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:
- ac1dc4eee1ac25cc2b2f7c700f24286c5d04bf9fdc1398739968c00e35612baf
- Size of remote file:
- 451 MB
- SHA256:
- 857973e092fad5e5a5aeaa4b81798f1786d86178e305fd019be69cb69440cead
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.