Instructions to use Sebabrata/lmv2-g-receipts2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebabrata/lmv2-g-receipts2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sebabrata/lmv2-g-receipts2")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Sebabrata/lmv2-g-receipts2") model = AutoModelForTokenClassification.from_pretrained("Sebabrata/lmv2-g-receipts2") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:00b372bff6460141f0c7604f848522baac7dc751b678469b45ee4f53ca33eb9b
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size 802131884
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