Instructions to use elvinaqa/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elvinaqa/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="elvinaqa/layoutlm-funsd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("elvinaqa/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("elvinaqa/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da8bc64ce1a0cf718fce2348dc33d6f424bc5b6e0ed2e3d9764d7c45f5646d14
- Size of remote file:
- 451 MB
- SHA256:
- 59f010d99b4da02a40f96c2b43da2de3830dc9023f8387faddca778c6e72481f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.