Token Classification
Transformers
Safetensors
English
layoutlmv3
document-ai
invoice
sroie
BIO-tagging
NER
named-entity-recognition
multimodel
Instructions to use devashish-pisal/layoutlmv3-sroie-token-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devashish-pisal/layoutlmv3-sroie-token-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devashish-pisal/layoutlmv3-sroie-token-classification")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("devashish-pisal/layoutlmv3-sroie-token-classification") model = AutoModelForTokenClassification.from_pretrained("devashish-pisal/layoutlmv3-sroie-token-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 525d8567e71ce9892b14f67eb3f5c8c2df872e12c2f0f442e6b5fb2ac6f73b11
- Size of remote file:
- 4.79 kB
- SHA256:
- 3114070c6c715c12913d7569602ec2974bb06f58826667ba9e738909f81225a4
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