Instructions to use hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51f46f476ba610edd2e4bce8cd50d0577beb26dc609d6eb8ad1d111995fc4bcc
- Size of remote file:
- 539 kB
- SHA256:
- 0da0618995040cc3846cc886d6b5c44edc95a8ca4db00e19ad57dc60379fb95e
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