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--- |
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license: mit |
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tags: |
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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: my-lilt-en-funsd |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# my-lilt-en-funsd |
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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: 1.7942 |
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- Answer: {'precision': 0.8597914252607184, 'recall': 0.9082007343941249, 'f1': 0.8833333333333333, 'number': 817} |
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- Header: {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119} |
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- Question: {'precision': 0.9046746104491292, 'recall': 0.9164345403899722, 'f1': 0.9105166051660516, 'number': 1077} |
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- Overall Precision: 0.8740 |
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- Overall Recall: 0.8927 |
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- Overall F1: 0.8833 |
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- Overall Accuracy: 0.8042 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 2500 |
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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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| 0.1935 | 26.32 | 500 | 1.2125 | {'precision': 0.8702830188679245, 'recall': 0.9033047735618115, 'f1': 0.8864864864864864, 'number': 817} | {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119} | {'precision': 0.8748921484037964, 'recall': 0.9415041782729805, 'f1': 0.9069767441860466, 'number': 1077} | 0.8605 | 0.9041 | 0.8818 | 0.8024 | |
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| 0.0063 | 52.63 | 1000 | 1.4406 | {'precision': 0.8732394366197183, 'recall': 0.9106487148102815, 'f1': 0.8915518274415818, 'number': 817} | {'precision': 0.632183908045977, 'recall': 0.46218487394957986, 'f1': 0.5339805825242718, 'number': 119} | {'precision': 0.8827708703374778, 'recall': 0.9229340761374187, 'f1': 0.902405810258738, 'number': 1077} | 0.8683 | 0.8907 | 0.8794 | 0.8175 | |
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| 0.002 | 78.95 | 1500 | 1.6624 | {'precision': 0.861904761904762, 'recall': 0.8861689106487148, 'f1': 0.8738684369342186, 'number': 817} | {'precision': 0.6363636363636364, 'recall': 0.5294117647058824, 'f1': 0.5779816513761468, 'number': 119} | {'precision': 0.8920863309352518, 'recall': 0.9210770659238626, 'f1': 0.9063499314755596, 'number': 1077} | 0.8674 | 0.8838 | 0.8755 | 0.7998 | |
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| 0.0006 | 105.26 | 2000 | 1.7942 | {'precision': 0.8597914252607184, 'recall': 0.9082007343941249, 'f1': 0.8833333333333333, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119} | {'precision': 0.9046746104491292, 'recall': 0.9164345403899722, 'f1': 0.9105166051660516, 'number': 1077} | 0.8740 | 0.8927 | 0.8833 | 0.8042 | |
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| 0.0002 | 131.58 | 2500 | 1.8161 | {'precision': 0.8591385331781141, 'recall': 0.9033047735618115, 'f1': 0.8806682577565632, 'number': 817} | {'precision': 0.6346153846153846, 'recall': 0.5546218487394958, 'f1': 0.5919282511210763, 'number': 119} | {'precision': 0.9047619047619048, 'recall': 0.9173630454967502, 'f1': 0.9110189027201475, 'number': 1077} | 0.8720 | 0.8902 | 0.8810 | 0.8021 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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