Instructions to use impira/layoutlm-document-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use impira/layoutlm-document-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="impira/layoutlm-document-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-classifier") model = AutoModelForSequenceClassification.from_pretrained("impira/layoutlm-document-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from impira/layoutlm-document-classifier: direct link, hf CLI and curl.
- Browser
- Download file 451 MB
-
https://huggingface.co/impira/layoutlm-document-classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://impira/layoutlm-document-classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/impira/layoutlm-document-classifier/resolve/main/pytorch_model.bin
451 MB
- Xet hash:
- c7e7c5c6a246ca900466e8c95a7d90b545e8f5e2448a9be0ff1a2dc636c1bb44
- Size of remote file:
- 451 MB
- SHA256:
- a118210320fa8fa0e0a90a3cba350e9510f59b9c42e271d6eaa402557c361c9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.