modeling¶
Modeling classes for LayoutLM model.
-
class
LayoutLMModel
(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, layer_norm_eps=1e-12, max_position_embeddings=512, max_2d_position_embeddings=1024, type_vocab_size=16, initializer_range=0.02, pad_token_id=0, pool_act='tanh')[source]¶ Bases:
paddlenlp.transformers.layoutlm.modeling.LayoutLMPretrainedModel
The bare LayoutLM Model 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
vocab_size (int) – Vocabulary size of the LayoutLM model. Defines the number of different tokens that can be represented by the
inputs_ids
passed when calling LayoutLMModel.hidden_size (int) – Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (int) – Number of hidden layers in the Transformer encoder.
num_attention_heads (int) – Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (int) – Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.
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.hidden_dropout_prob (float) – The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (float) – The dropout probability for all fully connected layers in the pooler.
type_vocab_size (int, optional) – The vocabulary size of
token_type_ids
. Defaults to16
.initializer_range (float) –
The standard deviation of the normal initializer. Defaults to 0.02.
Note
A normal_initializer initializes weight matrices as normal distributions. See
LayoutLMPretrainedModel.init_weights()
for how weights are initialized inLayoutLMModel
.pad_token_id (int, optional) – The index of padding token in the token vocabulary. Defaults to
0
.pooled_act (str, optional) – The non-linear activation function in the pooling layer. Defaults to
"tanh"
.
-
resize_position_embeddings
(new_num_position_embeddings)[source]¶ Resizes position embeddings of the model if
new_num_position_embeddings != config["max_position_embeddings"]
.- Parameters
new_num_position_embeddings (
int
) – The number of new position embedding matrix. If position embeddings are learned, increasing the size will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the end.
-
forward
(input_ids=None, bbox=None, token_type_ids=None, position_ids=None, attention_mask=None, output_hidden_states=False)[source]¶ The LayoutLMModel 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.output_hidden_states (bool, optional) – Whether to return the output of each hidden layers. Defaults to
False
.
- 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
-
class
LayoutLMPretrainedModel
(*args, **kwargs)[source]¶ Bases:
paddlenlp.transformers.model_utils.PretrainedModel
-
base_model_class
¶ alias of
paddlenlp.transformers.layoutlm.modeling.LayoutLMModel
-
-
class
LayoutLMForMaskedLM
(layoutlm)[source]¶ Bases:
paddlenlp.transformers.layoutlm.modeling.LayoutLMPretrainedModel
LayoutLM Model with a
masked language modeling
head on top.- Parameters
layoutlm (
LayoutLMModel
) – An instance ofLayoutLMModel
.
-
resize_position_embeddings
(new_num_position_embeddings)[source]¶ Resizes position embeddings of the model if
new_num_position_embeddings != config["max_position_embeddings"]
.- Parameters
new_num_position_embeddings (
int
) – The number of new position embedding matrix. If position embeddings are learned, increasing the size will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the end.
-
forward
(input_ids, bbox=None, token_type_ids=None, position_ids=None, attention_mask=None)[source]¶ - Parameters
input_ids (Tensor) – See
LayoutLMModel
.bbox (Tensor) – See
LayoutLMModel
.token_type_ids (Tensor, optional) – See
LayoutLMModel
.position_ids (Tensor, optional) – See
LayoutLMModel
.attention_mask (Tensor, optional) – See
LayoutLMModel
.
- Returns
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].- Return type
Tensor
Example
import paddle from paddlenlp.transformers import LayoutLMForMaskedLM, LayoutLMTokenizer tokenizer = LayoutLMTokenizer.from_pretrained('layoutlm-base-uncased') model = LayoutLMForMaskedLM.from_pretrained('layoutlm-base-uncased') 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)
-
class
LayoutLMForTokenClassification
(layoutlm, num_classes=2, dropout=None)[source]¶ Bases:
paddlenlp.transformers.layoutlm.modeling.LayoutLMPretrainedModel
LayoutLM Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- Parameters
layoutlm (
LayoutLMModel
) – An instance of LayoutLMModel.num_classes (int, optional) – The number of classes. Defaults to
2
.dropout (float, optional) – The dropout probability for output of LayoutLM. If None, use the same value as
hidden_dropout_prob
ofLayoutLMModel
instancelayoutlm
. Defaults to None.
-
get_input_embeddings
()[source]¶ get input embedding of model
- Returns
embedding of model
- Return type
nn.Embedding
-
resize_position_embeddings
(new_num_position_embeddings)[source]¶ Resizes position embeddings of the model if
new_num_position_embeddings != config["max_position_embeddings"]
.- Parameters
new_num_position_embeddings (
int
) – The number of new position embedding matrix. If position embeddings are learned, increasing the size will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the end.
-
forward
(input_ids, bbox=None, attention_mask=None, token_type_ids=None, position_ids=None, output_hidden_states=False)[source]¶ The LayoutLMForTokenClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
LayoutLMModel
.bbox (Tensor) – See
LayoutLMModel
.attention_mask (list, optional) – See
LayoutLMModel
.token_type_ids (Tensor, optional) – See
LayoutLMModel
.position_ids (Tensor, optional) – See
LayoutLMModel
.output_hidden_states (Tensor, optional) – See
LayoutLMModel
.
- Returns
Returns tensor
logits
, a tensor of the input token classification logits. Shape as[batch_size, sequence_length, num_classes]
and dtype asfloat32
.- Return type
Tensor
Example
import paddle from paddlenlp.transformers import LayoutLMFForTokenClassification from paddlenlp.transformers import LayoutLMFTokenizer tokenizer = LayoutLMFTokenizer.from_pretrained('layoutlm-base-uncased') model = LayoutLMFForTokenClassification.from_pretrained('layoutlm-base-uncased', num_classes=2) 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, 13, 2]
-
class
LayoutLMForSequenceClassification
(layoutlm, num_classes=2)[source]¶ Bases:
paddlenlp.transformers.layoutlm.modeling.LayoutLMPretrainedModel
LayoutLM Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- Parameters
layoutlm (
LayoutLMModel
) – An instance of LayoutLMModel.num_classes (int, optional) – The number of classes. Defaults to
2
.
-
get_input_embeddings
()[source]¶ get input embedding of model
- Returns
embedding of model
- Return type
nn.Embedding
-
resize_position_embeddings
(new_num_position_embeddings)[source]¶ Resizes position embeddings of the model if
new_num_position_embeddings != config["max_position_embeddings"]
.- Parameters
new_num_position_embeddings (
int
) – The number of new position embedding matrix. If position embeddings are learned, increasing the size will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the end.
-
forward
(input_ids, bbox=None, attention_mask=None, token_type_ids=None, position_ids=None, output_hidden_states=False)[source]¶ The LayoutLMForSequenceClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
LayoutLMModel
.bbox (Tensor) – See
LayoutLMModel
.attention_mask (list, optional) – See
LayoutLMModel
.token_type_ids (Tensor, optional) – See
LayoutLMModel
.position_ids (Tensor, optional) – See
LayoutLMModel
.output_hidden_states (Tensor, optional) – See
LayoutLMModel
.
- Returns
Returns tensor
logits
, a tensor of the input text classification logits. Shape as[batch_size, num_classes]
and dtype as float32.- Return type
Tensor
Example
import paddle from paddlenlp.transformers import LayoutLMForSequenceClassification from paddlenlp.transformers import LayoutLMTokenizer tokenizer = LayoutLMTokenizer.from_pretrained('layoutlm-base-uncased') model = LayoutLMForSequenceClassification.from_pretrained('layoutlm-base-uncased', num_classes=2) 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, 2]