Instructions to use Reshma27/Layout_LM_EntityExtractionFor_ProofOfAddress with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Reshma27/Layout_LM_EntityExtractionFor_ProofOfAddress with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Reshma27/Layout_LM_EntityExtractionFor_ProofOfAddress")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Reshma27/Layout_LM_EntityExtractionFor_ProofOfAddress") model = AutoModelForTokenClassification.from_pretrained("Reshma27/Layout_LM_EntityExtractionFor_ProofOfAddress") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95eb629032c445266b8468eb70603326f0b95548ead513e98e904d9681293f64
- Size of remote file:
- 451 MB
- SHA256:
- 5c7021740a61bada3b50d6929bf4fded5de25b79ab8c1e9223480524e9534637
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