modeling#
- class MegatronBertModel(config: MegatronBertConfig)[source]#
Bases:
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:
Args
config (
MegatronBertConfig
) – An instance of MegatronBertConfig used to construct MBartModel.
- 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:
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.- config_class#
alias of
MegatronBertConfig
- base_model_class#
alias of
MegatronBertModel
- class MegatronBertForQuestionAnswering(config: MegatronBertConfig)[source]#
Bases:
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(config: MegatronBertConfig)[source]#
Bases:
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(config: MegatronBertConfig)[source]#
Bases:
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(config: MegatronBertConfig)[source]#
Bases:
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(config: MegatronBertConfig)[source]#
Bases:
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(config: MegatronBertConfig)[source]#
Bases:
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(config: MegatronBertConfig)[source]#
Bases:
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(config: MegatronBertConfig)[source]#
Bases:
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)