Token Classification
Transformers
Safetensors
PyTorch
English
layoutlmv2
document-ai
information-extraction
cord-v2
ocr-post-processing
Eval Results (legacy)
Instructions to use ogulcanakca/layoutlmv2-base-uncased-finetuned-cordv2-receipts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ogulcanakca/layoutlmv2-base-uncased-finetuned-cordv2-receipts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ogulcanakca/layoutlmv2-base-uncased-finetuned-cordv2-receipts")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("ogulcanakca/layoutlmv2-base-uncased-finetuned-cordv2-receipts") model = AutoModelForTokenClassification.from_pretrained("ogulcanakca/layoutlmv2-base-uncased-finetuned-cordv2-receipts", device_map="auto") - Notebooks
- Google Colab
- Kaggle