Instructions to use hf-internal-testing/tiny-random-LayoutLMForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LayoutLMForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LayoutLMForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bddfec57299fa588b7da4acd0c5ce72d06942313d9c146bb5d3e55f5c55fa77
- Size of remote file:
- 911 kB
- SHA256:
- f6861ea852ef19b4c27eabab49b4393a9f23e2689537652628b10676a5094457
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