Instructions to use hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c579ae47dce7b9a4c7e66be1cd799efa4ae0b453b5ab049b7a2f794bd5860bb4
- Size of remote file:
- 528 kB
- SHA256:
- 4c029244d5eac3c0f318d9182557654680cec5a5c3871a9abadfac19a722b4c9
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