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5f789a687a
* Add LayoutXLMTokenizer and LayoutXLMTokenizerFast * Fix styling issues * Fix more styling issues * Fix more styling issues * Fix docstring * Fix unit tests * Fix docs * Fix unit tests * Fix typos and styling issues * Fix styling issues * Fix docstring * Make all tests of test_tokenization_layoutxlm pass * Add LayoutXLMProcessor * Make fixup * Make all LayoutXLMProcessor tests pass * Minor fixes * Leave LayoutLMv2Processor tests unchanged * Fix code quality * Move LayoutXLM tokenizers and processor to separate folder * Fix code quality * Apply suggestions from code review * Replace assertions by value errors * Remove methods from fast tokenizer Co-authored-by: King Yiu Suen <kingyiusuen@gmail.com>
85 lines
4.0 KiB
ReStructuredText
85 lines
4.0 KiB
ReStructuredText
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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LayoutXLM
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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LayoutXLM was proposed in `LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
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<https://arxiv.org/abs/2104.08836>`__ by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha
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Zhang, Furu Wei. It's a multilingual extension of the `LayoutLMv2 model <https://arxiv.org/abs/2012.14740>`__ trained
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on 53 languages.
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The abstract from the paper is the following:
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*Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document
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understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In
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this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to
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bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also
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introduce a multilingual form understanding benchmark dataset named XFUN, which includes form understanding samples in
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7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled
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for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA
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cross-lingual pre-trained models on the XFUN dataset.*
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One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like so:
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.. code-block::
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from transformers import LayoutLMv2Model
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model = LayoutLMv2Model.from_pretrained('microsoft/layoutxlm-base')
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Note that LayoutXLM has its own tokenizer, based on
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:class:`~transformers.LayoutXLMTokenizer`/:class:`~transformers.LayoutXLMTokenizerFast`. You can initialize it as
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follows:
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.. code-block::
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from transformers import LayoutXLMTokenizer
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tokenizer = LayoutXLMTokenizer.from_pretrained('microsoft/layoutxlm-base')
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Similar to LayoutLMv2, you can use :class:`~transformers.LayoutXLMProcessor` (which internally applies
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:class:`~transformers.LayoutLMv2FeatureExtractor` and
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:class:`~transformers.LayoutXLMTokenizer`/:class:`~transformers.LayoutXLMTokenizerFast` in sequence) to prepare all
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data for the model.
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As LayoutXLM's architecture is equivalent to that of LayoutLMv2, one can refer to :doc:`LayoutLMv2's documentation page
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<layoutlmv2>` for all tips, code examples and notebooks.
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This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
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<https://github.com/microsoft/unilm>`__.
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LayoutXLMTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.LayoutXLMTokenizer
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:members: __call__, build_inputs_with_special_tokens, get_special_tokens_mask,
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create_token_type_ids_from_sequences, save_vocabulary
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LayoutXLMTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.LayoutXLMTokenizerFast
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:members: __call__
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LayoutXLMProcessor
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.LayoutXLMProcessor
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:members: __call__
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