Instructions to use hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 453 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification/resolve/4b215fdbad2c6c9560616c127b5e691d7fbed239/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification@4b215fdbad2c6c9560616c127b5e691d7fbed239/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification/resolve/4b215fdbad2c6c9560616c127b5e691d7fbed239/pytorch_model.bin
453 kB
- Xet hash:
- 9cfa7b47d8f706bdd0864ea637eed76a7ee18c4042368a6b2749f0f23df0fe69
- Size of remote file:
- 453 kB
- SHA256:
- 757a9c89b483fe5e708ddac4bd2aafd0b5cff5d975e98e4e40cbfbe42725cd24
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