modeling¶
-
class
ErnieModel
(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=512, type_vocab_size=2, initializer_range=0.02, pad_token_id=0, task_type_vocab_size=3, task_id=0, use_task_id=False)[源代码]¶ 基类:
paddlenlp.transformers.ernie.modeling.ErniePretrainedModel
The bare ERNIE 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.
- 参数
vocab_size (int) -- Vocabulary size of
inputs_ids
inErnieModel
. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingErnieModel
.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
.注解
A normal_initializer initializes weight matrices as normal distributions. See
ErniePretrainedModel._init_weights()
for how weights are initialized inErnieModel
.pad_token_id (int, optional) -- The index of padding token in the token vocabulary. Defaults to
0
.
-
forward
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None, task_type_ids=None)[源代码]¶ - 参数
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].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. 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]. We use whole-word-mask in ERNIE, so the whole word will have the same value. For example, "使用" as a word, "使" and "用" will have the same value. Defaults toNone
, which means nothing needed to be prevented attention to.
- 返回
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].
- 返回类型
tuple
示例
import paddle from paddlenlp.transformers import ErnieModel, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieModel.from_pretrained('ernie-1.0') 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
ErniePretrainedModel
(name_scope=None, dtype='float32')[源代码]¶ 基类:
paddlenlp.transformers.model_utils.PretrainedModel
An abstract class for pretrained ERNIE models. It provides ERNIE 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
¶
-
-
class
ErnieForSequenceClassification
(ernie, num_classes=2, dropout=None)[源代码]¶ 基类:
paddlenlp.transformers.ernie.modeling.ErniePretrainedModel
Ernie Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- 参数
ernie (ErnieModel) -- An instance of
paddlenlp.transformers.ErnieModel
.num_classes (int, optional) -- The number of classes. Default to
2
.dropout (float, optional) -- The dropout probability for output of ERNIE. If None, use the same value as
hidden_dropout_prob
ofpaddlenlp.transformers.ErnieModel
instance. Defaults toNone
.
-
forward
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]¶ - 参数
input_ids (Tensor) -- See
ErnieModel
.token_type_ids (Tensor, optional) -- See
ErnieModel
.position_ids (Tensor, optional) -- See
ErnieModel
.attention_mask (Tensor, optional) -- See
ErnieModel
.
- 返回
Returns tensor
logits
, a tensor of the input text classification logits. Shape as[batch_size, num_classes]
and dtype as float32.- 返回类型
Tensor
示例
import paddle from paddlenlp.transformers import ErnieForSequenceClassification, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieForSequenceClassification.from_pretrained('ernie-1.0') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
-
class
ErnieForTokenClassification
(ernie, num_classes=2, dropout=None)[源代码]¶ 基类:
paddlenlp.transformers.ernie.modeling.ErniePretrainedModel
ERNIE Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- 参数
ernie (
ErnieModel
) -- An instance ofErnieModel
.num_classes (int, optional) -- The number of classes. Defaults to
2
.dropout (float, optional) -- The dropout probability for output of ERNIE. If None, use the same value as
hidden_dropout_prob
ofErnieModel
instanceernie
. Defaults toNone
.
-
forward
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]¶ - 参数
input_ids (Tensor) -- See
ErnieModel
.token_type_ids (Tensor, optional) -- See
ErnieModel
.position_ids (Tensor, optional) -- See
ErnieModel
.attention_mask (Tensor, optional) -- See
ErnieModel
.
- 返回
Returns tensor
logits
, a tensor of the input token classification logits. Shape as[batch_size, sequence_length, num_classes]
and dtype asfloat32
.- 返回类型
Tensor
示例
import paddle from paddlenlp.transformers import ErnieForTokenClassification, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieForTokenClassification.from_pretrained('ernie-1.0') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
-
class
ErnieForQuestionAnswering
(ernie)[源代码]¶ 基类:
paddlenlp.transformers.ernie.modeling.ErniePretrainedModel
Ernie 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.- 参数
ernie (
ErnieModel
) -- An instance ofErnieModel
.
-
forward
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]¶ - 参数
input_ids (Tensor) -- See
ErnieModel
.token_type_ids (Tensor, optional) -- See
ErnieModel
.position_ids (Tensor, optional) -- See
ErnieModel
.attention_mask (Tensor, optional) -- See
ErnieModel
.
- 返回
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].
- 返回类型
tuple
示例
import paddle from paddlenlp.transformers import ErnieForQuestionAnswering, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieForQuestionAnswering.from_pretrained('ernie-1.0') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
-
class
ErnieForPretraining
(ernie)[源代码]¶ 基类:
paddlenlp.transformers.ernie.modeling.ErniePretrainedModel
Ernie Model with a
masked language modeling
head and asentence order prediction
head on top.-
forward
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None, masked_positions=None)[源代码]¶ - 参数
input_ids (Tensor) -- See
ErnieModel
.token_type_ids (Tensor, optional) -- See
ErnieModel
.position_ids (Tensor, optional) -- See
ErnieModel
.attention_mask (Tensor, optional) -- See
ErnieModel
.
- 返回
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].
- 返回类型
tuple
-
-
class
ErniePretrainingCriterion
(with_nsp_loss=True)[源代码]¶ 基类:
paddle.fluid.dygraph.layers.Layer
The loss output of Ernie Model during the pretraining: a
masked language modeling
head and anext sentence prediction (classification)
head.-
forward
(prediction_scores, seq_relationship_score, masked_lm_labels, next_sentence_labels=None)[源代码]¶ - 参数
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]
masked_lm_labels (Tensor) -- The labels of the masked language modeling, its dimensionality is equal to
prediction_scores
. Its data type should be int64. Ifmasked_positions
is None, its shape is [batch_size, sequence_length, 1]. Otherwise, its shape is [batch_size, mask_token_num, 1]next_sentence_labels (Tensor) -- The labels of the next sentence prediction task, the dimensionality of
next_sentence_labels
is equal toseq_relation_labels
. Its data type should be int64 and its shape is [batch_size, 1]
- 返回
The pretraining loss, equals to the sum of
masked_lm_loss
plus the mean ofnext_sentence_loss
. Its data type should be float32 and its shape is [1].- 返回类型
Tensor
-
-
class
ErnieForMaskedLM
(ernie)[源代码]¶ 基类:
paddlenlp.transformers.ernie.modeling.ErniePretrainedModel
Ernie Model with a
masked language modeling
head on top.- 参数
ernie (
ErnieModel
) -- An instance ofErnieModel
.
-
forward
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]¶ - 参数
input_ids (Tensor) -- See
ErnieModel
.token_type_ids (Tensor, optional) -- See
ErnieModel
.position_ids (Tensor, optional) -- See
ErnieModel
.attention_mask (Tensor, optional) -- See
ErnieModel
.
- 返回
Returns tensor
prediction_scores
, The scores of masked token prediction. Its data type should be float32 and shape is [batch_size, sequence_length, vocab_size].- 返回类型
Tensor
示例
import paddle from paddlenlp.transformers import ErnieForMaskedLM, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieForMaskedLM.from_pretrained('ernie-1.0') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) print(logits.shape) # [1, 17, 18000]
-
class
ErnieForMultipleChoice
(ernie, num_choices=2, dropout=None)[源代码]¶ 基类:
paddlenlp.transformers.ernie.modeling.ErniePretrainedModel
Ernie Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- 参数
ernie (
ErnieModel
) -- An instance of ErnieModel.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
ofErnieModel
instanceernie
. Defaults to None.
-
forward
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]¶ The ErnieForMultipleChoice forward method, overrides the __call__() special method.
- 参数
input_ids (Tensor) -- See
ErnieModel
and shape as [batch_size, num_choice, sequence_length].token_type_ids (Tensor, optional) -- See
ErnieModel
and shape as [batch_size, num_choice, sequence_length].position_ids (Tensor, optional) -- See
ErnieModel
and shape as [batch_size, num_choice, sequence_length].attention_mask (list, optional) -- See
ErnieModel
and shape as [batch_size, num_choice, sequence_length].
- 返回
Returns tensor
reshaped_logits
, a tensor of the multiple choice classification logits. Shape as[batch_size, num_choice]
and dtype asfloat32
.- 返回类型
Tensor