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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d835bbefbfd6fa3651478bd70637d8adc67c74b5ec1dcd49a60316d4a9f68185
- Size of remote file:
- 891 kB
- SHA256:
- aee8640dedf75decc8e5320dffc20ad817888d879306072acaf9b8ee8269b157
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