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---
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library_name: transformers
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license: mit
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base_model: SCUT-DLVCLab/lilt-roberta-en-base
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tags:
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- generated_from_trainer
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.5924
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- Answer: {'precision': 0.8748538011695907, 'recall': 0.9155446756425949, 'f1': 0.8947368421052633, 'number': 817}
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- Header: {'precision': 0.64, 'recall': 0.5378151260504201, 'f1': 0.5844748858447488, 'number': 119}
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- Question: {'precision': 0.8945487042001787, 'recall': 0.9294336118848654, 'f1': 0.9116575591985429, 'number': 1077}
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- Overall Precision: 0.8742
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- Overall Recall: 0.9006
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- Overall F1: 0.8872
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- Overall Accuracy: 0.8193
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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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| 0.3657 | 10.5263 | 200 | 0.9521 | {'precision': 0.8126361655773421, 'recall': 0.9130966952264382, 'f1': 0.859942363112392, 'number': 817} | {'precision': 0.5252525252525253, 'recall': 0.4369747899159664, 'f1': 0.47706422018348627, 'number': 119} | {'precision': 0.8796046720575023, 'recall': 0.9090064995357474, 'f1': 0.8940639269406392, 'number': 1077} | 0.8343 | 0.8828 | 0.8578 | 0.8059 |
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| 0.0448 | 21.0526 | 400 | 1.2063 | {'precision': 0.8845686512758202, 'recall': 0.8910648714810282, 'f1': 0.8878048780487805, 'number': 817} | {'precision': 0.5034965034965035, 'recall': 0.6050420168067226, 'f1': 0.549618320610687, 'number': 119} | {'precision': 0.8940092165898618, 'recall': 0.9006499535747446, 'f1': 0.8973172987974098, 'number': 1077} | 0.8630 | 0.8793 | 0.8711 | 0.8133 |
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| 0.0135 | 31.5789 | 600 | 1.3466 | {'precision': 0.8726190476190476, 'recall': 0.8971848225214198, 'f1': 0.8847314423657212, 'number': 817} | {'precision': 0.4900662251655629, 'recall': 0.6218487394957983, 'f1': 0.5481481481481482, 'number': 119} | {'precision': 0.8789808917197452, 'recall': 0.8969359331476323, 'f1': 0.8878676470588236, 'number': 1077} | 0.8483 | 0.8808 | 0.8642 | 0.8083 |
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| 0.0069 | 42.1053 | 800 | 1.3562 | {'precision': 0.8235294117647058, 'recall': 0.9082007343941249, 'f1': 0.8637951105937136, 'number': 817} | {'precision': 0.6413043478260869, 'recall': 0.4957983193277311, 'f1': 0.5592417061611374, 'number': 119} | {'precision': 0.8723981900452489, 'recall': 0.8950789229340761, 'f1': 0.8835930339138405, 'number': 1077} | 0.8413 | 0.8768 | 0.8587 | 0.8063 |
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| 0.0058 | 52.6316 | 1000 | 1.4131 | {'precision': 0.8688524590163934, 'recall': 0.9082007343941249, 'f1': 0.8880909634949131, 'number': 817} | {'precision': 0.6310679611650486, 'recall': 0.5462184873949579, 'f1': 0.5855855855855856, 'number': 119} | {'precision': 0.8767605633802817, 'recall': 0.924791086350975, 'f1': 0.9001355625847266, 'number': 1077} | 0.8614 | 0.8957 | 0.8782 | 0.8110 |
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| 0.0034 | 63.1579 | 1200 | 1.4398 | {'precision': 0.867699642431466, 'recall': 0.8910648714810282, 'f1': 0.8792270531400967, 'number': 817} | {'precision': 0.6288659793814433, 'recall': 0.5126050420168067, 'f1': 0.5648148148148148, 'number': 119} | {'precision': 0.8971533516988063, 'recall': 0.9071494893221913, 'f1': 0.9021237303785781, 'number': 1077} | 0.8721 | 0.8773 | 0.8747 | 0.8054 |
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| 0.0016 | 73.6842 | 1400 | 1.6692 | {'precision': 0.8520231213872832, 'recall': 0.9020807833537332, 'f1': 0.8763376932223542, 'number': 817} | {'precision': 0.6039603960396039, 'recall': 0.5126050420168067, 'f1': 0.5545454545454545, 'number': 119} | {'precision': 0.9039923954372624, 'recall': 0.883008356545961, 'f1': 0.8933771723813998, 'number': 1077} | 0.8667 | 0.8689 | 0.8678 | 0.7919 |
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| 0.001 | 84.2105 | 1600 | 1.6412 | {'precision': 0.846927374301676, 'recall': 0.9277845777233782, 'f1': 0.8855140186915887, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.8877828054298642, 'recall': 0.9108635097493036, 'f1': 0.8991750687442712, 'number': 1077} | 0.8565 | 0.8957 | 0.8757 | 0.7982 |
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| 0.0006 | 94.7368 | 1800 | 1.5924 | {'precision': 0.8748538011695907, 'recall': 0.9155446756425949, 'f1': 0.8947368421052633, 'number': 817} | {'precision': 0.64, 'recall': 0.5378151260504201, 'f1': 0.5844748858447488, 'number': 119} | {'precision': 0.8945487042001787, 'recall': 0.9294336118848654, 'f1': 0.9116575591985429, 'number': 1077} | 0.8742 | 0.9006 | 0.8872 | 0.8193 |
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| 0.0004 | 105.2632 | 2000 | 1.5639 | {'precision': 0.8710433763188745, 'recall': 0.9094247246022031, 'f1': 0.8898203592814371, 'number': 817} | {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119} | {'precision': 0.8928892889288929, 'recall': 0.9210770659238626, 'f1': 0.9067641681901281, 'number': 1077} | 0.8708 | 0.8937 | 0.8821 | 0.8218 |
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| 0.0002 | 115.7895 | 2200 | 1.5740 | {'precision': 0.8684516880093132, 'recall': 0.9130966952264382, 'f1': 0.8902147971360381, 'number': 817} | {'precision': 0.65, 'recall': 0.5462184873949579, 'f1': 0.593607305936073, 'number': 119} | {'precision': 0.8928247048138056, 'recall': 0.9127205199628597, 'f1': 0.9026629935720845, 'number': 1077} | 0.8709 | 0.8912 | 0.8809 | 0.8162 |
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| 0.0002 | 126.3158 | 2400 | 1.5739 | {'precision': 0.8710433763188745, 'recall': 0.9094247246022031, 'f1': 0.8898203592814371, 'number': 817} | {'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119} | {'precision': 0.8975521305530372, 'recall': 0.9192200557103064, 'f1': 0.9082568807339448, 'number': 1077} | 0.8736 | 0.8927 | 0.8830 | 0.8177 |
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### Framework versions
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- Transformers 4.48.0
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- Pytorch 2.5.1+cpu
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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