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[LayoutLMv3] Add TensorFlow implementation (#18678)

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Co-authored-by: Esben Toke Christensen <esben.christensen@visma.com>
Co-authored-by: Lasse Reedtz <lasse.reedtz@visma.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
This commit is contained in:
Christoffer Koo Øhrstrøm
2022-08-30 12:48:11 +02:00
committed by GitHub
parent 7320d95d98
commit de8548ebf3
10 changed files with 2227 additions and 10 deletions
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docs/source/en/index.mdx
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@@ -38,7 +38,7 @@ The documentation is organized in five parts:
- **GET STARTED** contains a quick tour and installation instructions to get up and running with 🤗 Transformers.
- **TUTORIALS** are a great place to begin if you are new to our library. This section will help you gain the basic skills you need to start using 🤗 Transformers.
- **HOW-TO GUIDES** will show you how to achieve a specific goal like fine-tuning a pretrained model for language modeling or how to create a custom model head.
- **CONCEPTUAL GUIDES** provides more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.
- **CONCEPTUAL GUIDES** provides more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.
- **API** describes each class and function, grouped in:
- **MAIN CLASSES** for the main classes exposing the important APIs of the library.
@@ -245,7 +245,7 @@ Flax), PyTorch, and/or TensorFlow.
| ImageGPT | ❌ | ❌ | ✅ | ❌ | ❌ |
| LayoutLM | ✅ | ✅ | ✅ | ✅ | ❌ |
| LayoutLMv2 | ✅ | ✅ | ✅ | ❌ | ❌ |
| LayoutLMv3 | ✅ | ✅ | ✅ | ❌ | ❌ |
| LayoutLMv3 | ✅ | ✅ | ✅ | ✅ | ❌ |
| LED | ✅ | ✅ | ✅ | ✅ | ❌ |
| LeViT | ❌ | ❌ | ✅ | ❌ | ❌ |
| Longformer | ✅ | ✅ | ✅ | ✅ | ❌ |

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docs/source/en/model_doc/layoutlmv3.mdx
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@@ -26,18 +26,18 @@ Tips:
- In terms of data processing, LayoutLMv3 is identical to its predecessor [LayoutLMv2](layoutlmv2), except that:
- images need to be resized and normalized with channels in regular RGB format. LayoutLMv2 on the other hand normalizes the images internally and expects the channels in BGR format.
- text is tokenized using byte-pair encoding (BPE), as opposed to WordPiece.
- text is tokenized using byte-pair encoding (BPE), as opposed to WordPiece.
Due to these differences in data preprocessing, one can use [`LayoutLMv3Processor`] which internally combines a [`LayoutLMv3FeatureExtractor`] (for the image modality) and a [`LayoutLMv3Tokenizer`]/[`LayoutLMv3TokenizerFast`] (for the text modality) to prepare all data for the model.
- Regarding usage of [`LayoutLMv3Processor`], we refer to the [usage guide](layoutlmv2#usage-layoutlmv2processor) of its predecessor.
- Regarding usage of [`LayoutLMv3Processor`], we refer to the [usage guide](layoutlmv2#usage-layoutlmv2processor) of its predecessor.
- Demo notebooks for LayoutLMv3 can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3).
- Demo scripts can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3).
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/layoutlmv3_architecture.png"
alt="drawing" width="600"/>
alt="drawing" width="600"/>
<small> LayoutLMv3 architecture. Taken from the <a href="https://arxiv.org/abs/2204.08387">original paper</a>. </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/layoutlmv3).
This model was contributed by [nielsr](https://huggingface.co/nielsr). The TensorFlow version of this model was added by [chriskoo](https://huggingface.co/chriskoo), [tokec](https://huggingface.co/tokec), and [lre](https://huggingface.co/lre). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/layoutlmv3).
## LayoutLMv3Config
@@ -84,3 +84,23 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
[[autodoc]] LayoutLMv3ForQuestionAnswering
- forward
## TFLayoutLMv3Model
[[autodoc]] TFLayoutLMv3Model
- call
## TFLayoutLMv3ForSequenceClassification
[[autodoc]] TFLayoutLMv3ForSequenceClassification
- call
## TFLayoutLMv3ForTokenClassification
[[autodoc]] TFLayoutLMv3ForTokenClassification
- call
## TFLayoutLMv3ForQuestionAnswering
[[autodoc]] TFLayoutLMv3ForQuestionAnswering
- call