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---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6046
- Answer: {'precision': 0.8879518072289156, 'recall': 0.9020807833537332, 'f1': 0.8949605343047966, 'number': 817}
- Header: {'precision': 0.6761904761904762, 'recall': 0.5966386554621849, 'f1': 0.6339285714285715, 'number': 119}
- Question: {'precision': 0.8968609865470852, 'recall': 0.9285051067780873, 'f1': 0.9124087591240877, 'number': 1077}
- Overall Precision: 0.8820
- Overall Recall: 0.8982
- Overall F1: 0.8900
- Overall Accuracy: 0.8187
## 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 |
|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3984 | 10.5263 | 200 | 0.9319 | {'precision': 0.837129840546697, 'recall': 0.8996328029375765, 'f1': 0.8672566371681416, 'number': 817} | {'precision': 0.5238095238095238, 'recall': 0.46218487394957986, 'f1': 0.4910714285714286, 'number': 119} | {'precision': 0.8606060606060606, 'recall': 0.9229340761374187, 'f1': 0.8906810035842293, 'number': 1077} | 0.8344 | 0.8862 | 0.8596 | 0.7926 |
| 0.0543 | 21.0526 | 400 | 1.1018 | {'precision': 0.8492520138089759, 'recall': 0.9033047735618115, 'f1': 0.8754448398576511, 'number': 817} | {'precision': 0.5688073394495413, 'recall': 0.5210084033613446, 'f1': 0.543859649122807, 'number': 119} | {'precision': 0.8731277533039647, 'recall': 0.9201485608170845, 'f1': 0.8960216998191681, 'number': 1077} | 0.8476 | 0.8897 | 0.8682 | 0.8133 |
| 0.0134 | 31.5789 | 600 | 1.5231 | {'precision': 0.838963963963964, 'recall': 0.9118727050183598, 'f1': 0.873900293255132, 'number': 817} | {'precision': 0.5727272727272728, 'recall': 0.5294117647058824, 'f1': 0.5502183406113538, 'number': 119} | {'precision': 0.9149338374291115, 'recall': 0.8987929433611885, 'f1': 0.9067915690866512, 'number': 1077} | 0.8638 | 0.8823 | 0.8729 | 0.8046 |
| 0.0101 | 42.1053 | 800 | 1.5678 | {'precision': 0.8509895227008148, 'recall': 0.8947368421052632, 'f1': 0.8723150357995225, 'number': 817} | {'precision': 0.5700934579439252, 'recall': 0.5126050420168067, 'f1': 0.5398230088495575, 'number': 119} | {'precision': 0.8781770376862401, 'recall': 0.9303621169916435, 'f1': 0.9035166816952209, 'number': 1077} | 0.8514 | 0.8912 | 0.8709 | 0.8052 |
| 0.0041 | 52.6316 | 1000 | 1.6538 | {'precision': 0.8399558498896247, 'recall': 0.9314565483476133, 'f1': 0.8833430063842136, 'number': 817} | {'precision': 0.6705882352941176, 'recall': 0.4789915966386555, 'f1': 0.5588235294117647, 'number': 119} | {'precision': 0.9063386944181646, 'recall': 0.8895078922934077, 'f1': 0.8978444236176195, 'number': 1077} | 0.8672 | 0.8823 | 0.8747 | 0.7979 |
| 0.0033 | 63.1579 | 1200 | 1.4464 | {'precision': 0.875, 'recall': 0.9167686658506732, 'f1': 0.895397489539749, 'number': 817} | {'precision': 0.6049382716049383, 'recall': 0.4117647058823529, 'f1': 0.49000000000000005, 'number': 119} | {'precision': 0.8777292576419214, 'recall': 0.9331476323119777, 'f1': 0.9045904590459046, 'number': 1077} | 0.8660 | 0.8957 | 0.8806 | 0.8152 |
| 0.0015 | 73.6842 | 1400 | 1.5128 | {'precision': 0.8679906542056075, 'recall': 0.9094247246022031, 'f1': 0.8882247459653317, 'number': 817} | {'precision': 0.6511627906976745, 'recall': 0.47058823529411764, 'f1': 0.5463414634146342, 'number': 119} | {'precision': 0.8906810035842294, 'recall': 0.9229340761374187, 'f1': 0.9065207478340173, 'number': 1077} | 0.8712 | 0.8907 | 0.8809 | 0.8213 |
| 0.0014 | 84.2105 | 1600 | 1.6089 | {'precision': 0.8555176336746303, 'recall': 0.9204406364749081, 'f1': 0.8867924528301887, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119} | {'precision': 0.8891820580474934, 'recall': 0.9387186629526463, 'f1': 0.913279132791328, 'number': 1077} | 0.8610 | 0.9081 | 0.8839 | 0.8172 |
| 0.0005 | 94.7368 | 1800 | 1.6500 | {'precision': 0.865967365967366, 'recall': 0.9094247246022031, 'f1': 0.8871641791044775, 'number': 817} | {'precision': 0.6513761467889908, 'recall': 0.5966386554621849, 'f1': 0.6228070175438596, 'number': 119} | {'precision': 0.9027522935779817, 'recall': 0.9136490250696379, 'f1': 0.9081679741578219, 'number': 1077} | 0.8741 | 0.8932 | 0.8835 | 0.8135 |
| 0.0005 | 105.2632 | 2000 | 1.6204 | {'precision': 0.8909090909090909, 'recall': 0.8996328029375765, 'f1': 0.8952496954933008, 'number': 817} | {'precision': 0.6403508771929824, 'recall': 0.6134453781512605, 'f1': 0.6266094420600858, 'number': 119} | {'precision': 0.8925399644760214, 'recall': 0.9331476323119777, 'f1': 0.9123921924648207, 'number': 1077} | 0.8780 | 0.9006 | 0.8892 | 0.8177 |
| 0.0004 | 115.7895 | 2200 | 1.6046 | {'precision': 0.8879518072289156, 'recall': 0.9020807833537332, 'f1': 0.8949605343047966, 'number': 817} | {'precision': 0.6761904761904762, 'recall': 0.5966386554621849, 'f1': 0.6339285714285715, 'number': 119} | {'precision': 0.8968609865470852, 'recall': 0.9285051067780873, 'f1': 0.9124087591240877, 'number': 1077} | 0.8820 | 0.8982 | 0.8900 | 0.8187 |
| 0.0002 | 126.3158 | 2400 | 1.6270 | {'precision': 0.8790035587188612, 'recall': 0.9069767441860465, 'f1': 0.8927710843373493, 'number': 817} | {'precision': 0.6574074074074074, 'recall': 0.5966386554621849, 'f1': 0.6255506607929515, 'number': 119} | {'precision': 0.8972046889089269, 'recall': 0.9238625812441968, 'f1': 0.9103385178408051, 'number': 1077} | 0.8772 | 0.8977 | 0.8873 | 0.8193 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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