Instructions to use pabloOmega/ocr-style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pabloOmega/ocr-style with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pabloOmega/ocr-style")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("pabloOmega/ocr-style") model = AutoModelForTokenClassification.from_pretrained("pabloOmega/ocr-style") - Notebooks
- Google Colab
- Kaggle
Upload LayoutLMv3ForTokenClassification
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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"shape_size": 128,
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"text_embed": true,
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"torch_dtype": "float32",
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"transformers_version": "4.51.
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"type_vocab_size": 1,
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"visual_embed": true,
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"vocab_size": 50265
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"shape_size": 128,
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"text_embed": true,
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"torch_dtype": "float32",
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"transformers_version": "4.51.3",
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"type_vocab_size": 1,
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"visual_embed": true,
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"vocab_size": 50265
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 501361684
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version https://git-lfs.github.com/spec/v1
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size 501361684
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