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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: 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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# 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: 1.4801 |
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- Answer: {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} |
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- Header: {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} |
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- Question: {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} |
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- Overall Precision: 0.8720 |
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- Overall Recall: 0.8932 |
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- Overall F1: 0.8825 |
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- Overall Accuracy: 0.8040 |
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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: 2 |
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- eval_batch_size: 2 |
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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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| 0.0015 | 2.67 | 200 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0011 | 5.33 | 400 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0011 | 8.0 | 600 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0008 | 10.67 | 800 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0011 | 13.33 | 1000 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0011 | 16.0 | 1200 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0017 | 18.67 | 1400 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0008 | 21.33 | 1600 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0008 | 24.0 | 1800 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0009 | 26.67 | 2000 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0012 | 29.33 | 2200 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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| 0.0009 | 32.0 | 2400 | 1.4801 | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720 | 0.8932 | 0.8825 | 0.8040 | |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 1.8.0+cu101 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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