modeling¶
-
class
ErnieMModel
(vocab_size, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=514, initializer_range=0.02, pad_token_id=1)[source]¶ Bases:
paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel
The bare ERNIE-M 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
inErnieMModel
. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingErnieMModel
.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
. Defaults to2
.initializer_range (float, optional) –
The standard deviation of the normal initializer for initializing all weight matrices. Defaults to
0.02
.Note
A normal_initializer initializes weight matrices as normal distributions. See
ErnieMPretrainedModel._init_weights()
for how weights are initialized inErnieMModel
.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]¶ - Parameters
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].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. 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.
- 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 ErnieMModel, ErnieMTokenizer tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base') model = ErnieMModel.from_pretrained('ernie-m-base') 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
ErnieMPretrainedModel
(name_scope=None, dtype='float32')[source]¶ Bases:
paddlenlp.transformers.model_utils.PretrainedModel
An abstract class for pretrained ERNIE-M models. It provides ERNIE-M related
model_config_file
,pretrained_init_configuration
,resource_files_names
,pretrained_resource_files_map
,base_model_prefix
for downloading and loading pretrained models. Refer toPretrainedModel
for more details.-
base_model_class
¶ alias of
paddlenlp.transformers.ernie_m.modeling.ErnieMModel
-
-
class
ErnieMForSequenceClassification
(ernie_m, num_classes=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel
Ernie-M Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- Parameters
ernie (ErnieMModel) – An instance of
paddlenlp.transformers.ErnieMModel
.num_classes (int, optional) – The number of classes. Default to
2
.dropout (float, optional) – The dropout probability for output of ERNIE-M. If None, use the same value as
hidden_dropout_prob
ofpaddlenlp.transformers.ErnieMModel
instance. Defaults toNone
.
-
forward
(input_ids, position_ids=None, attention_mask=None)[source]¶ - Parameters
input_ids (Tensor) – See
ErnieMModel
.token_type_ids (Tensor, optional) – See
ErnieMModel
.position_ids (Tensor, optional) – See
ErnieMModel
.attention_mask (Tensor, optional) – See
ErnieMModel
.
- 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 ErnieMForSequenceClassification, ErnieMTokenizer tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base') model = ErnieMForSequenceClassification.from_pretrained('ernie-m-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
ErnieMForTokenClassification
(ernie_m, num_classes=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel
ERNIE-M Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- Parameters
ernie (
ErnieMModel
) – An instance ofErnieMModel
.num_classes (int, optional) – The number of classes. Defaults to
2
.dropout (float, optional) – The dropout probability for output of ERNIE-M. If None, use the same value as
hidden_dropout_prob
ofErnieMModel
instanceernie_m
. Defaults toNone
.
-
forward
(input_ids, position_ids=None, attention_mask=None)[source]¶ - Parameters
input_ids (Tensor) – See
ErnieMModel
.token_type_ids (Tensor, optional) – See
ErnieMModel
.position_ids (Tensor, optional) – See
ErnieMModel
.attention_mask (Tensor, optional) – See
ErnieMModel
.
- 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 ErnieMForTokenClassification, ErnieMTokenizer tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base') model = ErnieMForTokenClassification.from_pretrained('ernie-m-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
ErnieMForQuestionAnswering
(ernie_m)[source]¶ Bases:
paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel
Ernie-M 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
ernie (
ErnieMModel
) – An instance ofErnieMModel
.
-
forward
(input_ids, position_ids=None, attention_mask=None)[source]¶ - Parameters
input_ids (Tensor) – See
ErnieMModel
.token_type_ids (Tensor, optional) – See
ErnieMModel
.position_ids (Tensor, optional) – See
ErnieMModel
.attention_mask (Tensor, optional) – See
ErnieMModel
.
- 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 ErnieMForQuestionAnswering, ErnieMTokenizer tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base') model = ErnieMForQuestionAnswering.from_pretrained('ernie-m-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
ErnieMForMultipleChoice
(ernie_m, num_choices=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel
ERNIE-M with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- Parameters
ernie (
ErnieMModel
) – An instance of ErnieMModel.num_choices (int, optional) – The number of choices. Defaults to
2
.dropout (float, optional) – The dropout probability for output of Ernie. If None, use the same value as
hidden_dropout_prob
ofErnieMModel
instanceernie-m
. Defaults to None.
-
forward
(input_ids, position_ids=None, attention_mask=None)[source]¶ The ErnieMForMultipleChoice forward method, overrides the __call__() special method. :param input_ids: See
ErnieMModel
and shape as [batch_size, num_choice, sequence_length]. :type input_ids: Tensor :param position_ids: SeeErnieMModel
and shape as [batch_size, num_choice, sequence_length]. :type position_ids: Tensor, optional :param attention_mask: SeeErnieMModel
and shape as [batch_size, num_choice, sequence_length]. :type attention_mask: list, optional- 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