Instructions to use ryanznie/layoutlmv3-lora-invoice-number with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ryanznie/layoutlmv3-lora-invoice-number with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base") model = PeftModel.from_pretrained(base_model, "ryanznie/layoutlmv3-lora-invoice-number") - Notebooks
- Google Colab
- Kaggle
Finetuning notebook
#1
by kaamran - opened
Hey buddy.
Can you plz share the notebook of finetune layoutlmv3 with Lora. Currently i have problem I finetuned my model but whenever i need to train more data , I have to train whole data from scratch otherwise, model's performance effects and it is so time taking , so I want to test LoRA finetunning, to just train the new data instead of whole data.
Hey! See finetuning script here. Feel free to refactor into a Google Collab / Kaggle NB and share to Kaggle.
I personally have not re-finetuned or added any new data for finetuning. You can try:
- Retrain from scratch with old + new data (best practice)
- Continue training from a model checkpoint with some old + new data (to prevent forgetting old behavior)
If you have a M-series chip from Apple, you can use mps for faster training. cuda if you have CUDA.
Let me know how it goes:)