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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: 1.5192
- Answer: {'precision': 0.8623024830699775, 'recall': 0.9351285189718482, 'f1': 0.897240164415737, 'number': 817}
- Header: {'precision': 0.5980392156862745, 'recall': 0.5126050420168067, 'f1': 0.5520361990950226, 'number': 119}
- Question: {'precision': 0.9070191431175935, 'recall': 0.9238625812441968, 'f1': 0.9153633854645814, 'number': 1077}
- Overall Precision: 0.8729
- Overall Recall: 0.9041
- Overall F1: 0.8882
- Overall Accuracy: 0.8317
## 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 |
|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.41 | 10.5263 | 200 | 1.0931 | {'precision': 0.8183856502242153, 'recall': 0.8935128518971848, 'f1': 0.8543007606787594, 'number': 817} | {'precision': 0.42138364779874216, 'recall': 0.5630252100840336, 'f1': 0.48201438848920863, 'number': 119} | {'precision': 0.8991185112634672, 'recall': 0.8523676880222841, 'f1': 0.8751191611058151, 'number': 1077} | 0.8277 | 0.8520 | 0.8397 | 0.7869 |
| 0.0535 | 21.0526 | 400 | 1.2583 | {'precision': 0.8495475113122172, 'recall': 0.9192166462668299, 'f1': 0.8830099941211051, 'number': 817} | {'precision': 0.5636363636363636, 'recall': 0.5210084033613446, 'f1': 0.5414847161572053, 'number': 119} | {'precision': 0.8898999090081893, 'recall': 0.9080779944289693, 'f1': 0.8988970588235294, 'number': 1077} | 0.8557 | 0.8897 | 0.8724 | 0.8223 |
| 0.0132 | 31.5789 | 600 | 1.3993 | {'precision': 0.8563348416289592, 'recall': 0.9265605875152999, 'f1': 0.8900646678424456, 'number': 817} | {'precision': 0.6116504854368932, 'recall': 0.5294117647058824, 'f1': 0.5675675675675675, 'number': 119} | {'precision': 0.9144486692015209, 'recall': 0.89322191272052, 'f1': 0.9037106622827619, 'number': 1077} | 0.8740 | 0.8852 | 0.8796 | 0.8171 |
| 0.0078 | 42.1053 | 800 | 1.4683 | {'precision': 0.8583042973286876, 'recall': 0.9045287637698899, 'f1': 0.8808104886769966, 'number': 817} | {'precision': 0.684931506849315, 'recall': 0.42016806722689076, 'f1': 0.5208333333333334, 'number': 119} | {'precision': 0.9023041474654377, 'recall': 0.9090064995357474, 'f1': 0.9056429232192413, 'number': 1077} | 0.8757 | 0.8783 | 0.8770 | 0.8070 |
| 0.0035 | 52.6316 | 1000 | 1.4809 | {'precision': 0.8633177570093458, 'recall': 0.9045287637698899, 'f1': 0.8834429169157203, 'number': 817} | {'precision': 0.6582278481012658, 'recall': 0.4369747899159664, 'f1': 0.5252525252525252, 'number': 119} | {'precision': 0.886443661971831, 'recall': 0.9350046425255338, 'f1': 0.9100768187980117, 'number': 1077} | 0.8682 | 0.8932 | 0.8805 | 0.8184 |
| 0.0032 | 63.1579 | 1200 | 1.4947 | {'precision': 0.8544018058690744, 'recall': 0.9265605875152999, 'f1': 0.889019377568996, 'number': 817} | {'precision': 0.5238095238095238, 'recall': 0.46218487394957986, 'f1': 0.4910714285714286, 'number': 119} | {'precision': 0.9100185528756958, 'recall': 0.9108635097493036, 'f1': 0.9104408352668213, 'number': 1077} | 0.8666 | 0.8907 | 0.8785 | 0.8247 |
| 0.0016 | 73.6842 | 1400 | 1.4909 | {'precision': 0.8579676674364896, 'recall': 0.9094247246022031, 'f1': 0.8829471182412357, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5378151260504201, 'f1': 0.5953488372093023, 'number': 119} | {'precision': 0.9136822773186409, 'recall': 0.9238625812441968, 'f1': 0.9187442289935365, 'number': 1077} | 0.8786 | 0.8952 | 0.8868 | 0.8234 |
| 0.0006 | 84.2105 | 1600 | 1.5053 | {'precision': 0.8689492325855962, 'recall': 0.9008567931456548, 'f1': 0.8846153846153847, 'number': 817} | {'precision': 0.5922330097087378, 'recall': 0.5126050420168067, 'f1': 0.5495495495495496, 'number': 119} | {'precision': 0.8995475113122172, 'recall': 0.9229340761374187, 'f1': 0.9110907424381303, 'number': 1077} | 0.8715 | 0.8897 | 0.8805 | 0.8269 |
| 0.0005 | 94.7368 | 1800 | 1.5094 | {'precision': 0.8648648648648649, 'recall': 0.9400244798041616, 'f1': 0.9008797653958945, 'number': 817} | {'precision': 0.6138613861386139, 'recall': 0.5210084033613446, 'f1': 0.5636363636363637, 'number': 119} | {'precision': 0.9150141643059491, 'recall': 0.8997214484679665, 'f1': 0.9073033707865169, 'number': 1077} | 0.8784 | 0.8937 | 0.8860 | 0.8309 |
| 0.0004 | 105.2632 | 2000 | 1.5111 | {'precision': 0.8807017543859649, 'recall': 0.9216646266829865, 'f1': 0.9007177033492823, 'number': 817} | {'precision': 0.61, 'recall': 0.5126050420168067, 'f1': 0.5570776255707762, 'number': 119} | {'precision': 0.8981064021641119, 'recall': 0.924791086350975, 'f1': 0.9112534309240622, 'number': 1077} | 0.8769 | 0.8992 | 0.8879 | 0.8322 |
| 0.0004 | 115.7895 | 2200 | 1.5100 | {'precision': 0.8672768878718535, 'recall': 0.9277845777233782, 'f1': 0.8965109402720284, 'number': 817} | {'precision': 0.6145833333333334, 'recall': 0.4957983193277311, 'f1': 0.5488372093023256, 'number': 119} | {'precision': 0.9016245487364621, 'recall': 0.9275766016713092, 'f1': 0.91441647597254, 'number': 1077} | 0.8739 | 0.9021 | 0.8878 | 0.8312 |
| 0.0002 | 126.3158 | 2400 | 1.5192 | {'precision': 0.8623024830699775, 'recall': 0.9351285189718482, 'f1': 0.897240164415737, 'number': 817} | {'precision': 0.5980392156862745, 'recall': 0.5126050420168067, 'f1': 0.5520361990950226, 'number': 119} | {'precision': 0.9070191431175935, 'recall': 0.9238625812441968, 'f1': 0.9153633854645814, 'number': 1077} | 0.8729 | 0.9041 | 0.8882 | 0.8317 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.0.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
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