Instructions to use SimaoQuintela/LayoutLMv3-FUNSD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SimaoQuintela/LayoutLMv3-FUNSD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="SimaoQuintela/LayoutLMv3-FUNSD")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SimaoQuintela/LayoutLMv3-FUNSD", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- nielsr/funsd
language:
- pt
- en
base_model:
- microsoft/layoutlmv3-base
pipeline_tag: document-question-answering
library_name: transformers
tags:
- code
LayoutLMv3-FUNSD
This notebook trains the LayoutLMv3 layout model (on kagggle to take advantage of the GPU accelerators) on the FUNSD dataset. It also provides an inference phase where you can store your results on google drive