modeling¶
-
class
ErnieMModel
(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[源代码]¶ 基类:
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.
- 参数
config (
ErnieMConfig
) -- An instance of ErnieMConfig used to construct ErnieMModel.
-
forward
(input_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, past_key_values: Optional[Tuple[Tuple[paddle.Tensor]]] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = 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].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.inputs_embeds (Tensor, optional) -- If you want to control how to convert
inputs_ids
indices into associated vectors, you can pass an embedded representation directly instead of passinginputs_ids
.past_key_values (tuple(tuple(Tensor)), optional) -- The length of tuple equals to the number of layers, and each inner tuple haves 4 tensors of shape
(batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) which contains precomputed key and value hidden states of the attention blocks. Ifpast_key_values
are used, the user can optionally input only the lastinput_ids
(those that don't have their past key value states given to this model) of shape(batch_size, 1)
instead of allinput_ids
of shape(batch_size, sequence_length)
.use_cache (
bool
, optional) -- If set toTrue
,past_key_values
key value states are returned. Defaults toNone
.output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) -- Whether to return a
ModelOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
BaseModelOutputWithPoolingAndCrossAttentions
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofBaseModelOutputWithPoolingAndCrossAttentions
. tuple: 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].
示例
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
(*args, **kwargs)[源代码]¶ 基类:
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.-
config_class
¶ alias of
paddlenlp.transformers.ernie_m.configuration.ErnieMConfig
-
base_model_class
¶ alias of
paddlenlp.transformers.ernie_m.modeling.ErnieMModel
-
-
class
ErnieMForSequenceClassification
(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[源代码]¶ 基类:
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.
- 参数
config (
ErnieMConfig
) -- An instance of ErnieMConfig used to construct ErnieMForSequenceClassification.
-
forward
(input_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[源代码]¶ - 参数
input_ids (Tensor) -- See
ErnieMModel
.position_ids (Tensor, optional) -- See
ErnieMModel
.attention_mask (Tensor, optional) -- See
ErnieMModel
.labels (Tensor of shape
(batch_size,)
, optional) -- Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., num_labels - 1]
. Ifnum_labels == 1
a regression loss is computed (Mean-Square loss), Ifnum_labels > 1
a classification loss is computed (Cross-Entropy).inputs_embeds (Tensor, optional) -- If you want to control how to convert
inputs_ids
indices into associated vectors, you can pass an embedded representation directly instead of passinginputs_ids
.output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) -- Whether to return a
SequenceClassifierOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
SequenceClassifierOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofSequenceClassifierOutput
.
示例
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
(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[源代码]¶ 基类:
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.
- 参数
config (
ErnieMConfig
) -- An instance of ErnieMConfig used to construct ErnieMForTokenClassification.
-
forward
(input_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[源代码]¶ - 参数
input_ids (Tensor) -- See
ErnieMModel
.position_ids (Tensor, optional) -- See
ErnieMModel
.attention_mask (Tensor, optional) -- See
ErnieMModel
.labels (Tensor of shape
(batch_size, sequence_length)
, optional) -- Labels for computing the token classification loss. Indices should be in[0, ..., num_labels - 1]
.inputs_embeds (Tensor, optional) -- If you want to control how to convert
inputs_ids
indices into associated vectors, you can pass an embedded representation directly instead of passinginputs_ids
.output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) -- Whether to return a
TokenClassifierOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
Returns tensor
logits
, a tensor of the input token classification logits. Shape as[batch_size, sequence_length, num_labels]
and dtype asfloat32
.- 返回类型
Tensor
示例
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
(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[源代码]¶ 基类:
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.- 参数
config (
ErnieMConfig
) -- An instance of ErnieMConfig used to construct ErnieMForQuestionAnswering.
-
forward
(input_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, start_positions: Optional[paddle.Tensor] = None, end_positions: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[源代码]¶ - 参数
input_ids (Tensor) -- See
ErnieMModel
.position_ids (Tensor, optional) -- See
ErnieMModel
.attention_mask (Tensor, optional) -- See
ErnieMModel
.start_positions (Tensor of shape
(batch_size,)
, optional) -- Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length
). Position outside of the sequence are not taken into account for computing the loss.end_positions (Tensor of shape
(batch_size,)
, optional) -- Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length
). Position outside of the sequence are not taken into account for computing the loss.inputs_embeds (Tensor, optional) -- If you want to control how to convert
inputs_ids
indices into associated vectors, you can pass an embedded representation directly instead of passinginputs_ids
.output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) -- Whether to return a
QuestionAnsweringModelOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 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 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
(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[源代码]¶ 基类:
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.
- 参数
config (
ErnieMConfig
) -- An instance of ErnieMConfig used to construct ErnieMForMultipleChoice.
-
forward
(input_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[源代码]¶ 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 :param labels: Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,num_choices-1]` where
num_choices
is the size of the second dimension of the input tensors. (Seeinput_ids
above)- 参数
inputs_embeds (Tensor, optional) -- If you want to control how to convert
inputs_ids
indices into associated vectors, you can pass an embedded representation directly instead of passinginputs_ids
.output_hidden_states (bool, optional) -- Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) -- Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) -- Whether to return a
MultipleChoiceModelOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- 返回
An instance of
MultipleChoiceModelOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofMultipleChoiceModelOutput
.
-
class
UIEM
(config: paddlenlp.transformers.ernie_m.configuration.ErnieMConfig)[源代码]¶ 基类:
paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel
Ernie-M Model with two linear layer on top of the hidden-states output to compute
start_prob
andend_prob
, designed for Universal Information Extraction.- 参数
config (
ErnieMConfig
) -- An instance of ErnieMConfig used to construct UIEM.
-
forward
(input_ids, position_ids=None, attention_mask=None)[源代码]¶ - 参数
input_ids (Tensor) -- See
ErnieMModel
.position_ids (Tensor, optional) -- See
ErnieMModel
.attention_mask (Tensor, optional) -- See
ErnieMModel
.
示例
import paddle from paddlenlp.transformers import UIEM, ErnieMTokenizer tokenizer = ErnieMTokenizer.from_pretrained('uie-m-base') model = UIEM.from_pretrained('uie-m-base') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} start_prob, end_prob = model(**inputs)