Instructions to use Tornaid/LayoutLMv2_D3_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tornaid/LayoutLMv2_D3_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tornaid/LayoutLMv2_D3_Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Tornaid/LayoutLMv2_D3_Classifier") model = AutoModelForSequenceClassification.from_pretrained("Tornaid/LayoutLMv2_D3_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Tornaid/LayoutLMv2_D3_Classifier: direct link, hf CLI and curl.
- Browser
- Download file 802 MB
-
https://huggingface.co/Tornaid/LayoutLMv2_D3_Classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://Tornaid/LayoutLMv2_D3_Classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Tornaid/LayoutLMv2_D3_Classifier/resolve/main/model.safetensors
802 MB
- Xet hash:
- 8e7ae5a603ef936dce56e5f55f68d84ff6c1ecafee191c8bbd27c6a8439536ac
- Size of remote file:
- 802 MB
- SHA256:
- 664b9aca2e5c0b6a595432008035acc64103cb1a7205cdfc01a2cb7851f4a068
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