Instructions to use hf-internal-testing/tiny-random-LayoutLMv2ForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv2ForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-LayoutLMv2ForTokenClassification")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv2ForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv2ForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6985d840f87965c5964b2215cf4117ad129213f6c022b5b1b2d8d6fb521ba005
- Size of remote file:
- 59.9 MB
- SHA256:
- 09fd825462fe15a5624901833d2760f8271bdc67d409a80c3315b70bfc32d511
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