AventIQ-AI / LayoutLMv1_Information_Extraction

LayoutLMv1 Quantized Model for Form Understanding (FUNSD)

This repository hosts a quantized version of the LayoutLMv1 model, fine-tuned for document information extraction on the FUNSD dataset. The model is optimized for extracting entities such as questions, answers, and headers from scanned forms while maintaining efficient performance for deployment in production environments.

Model Details

• Model Architecture: LayoutLMv1 (Hugging Face Transformers)
• Task: Token Classification (Form Understanding / Information Extraction)
• Dataset: nielsr/funsd
• Quantization: Dynamic Quantization (INT8 for Linear layers)
• Fine-tuning Framework: PyTorch & Hugging Face Transformers

Usage

Installation

pip install transformers datasets torch torchvision

Loading the Quantized Model

from transformers import LayoutLMForTokenClassification, LayoutLMTokenizer
import torch

# Load quantized model
model = LayoutLMForTokenClassification.from_pretrained("saved_model_quantized/")
model.load_state_dict(torch.load("saved_model_quantized/pytorch_model.bin"))
model.eval()

# Load tokenizer
tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

Performance Metrics

📊 The model steadily improves until Epoch 3, indicating that the best checkpoint may be around this point.

Fine-Tuning Details

Dataset

• Name: nielsr/funsd
• Description: A benchmark dataset for form understanding tasks with manually labeled fields like questions, answers, headers, etc.

Training Configuration

• Epochs: 5
• Batch Size: As per available memory
• Learning Rate: 4e-5
• Optimizer: AdamW
• Loss Function: CrossEntropyLoss (token classification)

Quantization

Dynamic post-training quantization was applied using PyTorch:

import torch
quantized_model = torch.quantization.quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},
    dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "saved_model_quantized/pytorch_model.bin")

Repository Structure

.
├── saved_model/                 # Original fine-tuned LayoutLMv1
├── saved_model_quantized/      # INT8 quantized model files
│   ├── config.json
│   ├── pytorch_model.bin
├── README.md                   # Project documentation

Limitations

• Trained only on the FUNSD dataset; may not generalize to other document formats.
• Quantized version may have slight degradation in performance compared to the full-precision model.
• Requires OCR and bounding boxes as input for proper operation.

Contributing

Contributions are welcome! If you have suggestions, improvements, or find bugs, feel free to open an issue or submit a pull request.