Instructions to use V0ltron/layoutLMTesting-different-labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use V0ltron/layoutLMTesting-different-labels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="V0ltron/layoutLMTesting-different-labels")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("V0ltron/layoutLMTesting-different-labels") model = AutoModelForSequenceClassification.from_pretrained("V0ltron/layoutLMTesting-different-labels") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45817e59ccc3b1a3b678003ace4d6fab4c660d8d7dfe0465a58d5090dda74165
- Size of remote file:
- 804 MB
- SHA256:
- edf2b1af84f0d35a0a685132043c28f2fe9ac518998351f2c04b9753ac01e2cb
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