End of training
Browse files
README.md
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Answer: {'precision': 0.
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- Header: {'precision': 0.
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- Question: {'precision': 0.
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- Overall Precision: 0.
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- Overall Recall: 0.
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- Overall F1: 0.
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- Overall Accuracy: 0.
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- training_steps:
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question
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### Framework versions
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2479
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- Answer: {'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817}
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- Header: {'precision': 0.6262626262626263, 'recall': 0.5210084033613446, 'f1': 0.5688073394495413, 'number': 119}
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- Question: {'precision': 0.8877005347593583, 'recall': 0.924791086350975, 'f1': 0.9058663028649386, 'number': 1077}
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- Overall Precision: 0.8657
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- Overall Recall: 0.8932
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- Overall F1: 0.8792
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- Overall Accuracy: 0.8133
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- training_steps: 500
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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| 0.4245 | 10.53 | 200 | 0.9942 | {'precision': 0.8187845303867404, 'recall': 0.9069767441860465, 'f1': 0.8606271777003485, 'number': 817} | {'precision': 0.5178571428571429, 'recall': 0.48739495798319327, 'f1': 0.5021645021645021, 'number': 119} | {'precision': 0.8821396192203083, 'recall': 0.903435468895079, 'f1': 0.8926605504587157, 'number': 1077} | 0.8358 | 0.8803 | 0.8575 | 0.8150 |
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| 0.0366 | 21.05 | 400 | 1.2479 | {'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817} | {'precision': 0.6262626262626263, 'recall': 0.5210084033613446, 'f1': 0.5688073394495413, 'number': 119} | {'precision': 0.8877005347593583, 'recall': 0.924791086350975, 'f1': 0.9058663028649386, 'number': 1077} | 0.8657 | 0.8932 | 0.8792 | 0.8133 |
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### Framework versions
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tokenizer.json
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