Instructions to use 1glabs/layoutlmv3-document-classificationTrialyc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1glabs/layoutlmv3-document-classificationTrialyc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="1glabs/layoutlmv3-document-classificationTrialyc")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("1glabs/layoutlmv3-document-classificationTrialyc") model = AutoModelForSequenceClassification.from_pretrained("1glabs/layoutlmv3-document-classificationTrialyc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from 1glabs/layoutlmv3-document-classificationTrialyc: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/1glabs/layoutlmv3-document-classificationTrialyc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://1glabs/layoutlmv3-document-classificationTrialyc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/1glabs/layoutlmv3-document-classificationTrialyc/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- c7ba5c6cebcb786ae9d9f349c42607cacddf49ad908ff553c1066bfd45370831
- Size of remote file:
- 504 MB
- SHA256:
- 8430e0d647b5d2a512bb7fee69d159268323cc1509d56846e0ddabab4e32843d
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