Instructions to use hf-tiny-model-private/tiny-random-LayoutLMForSequenceClassification 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-LayoutLMForSequenceClassification 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-LayoutLMForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c9f354f66d5f2d52d663aef7f3b124d963560a7fe33f2fb403ca17f61ab607a
- Size of remote file:
- 911 kB
- SHA256:
- f7b735960a78b79051d31ade96f318f44cf402dc2cebc09febdd533957b504bf
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