Instructions to use 1glabs/layoutlmv3-document-classificationTrial2yc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1glabs/layoutlmv3-document-classificationTrial2yc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="1glabs/layoutlmv3-document-classificationTrial2yc")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("1glabs/layoutlmv3-document-classificationTrial2yc") model = AutoModelForSequenceClassification.from_pretrained("1glabs/layoutlmv3-document-classificationTrial2yc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from 1glabs/layoutlmv3-document-classificationTrial2yc: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/1glabs/layoutlmv3-document-classificationTrial2yc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://1glabs/layoutlmv3-document-classificationTrial2yc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/1glabs/layoutlmv3-document-classificationTrial2yc/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- 65196269b93a663d4d2cfdce033fcca3f76158a7df211ff77dbb8e5d8b25c921
- Size of remote file:
- 504 MB
- SHA256:
- db22930f2b6e4e16f4190b4e3d4fe3364d5a27e4fb11402f140cc6974e2be232
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