A LayoutLMv3-large model fine-tuned for Named Entity Recognition
on invoice documents. This is in a sequential
fine-tuning pipeline being developed for production
invoice processing — including Ghanaian and West African invoices.
Built on top of
invoice-ner-v1-mychen76,
this model extends coverage to a much larger and more diverse
invoice dataset (7,801 samples vs 416 in v1).
Same architecture as v1 — LayoutLMv3-large processing words,
bounding boxes, and image patches simultaneously. Fine-tuned
with a reduced learning rate (5e-6 vs 1e-5) to preserve
knowledge from v1 while adapting to the larger amaye15 dataset.
v1 scored 0.9497 on a 42-sample validation set of synthetic
invoices similar to its training data. v2 scores 0.9131 on
a 780-sample validation set of much more diverse invoices.
The larger, harder validation set explains the difference —
v2 is a stronger and more generalisable model overall.
