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:
- 8fd3592c9d5fe8e548a4c17e8ae5b59dd18a891004adedb9c413a8145d7736e1
- Size of remote file:
- 1.01 MB
- SHA256:
- fd56a337e391e4384a96fdeddb5ca22aabb6b124ab9b7c0178e055a068561544
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