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
File size: 461 Bytes
8514b57 2092df8 8514b57 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ---
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](https://paperswithcode.com/dataset/funsd) dataset.
It also provides an inference phase where you can store your results on google drive |