Instructions to use vaibhav1411/layoutlmv2-finetuned-cord with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhav1411/layoutlmv2-finetuned-cord with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vaibhav1411/layoutlmv2-finetuned-cord")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("vaibhav1411/layoutlmv2-finetuned-cord") model = AutoModelForTokenClassification.from_pretrained("vaibhav1411/layoutlmv2-finetuned-cord") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3086773949198be7363bccec5cff1c7c943e4a8038e55849ce415dc603cf193
- Size of remote file:
- 1.71 GB
- SHA256:
- 5ce45760ae8f7a25a7657c80843b60f73a1de1e0dd532c6398b41b86ee2aeb9e
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