Instructions to use hf-tiny-model-private/tiny-random-LayoutLMv3ForSequenceClassification 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-LayoutLMv3ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-LayoutLMv3ForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d3413a2c955b39887f474839d796d14a84ad268ceb3229be490522a52e58c19
- Size of remote file:
- 539 kB
- SHA256:
- a6780b1c4d137848ba2200ba96df9b0d1d96a36e33bbbf2e728f887a5e39b5b0
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