kbsooo/layoutlmv3_finetuned_doclaynet

Model Card for kbsooo/layoutlmv3finetuneddoclaynet

Model Details

Model Description

This model is a fine-tuned version of LayoutLMv3 for token classification on the DocLayNet dataset. It is designed to classify each token in a document image based on both textual and layout information.

• —Developed by: kbsooo
• —Model type: LayoutLMv3ForTokenClassification
• —Language(s) (NLP): Korean (document-oriented)
• —License: Check DocLayNet and LayoutLMv3 licenses
• —Finetuned from model: microsoft/layoutlmv3-base

Model Sources

• —Repository: Hugging Face Model Hub
• —Paper (optional): LayoutLMv3 Paper

Uses

Direct Use

This model can be used for:

• —Token classification in document images (e.g., identifying headings, paragraphs, tables, images, lists)
• —Document understanding tasks where layout + text information is important

Downstream Use

• —Can be integrated into pipelines for document information extraction
• —Useful for document analysis applications: invoice parsing, form processing, etc.

Out-of-Scope Use

• —Not intended for languages or layouts not represented in the DocLayNet dataset
• —Not suitable for free-form text without document structure

Bias, Risks, and Limitations

• —The model may misclassify tokens if the document layout or language differs from the training data
• —Biases may exist due to dataset composition (DocLayNet)
• —Limited to 10 classes of document layout elements

Recommendations

• —Users should preprocess documents similarly to the training setup (tokenization + bounding boxes + image)
• —Verify predictions, especially in production or high-stakes scenarios

How to Get Started with the Model

from transformers import LayoutLMv3ForTokenClassification, AutoProcessor
import torch

repo = "kbsooo/layoutlmv3_finetuned_doclaynet"
model = LayoutLMv3ForTokenClassification.from_pretrained(repo)
processor = AutoProcessor.from_pretrained(repo)

image = ...  # PIL.Image or np.array
text = "Sample document text"

encoding = processor(image, text, return_tensors="pt")
outputs = model(**encoding)
preds = torch.argmax(outputs.logits, dim=-1)
print(preds)

Training Details

Training Data

• —Dataset: DocLayNet-v1.2
• —Train/Validation split: 200/100 samples
• —Columns: inputids, attentionmask, bbox, labels, pixelvalues, nwordsin, nwords_out

Training Procedure

• —Optimizer: AdamW
• —Learning rate: 5e-5
• —Epochs: 5
• —Mixed precision: FP16 optional
• —Loss: Cross-entropy per token

Evaluation

• —Sample metrics (from validation set):
• —Avg Train Loss: 0.134
• —Avg Val Loss: 0.458
• —Token prediction accuracy should be checked against the DocLayNet labels

Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• —Hardware Type: NVIDIA A100
• —Hours used: ~1 hr for 5 epochs (for small dataset)

Technical Specifications

Model Architecture and Objective

• —Base model: LayoutLMv3
• —Task: Token classification for document layout elements
• —Input: Tokenized text, bounding boxes, and document images
• —Output: Token-wise logits for 10 classes

Compute Infrastructure

• —Training performed on Google Colab Pro (A100 GPU)
• —Framework: PyTorch + Hugging Face Transformers

Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

@article{huang2022layoutlmv3,
  title={LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking},
  author={Huang, Zejiang and et al.},
  journal={arXiv preprint arXiv:2112.01041},
  year={2022}
}

APA:

Huang, Z., et al. (2022). LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking. arXiv preprint arXiv:2112.01041.