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 model.safetensors from hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 439 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-LayoutLMv3ForTokenClassification/resolve/refs%2Fpr%2F1/model.safetensors
439 kB
- Xet hash:
- 70b6a243c71496d09c8d0793aa3c171f4ec11697f5986409b9e4700f77e0dfd9
- Size of remote file:
- 439 kB
- SHA256:
- cd53d7429e87684eb2e0383e2d88eb8e21445c666b975317e103f2ac1c85adc7
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