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")# pip install -U transformers accelerate # 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
Download tokenizer.json from devashish-pisal/layoutlmv3-sroie-token-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.56 MB
-
https://huggingface.co/devashish-pisal/layoutlmv3-sroie-token-classification/resolve/main/tokenizer.json
- Command line
-
hf download hf://devashish-pisal/layoutlmv3-sroie-token-classification/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/devashish-pisal/layoutlmv3-sroie-token-classification/resolve/main/tokenizer.json
3.56 MB
File too large to display, you can check the raw version instead.