modeling¶
-
class
MegatronBertModel
(vocab_size=29056, hidden_size=1024, pad_token_id=0, type_vocab_size=2, hidden_act='gelu', attention_probs_dropout_prob=0.1, num_attention_heads=16, num_hidden_layers=24, max_position_embeddings=512, hidden_dropout_prob=0.1, intermediate_size=4096, position_embedding_type='absolute', initializer_range=0.02)[source]¶ Bases:
paddlenlp.transformers.megatronbert.modeling.MegatronBertPretrainedModel
The bare MegatronBert 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
inMegatronBertModel
. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingMegatronBert
.hidden_size (int, optional) – Dimensionality of the encoder layer and pooler layer. Defaults to
1024
.pad_token_id (int, optional) – The index of padding token in the token vocabulary. Defaults to
0
.type_vocab_size (int, optional) – The vocabulary size of
token_type_ids
. Defaults to2
.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"
.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
.num_attention_heads (int, optional) – Number of attention heads for each attention layer in the Transformer encoder. Defaults to
16
.num_hidden_layers (int, optional) – Number of hidden layers in the Transformer encoder. Defaults to
24
.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
.hidden_dropout_prob (float, optional) – The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to
0.1
.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 to4096
.position_embedding_type (str, optional) – Type of position embedding. Defaults to “absolute”
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
MegatronBertPretrainedModel.init_weights()
for how weights are initialized inMegatronBertModel
.
-
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 MegatronBertModel 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 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 MegatronBertModel, MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased') model = MegatronBertModel.from_pretrained('megatronbert-uncased') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} output = model(**inputs)
-
class
MegatronBertPretrainedModel
(*args, **kwargs)[source]¶ Bases:
paddlenlp.transformers.model_utils.PretrainedModel
An abstract class for pretrained MegatronBert 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.-
base_model_class
¶ alias of
paddlenlp.transformers.megatronbert.modeling.MegatronBertModel
-
-
class
MegatronBertForQuestionAnswering
(megatronbert)[source]¶ Bases:
paddlenlp.transformers.megatronbert.modeling.MegatronBertPretrainedModel
MegatronBert Model with question answering tasks.
- Parameters
megatronbert (
MegatronBertModel
) – An instance ofMegatronBertModel
.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The MegatronBertForQuestionAnswering forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MegatronBertModel
.token_type_ids (Tensor, optional) – See
MegatronBertModel
.position_ids (Tensor, optional) – See
MegatronBertModel
.attention_mask (Tensor, optional) – See
MegatronBertModel
.
- 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 MegatronBertForQuestionAnswering, MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased') model = MegatronBertForQuestionAnswering.from_pretrained('megatronbert-uncased') 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
MegatronBertForSequenceClassification
(megatronbert, num_labels)[source]¶ Bases:
paddlenlp.transformers.megatronbert.modeling.MegatronBertPretrainedModel
MegatronBert Model with sequence classification tasks.
- Parameters
megatronbert (
MegatronBertModel
) – An instance ofMegatronBertModel
.num_labels (int) – The number of labels.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The MegatronBertForSequenceClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MegatronBertModel
.token_type_ids (Tensor, optional) – See
MegatronBertModel
.position_ids (Tensor, optional) – See
MegatronBertModel
.attention_mask (Tensor, optional) – See
MegatronBertModel
.
- Returns
Returns tensor
logits
, a tensor of the sequence classification logits.- Return type
Tensor
Example
import paddle from paddlenlp.transformers import MegatronBertForSequenceClassification, MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased') model = MegatronBertForSequenceClassification.from_pretrained('megatronbert-uncased', num_labels=2) inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
-
class
MegatronBertForNextSentencePrediction
(megatronbert)[source]¶ Bases:
paddlenlp.transformers.megatronbert.modeling.MegatronBertPretrainedModel
MegatronBert Model with a
next sentence prediction (classification)
head on top.- Parameters
megatronbert (
MegatronBertModel
) – An instance ofMegatronBertModel
.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The MegatronBertForNextSentencePrediction forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MegatronBertModel
.token_type_ids (Tensor, optional) – See
MegatronBertModel
.position_ids (Tensor, optional) – See
MegatronBertModel
.attention_mask (Tensor, optional) – See
MegatronBertModel
.
- Returns
- Returns Tensor
seq_relationship_scores
. The scores of next sentence prediction. Its data type should be float32 and its shape is [batch_size, 2].
- Returns Tensor
- Return type
Tensor
Example
import paddle from paddlenlp.transformers import MegatronBertForNextSentencePrediction, MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased') model = MegatronBertForNextSentencePrediction.from_pretrained('megatronbert-uncased') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} seq_relationship_scores = model(**inputs)
-
class
MegatronBertForCausalLM
(megatronbert)[source]¶ Bases:
paddlenlp.transformers.megatronbert.modeling.MegatronBertPretrainedModel
MegatronBert Model with a
causal masked language modeling
head on top.- Parameters
megatronbert (
MegatronBertModel
) – An instance ofMegatronBertModel
.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The MegatronBertForCausalLM forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MegatronBertModel
.token_type_ids (Tensor, optional) – See
MegatronBertModel
.position_ids (Tensor, optional) – See
MegatronBertModel
.attention_mask (Tensor, optional) – See
MegatronBertModel
.
- Returns
- Returns Tensor
prediction_scores
. The scores of masked token prediction. Its data type should be float32. If
masked_positions
is None, its shape is [batch_size, sequence_length, vocab_size]. Otherwise, its shape is [batch_size, mask_token_num, vocab_size].
- Returns Tensor
- Return type
Tensor
Example
import paddle from paddlenlp.transformers import MegatronBertForCausalLM, MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased') model = MegatronBertForCausalLM.from_pretrained('megatronbert-uncased') 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
MegatronBertForPreTraining
(megatronbert)[source]¶ Bases:
paddlenlp.transformers.megatronbert.modeling.MegatronBertPretrainedModel
Megatronbert Model with pretraining tasks on top.
- Parameters
megatronbert (
MegatronBertModel
) – An instance ofMegatronBertModel
.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The MegatronBertForPreTraining forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MegatronBertModel
.token_type_ids (Tensor, optional) – See
MegatronBertModel
.position_ids (Tensor, optional) – See
MegatronBertModel
.attention_mask (Tensor, optional) – See
MegatronBertModel
.
- Returns
Returns tuple (
prediction_scores
,seq_relationship_score
).With the fields:
prediction_scores
(Tensor):The scores of masked token prediction. Its data type should be float32. If
masked_positions
is None, its shape is [batch_size, sequence_length, vocab_size]. Otherwise, its shape is [batch_size, mask_token_num, vocab_size].
seq_relationship_score
(Tensor):The scores of next sentence prediction. Its data type should be float32 and its shape is [batch_size, 2].
- Return type
tuple
Example
import paddle from paddlenlp.transformers import MegatronBertForPreTraining, MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased') model = MegatronBertForPreTraining.from_pretrained('megatronbert-uncased') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} prediction_scores, seq_relationship_score = model(**inputs)
-
class
MegatronBertForMaskedLM
(megatronbert)[source]¶ Bases:
paddlenlp.transformers.megatronbert.modeling.MegatronBertPretrainedModel
MegatronBert Model with a
masked language modeling
head on top.- Parameters
megatronbert (
MegatronBertModel
) – An instance ofMegatronBertModel
.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None)[source]¶ The MegatronBertForMaskedLM forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MegatronBertModel
.token_type_ids (Tensor, optional) – See
MegatronBertModel
.position_ids (Tensor, optional) – See
MegatronBertModel
.attention_mask (Tensor, optional) – See
MegatronBertModel
.
- Returns
- Returns Tensor
prediction_scores
. The scores of masked token prediction. Its data type should be float32. If
masked_positions
is None, its shape is [batch_size, sequence_length, vocab_size]. Otherwise, its shape is [batch_size, mask_token_num, vocab_size].
- Returns Tensor
- Return type
Tensor
Example
import paddle from paddlenlp.transformers import MegatronBertForMaskedLM, MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased') model = MegatronBertForMaskedLM.from_pretrained('megatronbert-uncased') 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
MegatronBertForMultipleChoice
(megatronbert)[source]¶ Bases:
paddlenlp.transformers.megatronbert.modeling.MegatronBertPretrainedModel
MegatronBert Model with a multiple choice classification head on top.
- Parameters
megatronbert (
MegatronBertModel
) – An instance ofMegatronBertModel
.
-
forward
(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ The MegatronBertForMultipleChoice forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MegatronBertModel
.token_type_ids (Tensor, optional) – See
MegatronBertModel
.position_ids (Tensor, optional) – See
MegatronBertModel
.attention_mask (Tensor, optional) – See
MegatronBertModel
.
- Returns
- Returns Tensor
reshaped_logits
. A tensor of the multiple choice classification logits. Shape as
[batch_size, num_choice]
and dtype asfloat32
.
- Returns Tensor
- Return type
Tensor
Example
import paddle from paddlenlp.transformers import MegatronBertForMultipleChoice, MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased') model = MegatronBertForNextSentencePrediction.from_pretrained('megatronbert-uncased') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} reshaped_logits = model(**inputs)
-
class
MegatronBertForTokenClassification
(megatronbert, num_labels)[source]¶ Bases:
paddlenlp.transformers.megatronbert.modeling.MegatronBertPretrainedModel
MegatronBert Model with a token classification head on top.
- Parameters
megatronbert (
MegatronBertModel
) – An instance ofMegatronBertModel
.num_labels (int) – The number of labels.
-
forward
(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None)[source]¶ The MegatronBertForTokenClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
MegatronBertModel
.token_type_ids (Tensor, optional) – See
MegatronBertModel
.position_ids (Tensor, optional) – See
MegatronBertModel
.attention_mask (Tensor, optional) – See
MegatronBertModel
.
- Returns
- Returns tensor
logits
, a tensor of the input token classification logits. Shape as
[batch_size, sequence_length, num_classes]
and dtype asfloat32
.
- Returns tensor
- Return type
Tensor
Example
import paddle from paddlenlp.transformers import MegatronBertForTokenClassification, MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('megatronbert-uncased') model = MegatronBertForTokenClassification.from_pretrained('megatronbert-uncased', num_labels=2) inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} reshaped_logits = model(**inputs)