modeling#
- class ElectraModel(config: ElectraConfig)[source]#
Bases:
ElectraPretrainedModel
The bare Electra 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 (
ElectraConfig
) – An instance of ElectraConfig
- 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, token_type_ids=None, position_ids=None, attention_mask=None, inputs_embeds=None, past_key_values=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=False)[source]#
The ElectraModel 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. 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]
. Defaults toNone
, which means nothing needed to be prevented attention to.inputs_embeds (Tensor, optional) – Instead of passing input_ids you can choose to directly pass an embedded representation. This is useful for use cases such as P-Tuning, where you want more control over how to convert input_ids indices into the embedding space. Its data type should be
float32
and it has a shape of [batch_size, sequence_length, embedding_size].past_key_values (tuple(tuple(Tensor)), optional) – Precomputed key and value hidden states of the attention blocks of each layer. This can be used to speedup auto-regressive decoding for generation tasks or to support use cases such as Prefix-Tuning where vectors are prepended to each attention layer. The length of tuple equals to the number of layers, and each tuple having 2 tensors of shape
(batch_size, num_heads, past_key_values_length, embed_size_per_head)
) 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
QuestionAnsweringModelOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- Returns:
Returns tensor
encoder_outputs
, which is the output at the last layer of the model. Its data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size].- Return type:
Tensor
Example
import paddle from paddlenlp.transformers import ElectraModel, ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraModel.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} output = model(**inputs)
- class ElectraPretrainedModel(*args, **kwargs)[source]#
Bases:
PretrainedModel
An abstract class for pretrained Electra models. It provides Electra 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
ElectraConfig
- base_model_class#
alias of
ElectraModel
- class ElectraForTotalPretraining(config: ElectraConfig)[source]#
Bases:
ElectraPretrainedModel
Electra Model for pretraining tasks.
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig to construct ElectraForTotalPretraining
- get_input_embeddings()[source]#
get input embedding of model
- Returns:
embedding of model
- Return type:
nn.Embedding
- get_output_embeddings()[source]#
To be overwrited for models with output embeddings
- Returns:
the otuput embedding of model
- Return type:
Optional[Embedding]
- forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, raw_input_ids=None, generator_labels=None)[source]#
The ElectraForPretraining forward method, overrides the __call__() special method.
- Parameters:
input_ids (Tensor) – See
ElectraModel
.token_type_ids (Tensor, optional) – See
ElectraModel
.position_ids (Tensor, optional) – See
ElectraModel
.attention_mask (list, optional) – See
ElectraModel
.raw_input_ids (Tensor, optional) – Raw inputs used to get discriminator labels. Its data type should be
int64
and it has a shape of [batch_size, sequence_length].generator_labels (Tensor, optional) – Labels to compute the discriminator inputs. Its data type should be int64 and its shape is [batch_size, sequence_length]. The value for unmasked tokens should be -100 and value for masked tokens should be 0.
- Returns:
Returns tuple (generator_logits, disc_logits, disc_labels, attention_mask).
With the fields:
generator_logits
(Tensor):The scores of Electra Generator. Its data type should be int64 and its shape is [batch_size, sequence_length, vocab_size].
disc_logits
(Tensor):The prediction result of replaced tokens. Its data type should be float32 and if batch_size>1, its shape is [batch_size, sequence_length], if batch_size=1, its shape is [sequence_length].
disc_labels
(Tensor):The labels of electra discriminator. Its data type should be int32, and its shape is [batch_size, sequence_length].
attention_mask
(Tensor):See
ElectraModel
. Its data type should be bool.
- Return type:
tuple
- class ElectraDiscriminator(config: ElectraConfig)[source]#
Bases:
ElectraPretrainedModel
The Electra Discriminator can detect the tokens that are replaced by the Electra Generator.
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig
- forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None, inputs_embeds=None)[source]#
- Parameters:
input_ids (Tensor) – See
ElectraModel
.token_type_ids (Tensor, optional) – See
ElectraModel
.position_ids (Tensor, optional) – See
ElectraModel
.attention_mask (Tensor, optional) – See
ElectraModel
.inputs_embeds (Tensor, optional) – See
ElectraModel
.
- Returns:
Returns tensor
logits
, the prediction result of replaced tokens. Its data type should be float32 and if batch_size>1, its shape is [batch_size, sequence_length], if batch_size=1, its shape is [sequence_length].- Return type:
Tensor
Example
import paddle from paddlenlp.transformers import ElectraDiscriminator, ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraDiscriminator.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
- class ElectraGenerator(config: ElectraConfig)[source]#
Bases:
ElectraPretrainedModel
The Electra Generator will replace some tokens of the given sequence, it is trained as a masked language model.
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig
- 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, inputs_embeds=None, labels=None, output_attentions=False, output_hidden_states=False, return_dict=False)[source]#
- Parameters:
input_ids (Tensor) – See
ElectraModel
.token_type_ids (Tensor, optional) – See
ElectraModel
.position_ids (Tensor, optional) – See
ElectraModel
.attention_mask (Tensor, optional) – See
ElectraModel
.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 tensor
prediction_scores
, the scores of Electra Generator. Its data type should be int64 and its shape is [batch_size, sequence_length, vocab_size].- Return type:
Tensor
Example
import paddle from paddlenlp.transformers import ElectraGenerator, ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraGenerator.from_pretrained('electra-small') 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 ElectraClassificationHead(config: ElectraConfig)[source]#
Bases:
Layer
Perform sentence-level classification tasks.
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig
- forward(features, **kwargs)[source]#
The ElectraClassificationHead forward method, overrides the __call__() special method.
- Parameters:
features (Tensor) – Input sequence, usually the
sequence_output
of electra model. Its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].- Returns:
Returns a tensor of the input text classification logits. Shape as
[batch_size, num_labels]
and dtype as float32.- Return type:
Tensor
- class ElectraForSequenceClassification(config: ElectraConfig)[source]#
Bases:
ElectraPretrainedModel
Electra Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig to construct ElectraForSequenceClassification
- forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, inputs_embeds=None, labels=None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None)[source]#
The ElectraForSequenceClassification forward method, overrides the __call__() special method.
- Parameters:
input_ids (Tensor) – See
ElectraModel
.token_type_ids (Tensor, optional) – See
ElectraModel
.position_ids (Tensor, optional) – See
ElectraModel
.attention_mask (list, optional) – See
ElectraModel
.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 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 ElectraForSequenceClassification from paddlenlp.transformers import ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraForSequenceClassification.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
- class ElectraForTokenClassification(config: ElectraConfig)[source]#
Bases:
ElectraPretrainedModel
Electra Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig to construct ElectraForTokenClassification
- forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, inputs_embeds=None, labels: Tensor | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None)[source]#
The ElectraForTokenClassification forward method, overrides the __call__() special method.
- Parameters:
input_ids (Tensor) – See
ElectraModel
.token_type_ids (Tensor, optional) – See
ElectraModel
.position_ids (Tensor, optional) – See
ElectraModel
.attention_mask (list, optional) – See
ElectraModel
.labels (Tensor of shape
(batch_size, )
, optional) – Labels for computing the multiple choice classification loss. Indices should be in[0, ..., num_choices-1]
wherenum_choices
is the size of the second dimension of the input tensors. (Seeinput_ids
above)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 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 ElectraForTokenClassification from paddlenlp.transformers import ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraForTokenClassification.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
- class ElectraPretrainingCriterion(config: ElectraConfig)[source]#
Bases:
Layer
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig
- forward(generator_prediction_scores, discriminator_prediction_scores, generator_labels, discriminator_labels, attention_mask)[source]#
- Parameters:
generator_prediction_scores (Tensor) – The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length, vocab_size].
discriminator_prediction_scores (Tensor) – The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length] or [sequence length] if batch_size=1.
generator_labels (Tensor) – The labels of the generator, its dimensionality is equal to
generator_prediction_scores
. Its data type should be int64 and its shape is [batch_size, sequence_size, 1].discriminator_labels (Tensor) – The labels of the discriminator, its dimensionality is equal to
discriminator_prediction_scores
. The labels should be numbers between 0 and 1. Its data type should be float32 and its shape is [batch_size, sequence_size] or [sequence length] if batch_size=1.attention_mask (Tensor) – See
ElectraModel
.
- Returns:
The pretraining loss, equals to weighted generator loss plus the weighted discriminator loss. Its data type should be float32 and its shape is [1].
- Return type:
Tensor
- class ElectraForMultipleChoice(config: ElectraConfig)[source]#
Bases:
ElectraPretrainedModel
Electra Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig to construct ElectraForMultipleChoice
- forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, inputs_embeds=None, labels: Tensor | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None)[source]#
The ElectraForMultipleChoice forward method, overrides the __call__() special method.
- Parameters:
input_ids (Tensor) – See
ElectraModel
and shape as [batch_size, num_choice, sequence_length].token_type_ids (Tensor, optional) – See
ElectraModel
and shape as [batch_size, num_choice, sequence_length].position_ids (Tensor, optional) – See
ElectraModel
and shape as [batch_size, num_choice, sequence_length].attention_mask (list, optional) – See
ElectraModel
and shape as [batch_size, num_choice, sequence_length].labels (Tensor of shape
(batch_size, )
, optional) – Labels for computing the multiple choice classification loss. Indices should be in[0, ..., num_choices-1]
wherenum_choices
is the size of the second dimension of the input tensors. (Seeinput_ids
above)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 tensor
reshaped_logits
, a tensor of the multiple choice classification logits. Shape as[batch_size, num_choice]
and dtype asfloat32
.- Return type:
Tensor
Example
import paddle from paddlenlp.transformers import ElectraForMultipleChoice, ElectraTokenizer from paddlenlp.data import Pad, Dict tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraForMultipleChoice.from_pretrained('electra-small', num_choices=2) data = [ { "question": "how do you turn on an ipad screen?", "answer1": "press the volume button.", "answer2": "press the lock button.", "label": 1, }, { "question": "how do you indent something?", "answer1": "leave a space before starting the writing", "answer2": "press the spacebar", "label": 0, }, ] text = [] text_pair = [] for d in data: text.append(d["question"]) text_pair.append(d["answer1"]) text.append(d["question"]) text_pair.append(d["answer2"]) inputs = tokenizer(text, text_pair) batchify_fn = lambda samples, fn=Dict( { "input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id), # input_ids "token_type_ids": Pad( axis=0, pad_val=tokenizer.pad_token_type_id ), # token_type_ids } ): fn(samples) inputs = batchify_fn(inputs) reshaped_logits = model( input_ids=paddle.to_tensor(inputs[0], dtype="int64"), token_type_ids=paddle.to_tensor(inputs[1], dtype="int64"), ) print(reshaped_logits.shape) # [2, 2]
- class ElectraForQuestionAnswering(config: ElectraConfig)[source]#
Bases:
ElectraPretrainedModel
Electra 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 (
ElectraConfig
) – An instance of ElectraConfig used to construct ElectraForQuestionAnswering.
- forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None, inputs_embeds=None, start_positions: Tensor | None = None, end_positions: Tensor | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None)[source]#
The ElectraForQuestionAnswering forward method, overrides the __call__() special method.
- Parameters:
input_ids (Tensor) – See
ElectraModel
.token_type_ids (Tensor, optional) – See
ElectraModel
.position_ids (Tensor, optional) – See
ElectraModel
.attention_mask (list, optional) – See
ElectraModel
.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 ElectraForQuestionAnswering, ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraForQuestionAnswering.from_pretrained('electra-small') 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]
- ElectraForMaskedLM#
alias of
ElectraGenerator
- ElectraForPretraining#
alias of
ElectraForTotalPretraining
- class ErnieHealthForTotalPretraining(config: ElectraConfig)[source]#
Bases:
ElectraForTotalPretraining
ERNIE-Health Model for pretraining task.
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig to construct ElectraForMultipleChoice
- sample_negatives_from_softmax(logits, raw_inputs, use_softmax_sample=True)[source]#
Sample K=5 non-original negative samples for candidate set.
- Returns:
Returns tensor
neg_samples_ids
, a tensor of the negative samples of original inputs. Shape as ` [batch_size, sequence_length, K, vocab_size]` and dtype asint64
.- Return type:
Tensor
- forward(input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, raw_input_ids=None, generator_labels=None, return_dict: bool | None = None)[source]#
The ElectraForPretraining forward method, overrides the __call__() special method.
- Parameters:
input_ids (Tensor) – See
ElectraModel
.token_type_ids (Tensor, optional) – See
ElectraModel
.position_ids (Tensor, optional) – See
ElectraModel
.attention_mask (list, optional) – See
ElectraModel
.raw_input_ids (Tensor, optional) – Raw inputs used to get discriminator labels. Its data type should be
int64
and it has a shape of [batch_size, sequence_length].generator_labels (Tensor, optional) – Labels to compute the discriminator inputs. Its data type should be int64 and its shape is [batch_size, sequence_length]. The value for unmasked tokens should be -100 and value for masked tokens should be 0.
- Returns:
Returns tuple (generator_logits, disc_logits, disc_labels, attention_mask).
With the fields:
generator_logits
(Tensor):The scores of Electra Generator. Its data type should be int64 and its shape is [batch_size, sequence_length, vocab_size].
disc_logits
(Tensor):The prediction result of replaced tokens. Its data type should be float32 and if batch_size>1, its shape is [batch_size, sequence_length], if batch_size=1, its shape is [sequence_length].
disc_labels
(Tensor):The labels of electra discriminator. Its data type should be int32, and its shape is [batch_size, sequence_length].
attention_mask
(Tensor):See
ElectraModel
. Its data type should be bool.
- Return type:
tuple
- class ErnieHealthPretrainingCriterion(config: ElectraConfig)[source]#
Bases:
Layer
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig
- forward(generator_logits, generator_labels, logits_rtd, logits_mts, logits_csp, discriminator_labels, attention_mask)[source]#
- Parameters:
generator_logits (Tensor) – The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length, vocab_size].
generator_labels (Tensor) – The labels of the generator, its dimensionality is equal to
generator_prediction_scores
. Its data type should be int64 and its shape is [batch_size, sequence_size, 1].logits_rtd (Tensor) – The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length] or [sequence length] if batch_size=1.
discriminator_labels (Tensor) – The labels of the discriminator, its dimensionality is equal to
discriminator_prediction_scores
. The labels should be numbers between 0 and 1. Its data type should be float32 and its shape is [batch_size, sequence_size] or [sequence length] if batch_size=1.attention_mask (Tensor) – See
ElectraModel
.
- Returns:
The pretraining loss, equals to weighted generator loss plus the weighted discriminator loss. Its data type should be float32 and its shape is [1].
- Return type:
Tensor
- class ErnieHealthDiscriminator(config: ElectraConfig)[source]#
Bases:
ElectraPretrainedModel
- The Discriminators in ERNIE-Health (https://arxiv.org/abs/2110.07244), including
token-level Replaced Token Detection (RTD) task
token-level Multi-Token Selection (MTS) task
sequence-level Contrastive Sequence Prediction (CSP) task.
- Parameters:
config (
ElectraConfig
) – An instance of ElectraConfig to construct ErnieHealthDiscriminator
- forward(input_ids, candidate_ids, token_type_ids=None, position_ids=None, attention_mask=None)[source]#
- Parameters:
input_ids (Tensor) – See
ElectraModel
.candidate_ids (Tensor) – The candidate indices of input sequence tokens in the vocabulary for MTS task. Its data type should be
int64
and it has a shape of [batch_size, sequence_length].token_type_ids (Tensor, optional) – See
ElectraModel
.position_ids (Tensor, optional) – See
ElectraModel
.attention_mask (Tensor, optional) – See
ElectraModel
.
- Returns:
Returns list of tensors, the prediction results of RTD, MTS and CSP. The logits’ data type should be float32 and if batch_size > 1,
the shape of
logits_rtd
is [batch_size, sequence_length],the shape of
logits_mts
is [batch_size, sequence_length, num_candidate],the shape of
logits_csp
is [batch_size, 128].
If batch_size=1, the shapes are [sequence_length], [sequence_length, num_cadidate], [128], separately.
- Return type:
Tensor