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---
license: mit
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
base_model: SCUT-DLVCLab/lilt-roberta-en-base
model-index:
- name: lilt-en-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lilt-en-funsd
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.
It achieves the following results on the evaluation set:
- Loss: 1.7699
- Answer: {'precision': 0.8906439854191981, 'recall': 0.8971848225214198, 'f1': 0.8939024390243904, 'number': 817}
- Header: {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119}
- Question: {'precision': 0.8778359511343804, 'recall': 0.9340761374187558, 'f1': 0.9050832208726944, 'number': 1077}
- Overall Precision: 0.8706
- Overall Recall: 0.8957
- Overall F1: 0.8830
- Overall Accuracy: 0.7973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4312 | 10.53 | 200 | 0.9853 | {'precision': 0.8581818181818182, 'recall': 0.8665850673194615, 'f1': 0.8623629719853837, 'number': 817} | {'precision': 0.5625, 'recall': 0.5294117647058824, 'f1': 0.5454545454545455, 'number': 119} | {'precision': 0.8788706739526412, 'recall': 0.8960074280408542, 'f1': 0.8873563218390804, 'number': 1077} | 0.8531 | 0.8624 | 0.8577 | 0.8172 |
| 0.0478 | 21.05 | 400 | 1.2825 | {'precision': 0.8571428571428571, 'recall': 0.9033047735618115, 'f1': 0.8796185935637664, 'number': 817} | {'precision': 0.5136986301369864, 'recall': 0.6302521008403361, 'f1': 0.5660377358490567, 'number': 119} | {'precision': 0.8739650413983441, 'recall': 0.8820798514391829, 'f1': 0.878003696857671, 'number': 1077} | 0.8419 | 0.8758 | 0.8585 | 0.8026 |
| 0.0127 | 31.58 | 600 | 1.4791 | {'precision': 0.8568075117370892, 'recall': 0.8935128518971848, 'f1': 0.8747753145596165, 'number': 817} | {'precision': 0.5779816513761468, 'recall': 0.5294117647058824, 'f1': 0.5526315789473684, 'number': 119} | {'precision': 0.8909426987060998, 'recall': 0.8950789229340761, 'f1': 0.8930060213061601, 'number': 1077} | 0.8600 | 0.8728 | 0.8664 | 0.7957 |
| 0.0073 | 42.11 | 800 | 1.3846 | {'precision': 0.8853046594982079, 'recall': 0.9069767441860465, 'f1': 0.8960096735187424, 'number': 817} | {'precision': 0.5333333333333333, 'recall': 0.6050420168067226, 'f1': 0.5669291338582677, 'number': 119} | {'precision': 0.8932584269662921, 'recall': 0.8857938718662952, 'f1': 0.8895104895104896, 'number': 1077} | 0.8662 | 0.8778 | 0.8719 | 0.8142 |
| 0.0023 | 52.63 | 1000 | 1.5955 | {'precision': 0.8430034129692833, 'recall': 0.9069767441860465, 'f1': 0.8738207547169811, 'number': 817} | {'precision': 0.6190476190476191, 'recall': 0.5462184873949579, 'f1': 0.5803571428571429, 'number': 119} | {'precision': 0.8935574229691877, 'recall': 0.8885793871866295, 'f1': 0.8910614525139665, 'number': 1077} | 0.8579 | 0.8758 | 0.8668 | 0.7992 |
| 0.0023 | 63.16 | 1200 | 1.6214 | {'precision': 0.8955773955773956, 'recall': 0.8922888616891065, 'f1': 0.8939301042305334, 'number': 817} | {'precision': 0.5882352941176471, 'recall': 0.5882352941176471, 'f1': 0.5882352941176471, 'number': 119} | {'precision': 0.8841354723707665, 'recall': 0.9210770659238626, 'f1': 0.9022282855843565, 'number': 1077} | 0.8715 | 0.8897 | 0.8805 | 0.8057 |
| 0.0016 | 73.68 | 1400 | 1.8002 | {'precision': 0.8732394366197183, 'recall': 0.9106487148102815, 'f1': 0.8915518274415818, 'number': 817} | {'precision': 0.5765765765765766, 'recall': 0.5378151260504201, 'f1': 0.5565217391304348, 'number': 119} | {'precision': 0.8892921960072595, 'recall': 0.9099350046425255, 'f1': 0.8994951812758146, 'number': 1077} | 0.8659 | 0.8882 | 0.8769 | 0.7860 |
| 0.0013 | 84.21 | 1600 | 1.7699 | {'precision': 0.8906439854191981, 'recall': 0.8971848225214198, 'f1': 0.8939024390243904, 'number': 817} | {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119} | {'precision': 0.8778359511343804, 'recall': 0.9340761374187558, 'f1': 0.9050832208726944, 'number': 1077} | 0.8706 | 0.8957 | 0.8830 | 0.7973 |
| 0.0008 | 94.74 | 1800 | 1.7824 | {'precision': 0.8733572281959379, 'recall': 0.8947368421052632, 'f1': 0.8839177750906893, 'number': 817} | {'precision': 0.616822429906542, 'recall': 0.5546218487394958, 'f1': 0.5840707964601769, 'number': 119} | {'precision': 0.8901996370235935, 'recall': 0.9108635097493036, 'f1': 0.9004130335016063, 'number': 1077} | 0.8690 | 0.8833 | 0.8761 | 0.8019 |
| 0.0005 | 105.26 | 2000 | 1.7894 | {'precision': 0.872791519434629, 'recall': 0.9069767441860465, 'f1': 0.8895558223289316, 'number': 817} | {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119} | {'precision': 0.8931506849315068, 'recall': 0.9080779944289693, 'f1': 0.9005524861878452, 'number': 1077} | 0.8691 | 0.8872 | 0.8781 | 0.7940 |
| 0.0002 | 115.79 | 2200 | 1.8409 | {'precision': 0.8665893271461717, 'recall': 0.9143206854345165, 'f1': 0.8898153662894581, 'number': 817} | {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119} | {'precision': 0.8978644382544104, 'recall': 0.8978644382544104, 'f1': 0.8978644382544104, 'number': 1077} | 0.8705 | 0.8852 | 0.8778 | 0.7982 |
| 0.0002 | 126.32 | 2400 | 1.8311 | {'precision': 0.8709302325581395, 'recall': 0.9167686658506732, 'f1': 0.8932617769827073, 'number': 817} | {'precision': 0.6018518518518519, 'recall': 0.5462184873949579, 'f1': 0.5726872246696034, 'number': 119} | {'precision': 0.893953488372093, 'recall': 0.8922934076137419, 'f1': 0.8931226765799257, 'number': 1077} | 0.8688 | 0.8818 | 0.8752 | 0.7988 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2
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