Instructions to use jinhybr/layoutlm-funsd-tf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinhybr/layoutlm-funsd-tf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jinhybr/layoutlm-funsd-tf")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jinhybr/layoutlm-funsd-tf") model = AutoModelForTokenClassification.from_pretrained("jinhybr/layoutlm-funsd-tf") - Notebooks
- Google Colab
- Kaggle
Upload TFLayoutLMForTokenClassification
Browse files- README.md +2 -2
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README.md
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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#
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# layoutlm-funsd-tf
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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