Instructions to use Sebabrata/lmv2-g-receipts4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebabrata/lmv2-g-receipts4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sebabrata/lmv2-g-receipts4")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Sebabrata/lmv2-g-receipts4") model = AutoModelForTokenClassification.from_pretrained("Sebabrata/lmv2-g-receipts4") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#3 opened over 2 years ago
by
SFconvertbot
Librarian Bot: Add base_model information to model
#2 opened over 2 years ago
by
librarian-bot
Help: How to do inference
#1 opened over 3 years ago
by
preetsc27