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---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: lilT_fintuning
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_fintuning
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: 1.6381
- Answer: {'precision': 0.8744075829383886, 'recall': 0.9033047735618115, 'f1': 0.8886213124623721, 'number': 817}
- Header: {'precision': 0.6261682242990654, 'recall': 0.5630252100840336, 'f1': 0.5929203539823009, 'number': 119}
- Question: {'precision': 0.8998194945848376, 'recall': 0.9257195914577531, 'f1': 0.9125858123569794, 'number': 1077}
- Overall Precision: 0.8752
- Overall Recall: 0.8952
- Overall F1: 0.8851
- Overall Accuracy: 0.8174
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4236 | 10.53 | 200 | 0.9243 | {'precision': 0.8401360544217688, 'recall': 0.9069767441860465, 'f1': 0.872277810476751, 'number': 817} | {'precision': 0.5333333333333333, 'recall': 0.40336134453781514, 'f1': 0.45933014354066987, 'number': 119} | {'precision': 0.8789571694599627, 'recall': 0.8765088207985144, 'f1': 0.8777312877731288, 'number': 1077} | 0.8470 | 0.8609 | 0.8539 | 0.8079 |
| 0.0472 | 21.05 | 400 | 1.2753 | {'precision': 0.8249721293199554, 'recall': 0.9057527539779682, 'f1': 0.8634772462077013, 'number': 817} | {'precision': 0.5, 'recall': 0.5798319327731093, 'f1': 0.5369649805447471, 'number': 119} | {'precision': 0.8778195488721805, 'recall': 0.8672237697307336, 'f1': 0.8724894908921065, 'number': 1077} | 0.8304 | 0.8659 | 0.8478 | 0.7910 |
| 0.014 | 31.58 | 600 | 1.3381 | {'precision': 0.8335233751425314, 'recall': 0.8947368421052632, 'f1': 0.8630460448642266, 'number': 817} | {'precision': 0.6292134831460674, 'recall': 0.47058823529411764, 'f1': 0.5384615384615384, 'number': 119} | {'precision': 0.8754416961130742, 'recall': 0.9201485608170845, 'f1': 0.8972385694884564, 'number': 1077} | 0.8475 | 0.8833 | 0.8650 | 0.8046 |
| 0.0063 | 42.11 | 800 | 1.4519 | {'precision': 0.8738095238095238, 'recall': 0.8984088127294981, 'f1': 0.8859384429692213, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.6470588235294118, 'f1': 0.6135458167330677, 'number': 119} | {'precision': 0.9008341056533827, 'recall': 0.9025069637883009, 'f1': 0.901669758812616, 'number': 1077} | 0.8693 | 0.8857 | 0.8775 | 0.8092 |
| 0.0036 | 52.63 | 1000 | 1.6211 | {'precision': 0.8363228699551569, 'recall': 0.9130966952264382, 'f1': 0.8730251609128145, 'number': 817} | {'precision': 0.584070796460177, 'recall': 0.5546218487394958, 'f1': 0.5689655172413793, 'number': 119} | {'precision': 0.8984302862419206, 'recall': 0.903435468895079, 'f1': 0.900925925925926, 'number': 1077} | 0.8549 | 0.8867 | 0.8705 | 0.8039 |
| 0.0029 | 63.16 | 1200 | 1.6274 | {'precision': 0.871007371007371, 'recall': 0.8678090575275398, 'f1': 0.8694052728387494, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.5042016806722689, 'f1': 0.5357142857142857, 'number': 119} | {'precision': 0.8844404003639672, 'recall': 0.9025069637883009, 'f1': 0.8933823529411765, 'number': 1077} | 0.8627 | 0.8649 | 0.8638 | 0.8008 |
| 0.0018 | 73.68 | 1400 | 1.6562 | {'precision': 0.8401360544217688, 'recall': 0.9069767441860465, 'f1': 0.872277810476751, 'number': 817} | {'precision': 0.6132075471698113, 'recall': 0.5462184873949579, 'f1': 0.5777777777777778, 'number': 119} | {'precision': 0.8892921960072595, 'recall': 0.9099350046425255, 'f1': 0.8994951812758146, 'number': 1077} | 0.8545 | 0.8872 | 0.8706 | 0.8096 |
| 0.001 | 84.21 | 1600 | 1.6388 | {'precision': 0.8534090909090909, 'recall': 0.9192166462668299, 'f1': 0.8850913376546846, 'number': 817} | {'precision': 0.63, 'recall': 0.5294117647058824, 'f1': 0.5753424657534247, 'number': 119} | {'precision': 0.9009174311926605, 'recall': 0.9117920148560817, 'f1': 0.9063221042916475, 'number': 1077} | 0.8676 | 0.8922 | 0.8797 | 0.8103 |
| 0.0007 | 94.74 | 1800 | 1.6278 | {'precision': 0.8545454545454545, 'recall': 0.9204406364749081, 'f1': 0.8862698880377136, 'number': 817} | {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119} | {'precision': 0.8909740840035746, 'recall': 0.9257195914577531, 'f1': 0.9080145719489982, 'number': 1077} | 0.8620 | 0.8997 | 0.8804 | 0.8216 |
| 0.0002 | 105.26 | 2000 | 1.6381 | {'precision': 0.8744075829383886, 'recall': 0.9033047735618115, 'f1': 0.8886213124623721, 'number': 817} | {'precision': 0.6261682242990654, 'recall': 0.5630252100840336, 'f1': 0.5929203539823009, 'number': 119} | {'precision': 0.8998194945848376, 'recall': 0.9257195914577531, 'f1': 0.9125858123569794, 'number': 1077} | 0.8752 | 0.8952 | 0.8851 | 0.8174 |
| 0.0002 | 115.79 | 2200 | 1.6545 | {'precision': 0.8757467144563919, 'recall': 0.8971848225214198, 'f1': 0.8863361547762998, 'number': 817} | {'precision': 0.625, 'recall': 0.5462184873949579, 'f1': 0.5829596412556054, 'number': 119} | {'precision': 0.8902765388046388, 'recall': 0.9266480965645311, 'f1': 0.908098271155596, 'number': 1077} | 0.8710 | 0.8922 | 0.8815 | 0.8155 |
| 0.0002 | 126.32 | 2400 | 1.6477 | {'precision': 0.8658823529411764, 'recall': 0.9008567931456548, 'f1': 0.8830233953209357, 'number': 817} | {'precision': 0.6116504854368932, 'recall': 0.5294117647058824, 'f1': 0.5675675675675675, 'number': 119} | {'precision': 0.8930817610062893, 'recall': 0.9229340761374187, 'f1': 0.9077625570776255, 'number': 1077} | 0.8679 | 0.8907 | 0.8791 | 0.8167 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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