wehub-resource-sync
663 lines
28 KiB
Python
663 lines
28 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2021 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Modeling classes for LayoutLM model."""
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import paddle
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import paddle.nn as nn
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from paddle.nn import Layer
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from paddlenlp.utils.log import logger
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from ...layers import Linear as TransposedLinear
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from .. import PretrainedModel, register_base_model
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from .configuration import (
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LAYOUTLM_PRETRAINED_INIT_CONFIGURATION,
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LAYOUTLM_PRETRAINED_RESOURCE_FILES_MAP,
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LayoutLMConfig,
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)
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__all__ = [
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"LayoutLMModel",
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"LayoutLMPretrainedModel",
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"LayoutLMForMaskedLM",
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"LayoutLMForTokenClassification",
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"LayoutLMForSequenceClassification",
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]
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class LayoutLMPooler(Layer):
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def __init__(self, config: LayoutLMConfig):
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super(LayoutLMPooler, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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self.pool_act = config.pool_act
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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if self.pool_act == "tanh":
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class LayoutLMEmbeddings(Layer):
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"""
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Include embeddings from word, position and token_type embeddings
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"""
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def __init__(self, config: LayoutLMConfig):
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super(LayoutLMEmbeddings, self).__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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# gry add for layoutlm
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self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
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self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
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self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
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self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
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# end of gry add for layoutlm
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# self.token_type_embeddings = nn.Embedding(type_vocab_size, hidden_size, padding_idx=pad_token_id)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.register_buffer(
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"position_ids", paddle.arange(config.max_position_embeddings, dtype="int64").expand((1, -1))
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)
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def forward(self, input_ids, bbox=None, token_type_ids=None, position_ids=None):
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# input_shape = input_ids.size()
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# seq_length = input_shape[1]
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if position_ids is None:
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ones = paddle.ones_like(input_ids, dtype="int64")
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seq_length = paddle.cumsum(ones, axis=-1)
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position_ids = seq_length - ones
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position_ids.stop_gradient = True
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if token_type_ids is None:
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token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
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word_embeddings = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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# gry add
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try:
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left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
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upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
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right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
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lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
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except IndexError as e:
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raise IndexError("The :obj:`bbox`coordinate values should be within 0-1000 range.") from e
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h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
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w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
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# end of gry add
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = (
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word_embeddings
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+ position_embeddings
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+ left_position_embeddings
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+ upper_position_embeddings
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+ right_position_embeddings
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+ lower_position_embeddings
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+ h_position_embeddings
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+ w_position_embeddings
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+ token_type_embeddings
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)
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embeddings = self.layer_norm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class LayoutLMPretrainedModel(PretrainedModel):
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config_class = LayoutLMConfig
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pretrained_init_configuration = LAYOUTLM_PRETRAINED_INIT_CONFIGURATION
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pretrained_resource_files_map = LAYOUTLM_PRETRAINED_RESOURCE_FILES_MAP
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base_model_prefix = "layoutlm"
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def _init_weights(self, layer):
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"""Initialization hook"""
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if isinstance(layer, (nn.Linear, nn.Embedding)):
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# In the dygraph mode, use the `set_value` to reset the parameter directly,
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# and reset the `state_dict` to update parameter in static mode.
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if isinstance(layer.weight, paddle.Tensor):
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layer.weight.set_value(
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paddle.tensor.normal(
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mean=0.0,
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std=self.config.initializer_range,
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shape=layer.weight.shape,
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)
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)
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elif isinstance(layer, nn.LayerNorm):
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layer._epsilon = 1e-12
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@register_base_model
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class LayoutLMModel(LayoutLMPretrainedModel):
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"""
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The bare LayoutLM Model outputting raw hidden-states.
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This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
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Refer to the superclass documentation for the generic methods.
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This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
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/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
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and refer to the Paddle documentation for all matter related to general usage and behavior.
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Args:
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vocab_size (int):
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Vocabulary size of the LayoutLM model. Defines the number of different tokens that can
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be represented by the `inputs_ids` passed when calling LayoutLMModel.
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hidden_size (int):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (int):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (int):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (int):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (str, optional):
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The non-linear activation function in the feed-forward layer.
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``"gelu"``, ``"relu"`` and any other paddle supported activation functions
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are supported.
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hidden_dropout_prob (float):
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The dropout probability for all fully connected layers in the embeddings and encoder.
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attention_probs_dropout_prob (float):
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The dropout probability for all fully connected layers in the pooler.
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type_vocab_size (int, optional):
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The vocabulary size of `token_type_ids`.
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Defaults to `16`.
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initializer_range (float):
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The standard deviation of the normal initializer.
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Defaults to 0.02.
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.. note::
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A normal_initializer initializes weight matrices as normal distributions.
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See :meth:`LayoutLMPretrainedModel.init_weights()` for how weights are initialized in `LayoutLMModel`.
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pad_token_id (int, optional):
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The index of padding token in the token vocabulary.
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Defaults to `0`.
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pool_act (str, optional):
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The non-linear activation function in the pooling layer.
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Defaults to `"tanh"`.
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"""
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def __init__(self, config: LayoutLMConfig):
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super(LayoutLMModel, self).__init__(config)
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# self.config = kwargs
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self.num_hidden_layers = config.num_hidden_layers
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self.pad_token_id = config.pad_token_id
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self.initializer_range = config.initializer_range
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self.embeddings = LayoutLMEmbeddings(config)
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encoder_layer = nn.TransformerEncoderLayer(
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config.hidden_size,
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config.num_attention_heads,
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config.intermediate_size,
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dropout=config.hidden_dropout_prob,
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activation=config.hidden_act,
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attn_dropout=config.attention_probs_dropout_prob,
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act_dropout=0,
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)
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self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
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self.pooler = LayoutLMPooler(config)
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def get_input_embeddings(self):
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return self.embeddings.word_embeddings
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def set_input_embeddings(self, value):
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self.embeddings.word_embeddings = value
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def resize_position_embeddings(self, new_num_position_embeddings):
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"""
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Resizes position embeddings of the model if `new_num_position_embeddings != config["max_position_embeddings"]`.
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Arguments:
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new_num_position_embeddings (`int`):
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The number of new position embedding matrix. If position embeddings are learned, increasing the size
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will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
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end.
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"""
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num_position_embeds_diff = new_num_position_embeddings - self.config["max_position_embeddings"]
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# no resizing needs to be done if the length stays the same
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if num_position_embeds_diff == 0:
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return
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logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...")
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self.config.max_position_embeddings = new_num_position_embeddings
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old_position_embeddings_weight = self.embeddings.position_embeddings.weight
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self.embeddings.position_embeddings = nn.Embedding(
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self.config.max_position_embeddings, self.config.hidden_size
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)
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with paddle.no_grad():
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if num_position_embeds_diff > 0:
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self.embeddings.position_embeddings.weight[:-num_position_embeds_diff] = old_position_embeddings_weight
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else:
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self.embeddings.position_embeddings.weight = old_position_embeddings_weight[:num_position_embeds_diff]
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def forward(
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self,
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input_ids=None,
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bbox=None,
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token_type_ids=None,
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position_ids=None,
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attention_mask=None,
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output_hidden_states=False,
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):
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r"""
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The LayoutLMModel forward method, overrides the `__call__()` special method.
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Args:
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input_ids (Tensor):
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Indices of input sequence tokens in the vocabulary. They are
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numerical representations of tokens that build the input sequence.
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Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
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token_type_ids (Tensor, optional):
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Segment token indices to indicate different portions of the inputs.
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Selected in the range ``[0, type_vocab_size - 1]``.
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If `type_vocab_size` is 2, which means the inputs have two portions.
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Indices can either be 0 or 1:
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- 0 corresponds to a *sentence A* token,
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- 1 corresponds to a *sentence B* token.
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Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
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Defaults to `None`, which means we don't add segment embeddings.
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position_ids(Tensor, optional):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
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max_position_embeddings - 1]``.
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Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
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attention_mask (Tensor, optional):
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Mask used in multi-head attention to avoid performing attention on to some unwanted positions,
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usually the paddings or the subsequent positions.
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Its data type can be int, float and bool.
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When the data type is bool, the `masked` tokens have `False` values and the others have `True` values.
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When the data type is int, the `masked` tokens have `0` values and the others have `1` values.
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When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values.
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It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
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Defaults to `None`, which means nothing needed to be prevented attention to.
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output_hidden_states (bool, optional):
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Whether to return the output of each hidden layers.
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Defaults to `False`.
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Returns:
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tuple: Returns tuple (`sequence_output`, `pooled_output`).
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With the fields:
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- `sequence_output` (Tensor):
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Sequence of hidden-states at the last layer of the model.
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It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
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- `pooled_output` (Tensor):
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The output of first token (`[CLS]`) in sequence.
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We "pool" the model by simply taking the hidden state corresponding to the first token.
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Its data type should be float32 and its shape is [batch_size, hidden_size].
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"""
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input_shape = input_ids.shape
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if attention_mask is None:
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attention_mask = paddle.unsqueeze(
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(input_ids == self.pad_token_id).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]
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)
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else:
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if attention_mask.ndim == 2:
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# attention_mask [batch_size, sequence_length] -> [batch_size, 1, 1, sequence_length]
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attention_mask = attention_mask.unsqueeze(axis=[1, 2])
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if bbox is None:
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bbox = paddle.zeros(tuple(list(input_shape) + [4]), dtype="int64")
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embedding_output = self.embeddings(
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input_ids=input_ids,
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bbox=bbox,
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position_ids=position_ids,
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token_type_ids=token_type_ids,
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)
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if output_hidden_states:
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output = embedding_output
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encoder_outputs = []
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for mod in self.encoder.layers:
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output = mod(output, src_mask=attention_mask)
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encoder_outputs.append(output)
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if self.encoder.norm is not None:
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encoder_outputs[-1] = self.encoder.norm(encoder_outputs[-1])
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pooled_output = self.pooler(encoder_outputs[-1])
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else:
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sequence_output = self.encoder(embedding_output, attention_mask)
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pooled_output = self.pooler(sequence_output)
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if output_hidden_states:
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return encoder_outputs, pooled_output
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else:
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return sequence_output, pooled_output
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class LayoutLMForTokenClassification(LayoutLMPretrainedModel):
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"""
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LayoutLM Model with a linear layer on top of the hidden-states output layer,
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designed for token classification tasks like NER tasks.
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Args:
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config (:class:`LayoutLMConfig`):
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An instance of LayoutLMConfig used to construct LayoutLMForTokenClassification.
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"""
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def __init__(self, config: LayoutLMConfig):
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super(LayoutLMForTokenClassification, self).__init__(config)
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self.num_classes = config.num_classes
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self.layoutlm = LayoutLMModel(config)
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self.dropout = nn.Dropout(
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config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
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)
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self.classifier = nn.Linear(config.hidden_size, self.num_classes)
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self.classifier.apply(self._init_weights)
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def get_input_embeddings(self):
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return self.layoutlm.embeddings.word_embeddings
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def resize_position_embeddings(self, new_num_position_embeddings):
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"""
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Resizes position embeddings of the model if `new_num_position_embeddings != config["max_position_embeddings"]`.
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Arguments:
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new_num_position_embeddings (`int`):
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The number of new position embedding matrix. If position embeddings are learned, increasing the size
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will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
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end.
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"""
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self.layoutlm.resize_position_embeddings(new_num_position_embeddings)
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def forward(
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self,
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input_ids,
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bbox=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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output_hidden_states=False,
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):
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r"""
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The LayoutLMForTokenClassification forward method, overrides the __call__() special method.
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Args:
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input_ids (Tensor):
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See :class:`LayoutLMModel`.
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bbox (Tensor):
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See :class:`LayoutLMModel`.
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attention_mask (list, optional):
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See :class:`LayoutLMModel`.
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token_type_ids (Tensor, optional):
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See :class:`LayoutLMModel`.
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position_ids(Tensor, optional):
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See :class:`LayoutLMModel`.
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output_hidden_states(Tensor, optional):
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See :class:`LayoutLMModel`.
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Returns:
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Tensor: Returns tensor `logits`, a tensor of the input token classification logits.
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Shape as `[batch_size, sequence_length, num_classes]` and dtype as `float32`.
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Example:
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.. code-block::
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import paddle
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from paddlenlp.transformers import LayoutLMFForTokenClassification
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from paddlenlp.transformers import LayoutLMFTokenizer
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tokenizer = LayoutLMFTokenizer.from_pretrained('layoutlm-base-uncased')
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model = LayoutLMFForTokenClassification.from_pretrained('layoutlm-base-uncased', num_classes=2)
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inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", return_tensors="pd")
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logits = model(**inputs)
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print(logits.shape)
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# [1, 13, 2]
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"""
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if attention_mask is not None:
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attention_mask = attention_mask.unsqueeze(axis=[1, 2]).astype("int64")
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outputs = self.layoutlm(
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input_ids=input_ids,
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bbox=bbox,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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output_hidden_states=False,
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)
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sequence_output = outputs[0]
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sequence_output = self.dropout(sequence_output)
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logits = self.classifier(sequence_output)
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return logits
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class LayoutLMForSequenceClassification(LayoutLMPretrainedModel):
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"""
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LayoutLM Model with a linear layer on top of the output layer,
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designed for sequence classification/regression tasks like GLUE tasks.
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Args:
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config (:class:`LayoutLMConfig`):
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An instance of LayoutLMConfig used to construct LayoutLMForSequenceClassification.
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"""
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def __init__(self, config: LayoutLMConfig):
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super(LayoutLMForSequenceClassification, self).__init__(config)
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self.layoutlm = LayoutLMModel(config)
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|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.num_classes = config.num_classes
|
|
self.classifier = nn.Linear(config.hidden_size, self.num_classes)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.layoutlm.embeddings.word_embeddings
|
|
|
|
def resize_position_embeddings(self, new_num_position_embeddings):
|
|
"""
|
|
Resizes position embeddings of the model if `new_num_position_embeddings != config["max_position_embeddings"]`.
|
|
|
|
Arguments:
|
|
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.
|
|
"""
|
|
self.layoutlm.resize_position_embeddings(new_num_position_embeddings)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
bbox=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
output_hidden_states=False,
|
|
):
|
|
r"""
|
|
The LayoutLMForSequenceClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`LayoutLMModel`.
|
|
bbox (Tensor):
|
|
See :class:`LayoutLMModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`LayoutLMModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`LayoutLMModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`LayoutLMModel`.
|
|
output_hidden_states(Tensor, optional):
|
|
See :class:`LayoutLMModel`.
|
|
|
|
Returns:
|
|
Tensor: Returns tensor `logits`, a tensor of the input text classification logits.
|
|
Shape as `[batch_size, num_classes]` and dtype as float32.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
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!", return_tensors="pd")
|
|
|
|
logits = model(**inputs)
|
|
print(logits.shape)
|
|
# [1, 2]
|
|
|
|
"""
|
|
outputs = self.layoutlm(
|
|
input_ids=input_ids,
|
|
bbox=bbox,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
output_hidden_states=output_hidden_states,
|
|
)
|
|
pooled_outputs = outputs[1]
|
|
pooled_outputs = self.dropout(pooled_outputs)
|
|
logits = self.classifier(pooled_outputs)
|
|
return logits
|
|
|
|
|
|
class LayoutLMLMPredictionHead(Layer):
|
|
"""
|
|
LayoutLM Model with a `language modeling` head on top for CLM fine-tuning.
|
|
"""
|
|
|
|
def __init__(self, config: LayoutLMConfig, weight_attr=None):
|
|
super(LayoutLMLMPredictionHead, self).__init__()
|
|
self.transform = nn.Linear(config.hidden_size, config.hidden_size, weight_attr=weight_attr)
|
|
self.activation = getattr(nn.functional, config.hidden_act)
|
|
self.layer_norm = nn.LayerNorm(config.hidden_size)
|
|
self.decoder = TransposedLinear(config.hidden_size, config.vocab_size)
|
|
# link bias to load pretrained weights
|
|
self.decoder_bias = self.decoder.bias
|
|
# self.decoder_weight = (
|
|
# self.create_parameter(shape=[vocab_size, hidden_size], dtype=self.transform.weight.dtype, is_bias=False)
|
|
# if embedding_weights is None
|
|
# else embedding_weights
|
|
# )
|
|
# self.decoder_bias = self.create_parameter(shape=[vocab_size], dtype=self.decoder_weight.dtype, is_bias=True)
|
|
|
|
def forward(self, hidden_states, masked_positions=None):
|
|
if masked_positions is not None:
|
|
hidden_states = paddle.reshape(hidden_states, [-1, hidden_states.shape[-1]])
|
|
hidden_states = paddle.tensor.gather(hidden_states, masked_positions)
|
|
# gather masked tokens might be more quick
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.activation(hidden_states)
|
|
hidden_states = self.layer_norm(hidden_states)
|
|
hidden_states = self.decoder(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class LayoutLMOnlyMLMHead(nn.Layer):
|
|
def __init__(self, config: LayoutLMConfig, weight_attr=None):
|
|
super().__init__()
|
|
self.predictions = LayoutLMLMPredictionHead(config, weight_attr=weight_attr)
|
|
|
|
def forward(self, sequence_output, masked_positions=None):
|
|
prediction_scores = self.predictions(sequence_output, masked_positions)
|
|
return prediction_scores
|
|
|
|
|
|
class LayoutLMForMaskedLM(LayoutLMPretrainedModel):
|
|
"""
|
|
LayoutLM Model with a `masked language modeling` head on top.
|
|
|
|
Args:
|
|
config (:class:`LayoutLMConfig`):
|
|
An instance of LayoutLMConfig used to construct LayoutLMForMaskedLM.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: LayoutLMConfig):
|
|
super(LayoutLMForMaskedLM, self).__init__(config)
|
|
self.layoutlm = LayoutLMModel(config)
|
|
self.cls = LayoutLMOnlyMLMHead(config)
|
|
|
|
def resize_position_embeddings(self, new_num_position_embeddings):
|
|
"""
|
|
Resizes position embeddings of the model if `new_num_position_embeddings != config["max_position_embeddings"]`.
|
|
|
|
Arguments:
|
|
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.
|
|
"""
|
|
self.layoutlm.resize_position_embeddings(new_num_position_embeddings)
|
|
|
|
def forward(self, input_ids, bbox=None, token_type_ids=None, position_ids=None, attention_mask=None):
|
|
r"""
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`LayoutLMModel`.
|
|
bbox (Tensor):
|
|
See :class:`LayoutLMModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`LayoutLMModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`LayoutLMModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`LayoutLMModel`.
|
|
|
|
Returns:
|
|
Tensor: 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].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
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!", return_tensors="pd")
|
|
|
|
logits = model(**inputs)
|
|
print(logits.shape)
|
|
|
|
"""
|
|
|
|
outputs = self.layoutlm(
|
|
input_ids,
|
|
bbox=bbox,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
)
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output, masked_positions=None)
|
|
return prediction_scores
|