Object Detection
Transformers
PyTorch
TensorBoard
layoutlm
token-classification
Generated from Trainer
endpoints-template
Instructions to use Narsil/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Narsil/layoutlm-funsd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("Narsil/layoutlm-funsd") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
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---
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tags:
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- generated_from_trainer
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- endpoints-template
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pipeline_tag: object-detection
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widget:
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- src:
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example_title: invoice
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- src:
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example_title: contract
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- funsd
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model-index:
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- name: layoutlm-funsd
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results: []
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---
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library_name: transformers
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tags:
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- generated_from_trainer
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- endpoints-template
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datasets:
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- funsd
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pipeline_tag: object-detection
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widget:
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- src: https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png
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example_title: invoice
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- src: https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg
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example_title: contract
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base_model: microsoft/layoutlm-base-uncased
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model-index:
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- name: layoutlm-funsd
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results: []
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