Instructions to use hf-tiny-model-private/tiny-random-LayoutLMForTokenClassification 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-LayoutLMForTokenClassification 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-LayoutLMForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa432e5515487a6e234f43b939fef7917c0d54e7167d0a1425b42bd7b62ed6e1
- Size of remote file:
- 891 kB
- SHA256:
- 920177949d4b19a6dafb467131bda078d9f48f47d970936dcdb1c7ab83bd6fad
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