huggingface--transformers/docs/source/en/model_doc/layoutlm.md

This model was published in HF papers on 2019-12-31 and contributed to Hugging Face Transformers on 2020-11-16.

LayoutLM

LayoutLM jointly learns text and the document layout rather than focusing only on text. It incorporates positional layout information and visual features of words from the document images.

You can find all the original LayoutLM checkpoints under the LayoutLM collection.

Tip

Click on the LayoutLM models in the right sidebar for more examples of how to apply LayoutLM to different vision and language tasks.

The example below demonstrates question answering with the [AutoModel] class.

import torch
from datasets import load_dataset

from transformers import AutoTokenizer, LayoutLMForQuestionAnswering


tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
model = LayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", device_map="auto")

dataset = load_dataset("nielsr/funsd", split="train")
example = dataset[0]
question = "what's his name?"
words = example["words"]
boxes = example["bboxes"]

encoding = tokenizer(
    question.split(),
    words,
    is_split_into_words=True,
    return_token_type_ids=True,
    return_tensors="pt"
)
bbox = []
for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
    if s == 1:
        bbox.append(boxes[w])
    elif i == tokenizer.sep_token_id:
        bbox.append([1000] * 4)
    else:
        bbox.append([0] * 4)
encoding["bbox"] = torch.tensor([bbox])

word_ids = encoding.word_ids(0)
outputs = model(**encoding)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
start, end = word_ids[start_scores.argmax(-1)], word_ids[end_scores.argmax(-1)]
print(" ".join(words[start : end + 1]))

Notes

• The original LayoutLM was not designed with a unified processing workflow. Instead, it expects preprocessed text (words) and bounding boxes (boxes) from an external OCR engine (like Pytesseract) and provide them as additional inputs to the tokenizer.

• The [~LayoutLMModel.forward] method expects the input bbox (bounding boxes of the input tokens). Each bounding box should be in the format (x0, y0, x1, y1). (x0, y0) corresponds to the upper left corner of the bounding box and {x1, y1) corresponds to the lower right corner. The bounding boxes need to be normalized on a 0-1000 scale as shown below.

def normalize_bbox(bbox, width, height):
    return [
        int(1000 * (bbox[0] / width)),
        int(1000 * (bbox[1] / height)),
        int(1000 * (bbox[2] / width)),
        int(1000 * (bbox[3] / height)),
    ]

• width and height correspond to the width and height of the original document in which the token occurs. These values can be obtained as shown below.

from PIL import Image


# Document can be a png, jpg, etc. PDFs must be converted to images.
image = Image.open(name_of_your_document).convert("RGB")

width, height = image.size

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLM. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

• Read fine-tuning LayoutLM for document-understanding using Keras & Hugging Face Transformers to learn more.
• Read fine-tune LayoutLM for document-understanding using only Hugging Face Transformers for more information.
• Refer to this notebook for a practical example of how to fine-tune LayoutLM.
• Refer to this notebook for an example of how to fine-tune LayoutLM for sequence classification.
• Refer to this notebook for an example of how to fine-tune LayoutLM for token classification.
• Read Deploy LayoutLM with Hugging Face Inference Endpoints to learn how to deploy LayoutLM.

LayoutLMConfig

autodoc LayoutLMConfig

LayoutLMTokenizer

autodoc LayoutLMTokenizer - call

LayoutLMTokenizerFast

autodoc LayoutLMTokenizerFast - call

LayoutLMModel

autodoc LayoutLMModel

LayoutLMForMaskedLM

autodoc LayoutLMForMaskedLM

LayoutLMForSequenceClassification

autodoc LayoutLMForSequenceClassification

LayoutLMForTokenClassification

autodoc LayoutLMForTokenClassification

LayoutLMForQuestionAnswering

autodoc LayoutLMForQuestionAnswering