modeling¶
-
class
ErnieGramModel
(config: paddlenlp.transformers.ernie_gram.configuration.ErnieGramConfig)[source]¶ Bases:
paddlenlp.transformers.ernie_gram.modeling.ErnieGramPretrainedModel
The bare ERNIE-Gram 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
config (
ErnieGramConfig
) – An instance of ErnieGramConfig used to construct ErnieGramModel.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_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)[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].token_type_ids (Tensor, optional) –
Segment token indices to indicate first and second portions of the inputs. Indices can be either 0 or 1:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
It’s data type should be
int64
and has a shape of [batch_size, sequence_length]. Defaults to None, which means no segment embeddings is added to token embeddings.position_ids (Tensor, optional) – Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, config.max_position_embeddings - 1]
. Defaults toNone
. Shape as(batch_sie, num_tokens)
and dtype asint32
orint64
.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.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
.
- 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 ErnieGramModel, ErnieGramTokenizer tokenizer = ErnieGramTokenizer.from_pretrained('ernie-gram-zh') model = ErnieGramModel.from_pretrained('ernie-gram-zh) inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} sequence_output, pooled_output = model(**inputs)
-
class
ErnieGramPretrainedModel
(*args, **kwargs)[source]¶ Bases:
paddlenlp.transformers.model_utils.PretrainedModel
An abstract class for pretrained ERNIE-Gram models. It provides ERNIE-Gram related
model_config_file
,resource_files_names
,pretrained_resource_files_map
,pretrained_init_configuration
,base_model_prefix
for downloading and loading pretrained models. SeePretrainedModel
for more details.-
config_class
¶ alias of
paddlenlp.transformers.ernie_gram.configuration.ErnieGramConfig
-
base_model_class
¶ alias of
paddlenlp.transformers.ernie_gram.modeling.ErnieGramModel
-
-
class
ErnieGramForSequenceClassification
(config: paddlenlp.transformers.ernie_gram.configuration.ErnieGramConfig)[source]¶ Bases:
paddlenlp.transformers.ernie_gram.modeling.ErnieGramPretrainedModel
ERNIE-Gram Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- Parameters
config (
ErnieGramConfig
) – An instance of ErnieGramConfig used to construct ErnieGramForSequenceClassification.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ - Parameters
input_ids (Tensor) – See
ErnieGramModel
.token_type_ids (Tensor, optional) – See
ErnieGramModel
.position_ids (Tensor, optional) – See
ErnieGramModel
.attention_mask (Tensor, optional) – See
BertModel
.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) – See
ErnieGramModel
.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
.
- Returns
Returns tensor
logits
, a tensor of the input text classification logits. Shape as[batch_size, num_labels]
and dtype as float32.- Return type
Tensor
Example
import paddle from paddlenlp.transformers import ErnieGramForSequenceClassification, ErnieGramTokenizer tokenizer = ErnieGramTokenizer.from_pretrained('ernie-gram-zh') model = ErnieGramForSequenceClassification.from_pretrained('ernie-gram-zh') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
-
class
ErnieGramForTokenClassification
(config: paddlenlp.transformers.ernie_gram.configuration.ErnieGramConfig)[source]¶ Bases:
paddlenlp.transformers.ernie_gram.modeling.ErnieGramPretrainedModel
ERNIE-Gram Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- Parameters
config (
ErnieGramConfig
) – An instance of ErnieGramConfig used to construct ErnieGramForTokenClassification.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ - Parameters
input_ids (Tensor) – See
ErnieGramModel
.token_type_ids (Tensor, optional) – See
ErnieGramModel
.position_ids (Tensor, optional) – See
ErnieGramModel
.attention_mask (Tensor, optional) – See
ErnieGramModel
.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) – See
ErnieGramModel
.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
.
- Returns
Returns tensor
logits
, a tensor of the input token classification logits. Shape as[batch_size, sequence_length, num_labels]
and dtype asfloat32
.- Return type
Tensor
Example
import paddle from paddlenlp.transformers import ErnieGramForTokenClassification, ErnieGramTokenizer tokenizer = ErnieGramTokenizer.from_pretrained('ernie-gram-zh') model = ErnieGramForTokenClassification.from_pretrained('ernie-gram-zh') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
-
class
ErnieGramForQuestionAnswering
(config: paddlenlp.transformers.ernie_gram.configuration.ErnieGramConfig)[source]¶ Bases:
paddlenlp.transformers.ernie_gram.modeling.ErnieGramPretrainedModel
ERNIE-Gram 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
config (
ErnieGramConfig
) – An instance of ErnieGramConfig used to construct ErnieGramForQuestionAnswering.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, position_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, start_positions: Optional[paddle.Tensor] = None, end_positions: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ - Parameters
input_ids (Tensor) – See
ErnieGramModel
.token_type_ids (Tensor, optional) – See
ErnieGramModel
.position_ids (Tensor, optional) – See
ErnieGramModel
.attention_mask (Tensor, optional) – See
ErnieGramModel
.inputs_embeds (Tensor, optional) – See
ErnieGramModel
.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.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
.
- 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 ErnieGramForQuestionAnswering, ErnieGramTokenizer tokenizer = ErnieGramTokenizer.from_pretrained('ernie-gram-zh') model = ErnieGramForQuestionAnswering.from_pretrained('ernie-gram-zh') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)