Instructions to use philschmid/lilt-en-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/lilt-en-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="philschmid/lilt-en-funsd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/lilt-en-funsd") model = AutoModelForTokenClassification.from_pretrained("philschmid/lilt-en-funsd") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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- generated_from_trainer
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datasets:
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- funsd-layoutlmv3
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model-index:
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- name: lilt-en-funsd
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results: []
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- generated_from_trainer
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datasets:
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- funsd-layoutlmv3
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base_model: SCUT-DLVCLab/lilt-roberta-en-base
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model-index:
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- name: lilt-en-funsd
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results: []
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