Mosal1985/gst-layoutlm-tiny

GST LayoutLM Tiny (v10)

A tiny LayoutLM-style student for extracting fields from Indian GST invoices. Architecture: 6 layers / 256 hidden / 4 attention heads (~13M parameters).

v10 training and evaluation

• Training: 2,422 pages, 40 epochs, 48,440 steps, Tesla T4
• Evaluation: 18 held-out pages across four vendor families
• Macro field F1: 0.598
• Entity F1: 0.626 (precision 0.601, recall 0.654)
• Token accuracy: 0.476
• BUYER_NAME F1: 0.805
• INVOICE_DATE F1: 0.818
• CGST_AMOUNT / SGST_AMOUNT / PLACE_OF_SUPPLY F1: 1.000
• INT8 ONNX size: 13.8 MB

The v10 dataset contains 2,440 records in total (2,422 train + 18 evaluation) and includes retail-layout, title-negative, buyer/GSTIN/POS, and multi-page PDF augmentation fixes.

Files

File	Size	Use
pytorch_model.bin	52.4 MB	PyTorch from_pretrained weights
model_int8.onnx	13.8 MB	INT8 ONNX Runtime / Android deployment
config.json / label2id.json	—	Model configuration and BIO labels
report.json	—	Evaluation report and per-field metrics
onnx_benchmark.json	—	ONNX benchmark results

Quickstart

from transformers import LayoutLMConfig, LayoutLMForTokenClassification

config = LayoutLMConfig.from_pretrained("Mosal1985/gst-layoutlm-tiny")
model = LayoutLMForTokenClassification.from_pretrained(
    "Mosal1985/gst-layoutlm-tiny", config=config
)

For mobile/edge inference, use model_int8.onnx with ONNX Runtime and the corresponding config.json / label2id.json files.