# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple
import paddle
import paddle.nn as nn
from paddle import Tensor
from paddlenlp.utils.env import CONFIG_NAME
from .. import PretrainedModel, register_base_model
from ..model_outputs import (
BaseModelOutputWithPoolingAndCrossAttentions,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
tuple_output,
)
from .configuration import (
ERNIE_M_PRETRAINED_INIT_CONFIGURATION,
ERNIE_M_PRETRAINED_RESOURCE_FILES_MAP,
ErnieMConfig,
)
__all__ = [
"ErnieMModel",
"ErnieMPretrainedModel",
"ErnieMForSequenceClassification",
"ErnieMForTokenClassification",
"ErnieMForQuestionAnswering",
"ErnieMForMultipleChoice",
"UIEM",
]
class ErnieMEmbeddings(nn.Layer):
r"""
Include embeddings from word, position.
"""
def __init__(self, config: ErnieMConfig):
super(ErnieMEmbeddings, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(
self,
input_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
past_key_values_length: int = 0,
):
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if position_ids is None:
input_shape = paddle.shape(inputs_embeds)[:-1]
# maybe need use shape op to unify static graph and dynamic graph
ones = paddle.ones(input_shape, dtype="int64")
seq_length = paddle.cumsum(ones, axis=1)
position_ids = seq_length - ones
if past_key_values_length > 0:
position_ids = position_ids + past_key_values_length
position_ids.stop_gradient = True
position_ids += 2
position_embeddings = self.position_embeddings(position_ids)
embeddings = inputs_embeds + position_embeddings
embeddings = self.layer_norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class ErnieMPooler(nn.Layer):
def __init__(self, config: ErnieMConfig):
super(ErnieMPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
[文档]class ErnieMPretrainedModel(PretrainedModel):
r"""
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 to :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
"""
model_config_file = CONFIG_NAME
config_class = ErnieMConfig
resource_files_names = {"model_state": "model_state.pdparams"}
pretrained_init_configuration = ERNIE_M_PRETRAINED_INIT_CONFIGURATION
pretrained_resource_files_map = ERNIE_M_PRETRAINED_RESOURCE_FILES_MAP
base_model_prefix = "ernie_m"
def _init_weights(self, layer):
"""Initialization hook"""
if isinstance(layer, (nn.Linear, nn.Embedding)):
# only support dygraph, use truncated_normal and make it inplace
# and configurable later
if isinstance(layer.weight, paddle.Tensor):
layer.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.weight.shape,
)
)
[文档]@register_base_model
class ErnieMModel(ErnieMPretrainedModel):
r"""
The bare ERNIE-M Model transformer outputting raw hidden-states.
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
Refer to the superclass documentation for the generic methods.
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
/docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use it as a regular Paddle Layer
and refer to the Paddle documentation for all matter related to general usage and behavior.
Args:
config (:class:`ErnieMConfig`):
An instance of ErnieMConfig used to construct ErnieMModel.
"""
def __init__(self, config: ErnieMConfig):
super(ErnieMModel, self).__init__(config)
self.pad_token_id = config.pad_token_id
self.initializer_range = config.initializer_range
self.embeddings = ErnieMEmbeddings(config)
encoder_layer = nn.TransformerEncoderLayer(
config.hidden_size,
config.num_attention_heads,
dim_feedforward=4 * config.hidden_size,
dropout=config.hidden_dropout_prob,
activation=config.hidden_act,
attn_dropout=config.attention_probs_dropout_prob,
act_dropout=0,
normalize_before=False,
)
self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
self.pooler = ErnieMPooler(config)
[文档] def forward(
self,
input_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
past_key_values: Optional[Tuple[Tuple[Tensor]]] = None,
use_cache: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
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 to `None`.
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 have `False` values and the others have `True` values.
When the data type is int, the `masked` tokens have `0` values and the others have `1` values.
When the data type is float, the `masked` tokens have `-INF` values and the others have `0` 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 to `None`, 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 passing `inputs_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.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, optional):
If set to `True`, `past_key_values` key value states are returned.
Defaults to `None`.
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 :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output
will be a tuple of tensors. Defaults to `False`.
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions` if
`return_dict=True`. Otherwise it returns a tuple of tensors corresponding
to ordered and not None (depending on the input arguments) fields of
:class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions`.
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].
Example:
.. code-block::
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)
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time.")
# init the default bool value
output_attentions = output_attentions if output_attentions is not None else False
output_hidden_states = output_hidden_states if output_hidden_states is not None else False
return_dict = return_dict if return_dict is not None else False
use_cache = use_cache if use_cache is not None else False
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
if attention_mask is None:
# TODO(linjieccc): fix attention mask after uie-m related models updated
attention_mask = paddle.unsqueeze(
(input_ids == 0).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]
)
if past_key_values is not None:
batch_size = past_key_values[0][0].shape[0]
past_mask = paddle.zeros([batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype)
attention_mask = paddle.concat([past_mask, attention_mask], axis=-1)
# For 2D attention_mask from tokenizer
elif attention_mask.ndim == 2:
attention_mask = paddle.unsqueeze(attention_mask, axis=[1, 2]).astype(paddle.get_default_dtype())
attention_mask = (1.0 - attention_mask) * -1e4
attention_mask.stop_gradient = True
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
self.encoder._use_cache = use_cache # To be consistent with HF
encoder_outputs = self.encoder(
embedding_output,
attention_mask,
cache=past_key_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if isinstance(encoder_outputs, type(embedding_output)):
sequence_output = encoder_outputs
pooled_output = self.pooler(sequence_output)
return (sequence_output, pooled_output)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
[文档]class ErnieMForSequenceClassification(ErnieMPretrainedModel):
r"""
Ernie-M Model with a linear layer on top of the output layer,
designed for sequence classification/regression tasks like GLUE tasks.
Args:
config (:class:`ErnieMConfig`):
An instance of ErnieMConfig used to construct ErnieMForSequenceClassification.
"""
def __init__(self, config: ErnieMConfig):
super(ErnieMForSequenceClassification, self).__init__(config)
self.ernie_m = ErnieMModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
[文档] def forward(
self,
input_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
labels: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (Tensor):
See :class:`ErnieMModel`.
position_ids (Tensor, optional):
See :class:`ErnieMModel`.
attention_mask (Tensor, optional):
See :class:`ErnieMModel`.
labels (Tensor of shape `(batch_size,)`, optional):
Labels for computing the sequence classification/regression loss.
Indices should be in `[0, ..., num_labels - 1]`. If `num_labels == 1`
a regression loss is computed (Mean-Square loss), If `num_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 passing `inputs_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 :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` object. If
`False`, the output will be a tuple of tensors. Defaults to `False`.
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` if `return_dict=True`.
Otherwise it returns a tuple of tensors corresponding to ordered and
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput`.
Example:
.. code-block::
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)
"""
outputs = self.ernie_m(
input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = self.dropout(outputs[1])
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.num_labels == 1:
loss_fct = paddle.nn.MSELoss()
loss = loss_fct(logits, labels)
elif labels.dtype == paddle.int64 or labels.dtype == paddle.int32:
loss_fct = paddle.nn.CrossEntropyLoss()
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
else:
loss_fct = paddle.nn.BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return tuple_output(output, loss)
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[文档]class ErnieMForQuestionAnswering(ErnieMPretrainedModel):
"""
Ernie-M Model with a linear layer on top of the hidden-states
output to compute `span_start_logits` and `span_end_logits`,
designed for question-answering tasks like SQuAD.
Args:
config (:class:`ErnieMConfig`):
An instance of ErnieMConfig used to construct ErnieMForQuestionAnswering.
"""
def __init__(self, config: ErnieMConfig):
super(ErnieMForQuestionAnswering, self).__init__(config)
self.ernie_m = ErnieMModel(config)
self.classifier = nn.Linear(config.hidden_size, 2)
[文档] def forward(
self,
input_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
start_positions: Optional[Tensor] = None,
end_positions: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (Tensor):
See :class:`ErnieMModel`.
position_ids (Tensor, optional):
See :class:`ErnieMModel`.
attention_mask (Tensor, optional):
See :class:`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 passing `inputs_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 :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` object. If
`False`, the output will be a tuple of tensors. Defaults to `False`.
Returns:
tuple: 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].
Example:
.. code-block::
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)
"""
outputs = self.ernie_m(
input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.classifier(outputs[0])
logits = paddle.transpose(logits, perm=[2, 0, 1])
start_logits, end_logits = paddle.unstack(x=logits, axis=0)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if start_positions.ndim > 1:
start_positions = start_positions.squeeze(-1)
if start_positions.ndim > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = paddle.shape(start_logits)[1]
start_positions = start_positions.clip(0, ignored_index)
end_positions = end_positions.clip(0, ignored_index)
loss_fct = paddle.nn.CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return tuple_output(output, total_loss)
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[文档]class ErnieMForTokenClassification(ErnieMPretrainedModel):
r"""
ERNIE-M Model with a linear layer on top of the hidden-states output layer,
designed for token classification tasks like NER tasks.
Args:
config (:class:`ErnieMConfig`):
An instance of ErnieMConfig used to construct ErnieMForTokenClassification.
"""
def __init__(self, config: ErnieMConfig):
super(ErnieMForTokenClassification, self).__init__(config)
self.ernie_m = ErnieMModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
[文档] def forward(
self,
input_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
labels: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (Tensor):
See :class:`ErnieMModel`.
position_ids (Tensor, optional):
See :class:`ErnieMModel`.
attention_mask (Tensor, optional):
See :class:`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 passing `inputs_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 :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` object. If
`False`, the output will be a tuple of tensors. Defaults to `False`.
Returns:
Tensor: Returns tensor `logits`, a tensor of the input token classification logits.
Shape as `[batch_size, sequence_length, num_labels]` and dtype as `float32`.
Example:
.. code-block::
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)
"""
outputs = self.ernie_m(
input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = self.dropout(outputs[0])
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = paddle.nn.CrossEntropyLoss()
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
if not return_dict:
output = (logits,) + outputs[2:]
return tuple_output(output, loss)
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[文档]class UIEM(ErnieMPretrainedModel):
"""
Ernie-M Model with two linear layer on top of the hidden-states
output to compute `start_prob` and `end_prob`,
designed for Universal Information Extraction.
Args:
config (:class:`ErnieMConfig`):
An instance of ErnieMConfig used to construct UIEM.
"""
def __init__(self, config: ErnieMConfig):
super(UIEM, self).__init__(config)
self.ernie_m = ErnieMModel(config)
self.linear_start = paddle.nn.Linear(config.hidden_size, 1)
self.linear_end = paddle.nn.Linear(config.hidden_size, 1)
self.sigmoid = nn.Sigmoid()
[文档] def forward(self, input_ids, position_ids=None, attention_mask=None):
r"""
Args:
input_ids (Tensor):
See :class:`ErnieMModel`.
position_ids (Tensor, optional):
See :class:`ErnieMModel`.
attention_mask (Tensor, optional):
See :class:`ErnieMModel`.
Example:
.. code-block::
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)
"""
sequence_output, _ = self.ernie_m(
input_ids=input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
)
start_logits = self.linear_start(sequence_output)
start_logits = paddle.squeeze(start_logits, -1)
start_prob = self.sigmoid(start_logits)
end_logits = self.linear_end(sequence_output)
end_logits = paddle.squeeze(end_logits, -1)
end_prob = self.sigmoid(end_logits)
# TODO: add return dict support
return start_prob, end_prob