Instructions to use hf-internal-testing/tiny-random-LayoutLMForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-LayoutLMForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LayoutLMForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f0736e9dcec8889f8d4a5afa72ae66f13e0a7dbde21bcd1ee6c2872a964f563
- Size of remote file:
- 995 kB
- SHA256:
- a35c05f509ac70f0649d75abd0cfcab753d165f5ec3fe626de9f961a807d21d5
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