ConvBert 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
inConvBertModel
. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingConvBertModel
.embedding_size (int, optional) – Dimensionality of the embedding layer. Defaults to
768
.hidden_size (int, optional) – Dimensionality of the 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
512
.type_vocab_size (int, optional) – The vocabulary size of
token_type_ids
. Defaults to2
.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
ConvBertPretrainedModel.init_weights()
for how weights are initialized inConvBertModel
.pad_token_id (int, optional) – The index of padding token in the token vocabulary. Defaults to
0
.conv_kernel_size (int, optional) – The size of the convolutional kernel. Defaults to
9
.head_ratio (int, optional) – Ratio gamma to reduce the number of attention heads. Defaults to
2
.num_groups (int, optional) – The number of groups for grouped linear layers for ConvBert model. Defaults to
1
.
-
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, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The ConvBertModel 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. 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 Tensor
sequence_output
, sequence of hidden-states at the last layer of the model. Shape as[batch_size, sequence_length, hidden_size]
and dtype as float32.- Return type
Tensor
Example
import paddle from paddlenlp.transformers import ConvBertModel, ConvBertTokenizer tokenizer = ConvBertTokenizer.from_pretrained('convbert-base') model = ConvBertModel.from_pretrained('convbert-base') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} output = model(**inputs)
-
class
ConvBertPretrainedModel
(*args, **kwargs)[source]¶ Bases:
paddlenlp.transformers.model_utils.PretrainedModel
An abstract class for pretrained ConvBert models. It provides ConvBert 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.-
base_model_class
¶ alias of
paddlenlp.transformers.convbert.modeling.ConvBertModel
-
-
class
ConvBertForTotalPretraining
(generator, discriminator)[source]¶ Bases:
paddlenlp.transformers.convbert.modeling.ConvBertPretrainedModel
Combine generator with discriminator for Replaced Token Detection (RTD) pretraining.
-
get_input_embeddings
()[source]¶ get input embedding of model
- Returns
embedding of model
- Return type
nn.Embedding
-
get_output_embeddings
()[source]¶ To be overwrited for models with output embeddings
- Returns
the otuput embedding of model
- Return type
Optional[Embedding]
-
get_discriminator_inputs
(inputs, raw_inputs, gen_logits, gen_labels, use_softmax_sample)[source]¶ Sample from the generator to create discriminator input.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, raw_input_ids=None, gen_labels=None)[source]¶ - Parameters
input_ids (Tensor) – See
ConvBertModel
.token_type_ids (Tensor, optional) – See
ConvBertModel
.position_ids (Tensor, optional) – See
ConvBertModel
.attention_mask (Tensor, optional) – See
ConvBertModel
.raw_input_ids (Tensor, optional) – The raw input_ids. Its data type should be
int64
and it has a shape of [batch_size, sequence_length].gen_labels (Tensor, optional) – The generator labels. Its data type should be
int64
and it has a shape of [batch_size, sequence_length].
- Returns
Returns tuple (
gen_logits
,disc_logits
,disc_labels
,attention_mask
).With the fields:
gen_logits
(Tensor):a tensor of the generator prediction logits. Shape as
[batch_size, sequence_length, vocab_size]
and dtype as float32.
disc_logits
(Tensor):a tensor of the discriminator prediction logits. Shape as
[batch_size, sequence_length]
and dtype as float32.
disc_labels
(Tensor):a tensor of the discriminator prediction labels. Shape as
[batch_size, sequence_length]
and dtype as int64.
attention_mask
(Tensor):See
ConvBertModel
.
- Return type
tuple
-
-
class
ConvBertDiscriminator
(convbert)[source]¶ Bases:
paddlenlp.transformers.convbert.modeling.ConvBertPretrainedModel
ConvBert Model with a discriminator prediction head on top.
- Parameters
convbert (
ConvBertModel
) – An instance of ConvBertModel.
-
forward
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The ConvBertDiscriminator 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. 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 tensor
logits
, a tensor of the discriminator prediction logits. Shape as[batch_size, sequence_length]
and dtype as float32.- Return type
Tensor
Example
import paddle from paddlenlp.transformers import ConvBertDiscriminatorPredictions, ConvBertTokenizer tokenizer = ConvBertTokenizer.from_pretrained('convbert-base') model = ConvBertDiscriminatorPredictions.from_pretrained('convbert-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
ConvBertGenerator
(convbert)[source]¶ Bases:
paddlenlp.transformers.convbert.modeling.ConvBertPretrainedModel
ConvBert Model with a generator prediction head on top.
- Parameters
convbert (
ConvBertModel
) – An instance of ConvBertModel.
-
get_input_embeddings
()[source]¶ get input embedding of model
- Returns
embedding of model
- Return type
nn.Embedding
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The ConvBertGenerator 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. 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 tensor
prediction_scores
, a tensor of the generator prediction scores. Shape as[batch_size, sequence_length, vocab_size]
and dtype as float32.- Return type
Tensor
Example
import paddle from paddlenlp.transformers import ConvBertGenerator, ConvBertTokenizer tokenizer = ConvBertTokenizer.from_pretrained('convbert-base') model = ConvBertGenerator.from_pretrained('convbert-base') 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
ConvBertClassificationHead
(hidden_size, hidden_dropout_prob, num_classes)[source]¶ Bases:
paddle.fluid.dygraph.layers.Layer
ConvBert head for sentence-level classification tasks.
-
class
ConvBertForSequenceClassification
(convbert, num_classes=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.convbert.modeling.ConvBertPretrainedModel
ConvBert Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- Parameters
convbert (
ConvBertModel
) – An instance of ConvBertModel.num_classes (int, optional) – The number of classes. Defaults to
2
.dropout (float, optional) – The dropout probability for output of ConvBert. If None, use the same value as
hidden_dropout_prob
ofConvBertModel
instanceconvbert
. Defaults to None.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The ConvBertForSequenceClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
ConvBertModel
.token_type_ids (Tensor, optional) – See
ConvBertModel
.position_ids (Tensor, optional) – See
ConvBertModel
.attention_mask (list, optional) – See
ConvBertModel
.
- 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 ConvBertForSequenceClassification, ConvBertTokenizer tokenizer = ConvBertTokenizer.from_pretrained('convbert-base') model = ConvBertForSequenceClassification.from_pretrained('convbert-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
ConvBertForTokenClassification
(convbert, num_classes=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.convbert.modeling.ConvBertPretrainedModel
ConvBert Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- Parameters
convbert (
ConvBertModel
) – An instance of ConvBertModel.num_classes (int, optional) – The number of classes. Defaults to
2
.dropout (float, optional) – The dropout probability for output of ConvBert. If None, use the same value as
hidden_dropout_prob
ofConvBertModel
instanceconvbert
. Defaults to None.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The ConvBertForTokenClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
ConvBertModel
.token_type_ids (Tensor, optional) – See
ConvBertModel
.position_ids (Tensor, optional) – See
ConvBertModel
.attention_mask (list, optional) – See
ConvBertModel
.
- 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 ConvBertForTokenClassification, ConvBertTokenizer tokenizer = ConvBertTokenizer.from_pretrained('convbert-base') model = ConvBertForTokenClassification.from_pretrained('convbert-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
ConvBertPretrainingCriterion
(vocab_size, gen_weight, disc_weight)[source]¶ Bases:
paddle.fluid.dygraph.layers.Layer
- Parameters
vocab_size (int) – Vocabulary size of
inputs_ids
inConvBertModel
. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingConvBertModel
.gen_weight (float) – This is the generator weight.
disc_weight (float) – This is the discriminator weight.
-
forward
(generator_prediction_scores, discriminator_prediction_scores, generator_labels, discriminator_labels, attention_mask)[source]¶ Defines the computation performed at every call. Should be overridden by all subclasses.
- Parameters
*inputs (tuple) – unpacked tuple arguments
**kwargs (dict) – unpacked dict arguments
-
class
ConvBertForQuestionAnswering
(convbert)[source]¶ Bases:
paddlenlp.transformers.convbert.modeling.ConvBertPretrainedModel
ConvBert 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
convbert (
ConvBertModel
) – An instance of ConvBertModel.
-
forward
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The ConvBertForQuestionAnswering forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
ConvBertModel
.token_type_ids (Tensor, optional) – See
ConvBertModel
.position_ids (Tensor, optional) – See
ConvBertModel
.attention_mask (list, optional) – See
ConvBertModel
.
- 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 ConvBertForQuestionAnswering, ConvBertTokenizer tokenizer = ConvBertTokenizer.from_pretrained('convbert-base') model = ConvBertForQuestionAnswering.from_pretrained('convbert-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]
-
class
ConvBertForMultipleChoice
(convbert, num_choices=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.convbert.modeling.ConvBertPretrainedModel
ConvBert Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks .
- Parameters
convbert (
ConvBertModel
) – An instance of ConvBertModel.num_choices (int, optional) – The number of choices. Defaults to
2
.dropout (float, optional) – The dropout probability for output of ConvBert. If None, use the same value as
hidden_dropout_prob
ofConvBertModel
instanceconvbert
. Defaults to None.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The ConvBertForMultipleChoice forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
ConvBertModel
and shape as [batch_size,num_choice, sequence_length].token_type_ids (Tensor, optional) – See
ConvBertModel
and shape as [batch_size,num_choice, sequence_length].position_ids (Tensor, optional) – See
ConvBertModel
and shape as [batch_size,num_choice, sequence_length].attention_mask (list, optional) – See
ConvBertModel
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 ConvBertForMultipleChoice, ConvBertTokenizer tokenizer = ConvBertTokenizer.from_pretrained('convbert-base') model = ConvBertForMultipleChoice.from_pretrained('convbert-base') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
-
ConvBertForPretraining
¶ alias of
paddlenlp.transformers.convbert.modeling.ConvBertForTotalPretraining